abstracts aewg-54 2012 may 21_22... · s6-4 introducing sifted b -value analysis and a new crack...
TRANSCRIPT
ABSTRACTS
AEWG-54
May 21 – 22, 2012
PRINCETON, NJ
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Table of Contents ABSTRACTS AEWG-54 2012
Session 1: New Systems and Industrial Applications ................................................................................ 4
S1-1 HPIS (High Pressure Institute of Japan Standard) recommended practice for acoustic emission evaluation of corrosion damages in underground tanks. Hashimoto et al. ........................................... 5
S1-2 Intrinsically safe acoustic emission equipment opens the door to permanent monitoring applications in the oil and gas industry. Vallen and Thenikl. ................................................................. 7
S1-3 AE Monitoring on Surface Transport Products. Tscheliesnig. .................................................... 8
S1-4 Corrosion monitoring in plate like structures using AE tomography. Koduru and Gonzalez. ...... 9
S1-5 Gas linepipe full scale burst test monitoring by AE technique. Budano et al. ........................... 10
S1-6 A Novel Portable Fiber-Optic based Acoustic Emission System. Momeni et al. ........................ 11
Session 2: Crack Growth Monitoring in Steel Structures ......................................................................... 12
S2-1 Fatigue Crack Growth Analysis From Acoustic Emission Data On The Navy H-60 Seahawk Helicopter Tail Gearbox. Barsoum el al. ............................................................................................. 13
S2-2 Detecting and Monitoring Thermal Shock/Thermal Fatigue Damage in High Energy Piping (HEP) by QAE NDI. Mizrahi and Rosenberg. ................................................................................................. 14
S2-3 Prognostics of Fatigue Crack Behavior with Acoustic Emission An Overview of NIST TIP at the University of South Carolina. Ziehl et al.............................................................................................. 15
S2-4 Fatigue Life Prediction In Steel I-Beams Using Neural Networks And Mathematically Modeled Acoustic Emission Data. Barsoum et al. .............................................................................................. 16
S2-5 Novel Optico-Acoustic Sensing System (NOAS) for Damage Identification. Vanniamparambil et al. (student paper). ............................................................................................................................ 17
S2-6 Structural Health Condition Monitoring of Rail Steel Using Acoustic Emission Techniques. Yilmazer and Papaelias. ..................................................................................................................... 18
Session 3: Signal processing and New Location Algorithms .................................................................... 19
S3-1 Numerical verification of AE tomography algorithm with new AE source location technique. Kobayashi and Shiotani. ..................................................................................................................... 20
S3-2 Development of a Probabilistic Acoustic Emission Source Location Algorithm. Schumacher et al. .............................................................................................................................................. 21
S3-3 Method to Evaluate the Incompleteness due to Lossy Acquisition of AE. Fan and Qi (student paper). .............................................................................................................................................. 22
S3-4 Time reverse modeling of acoustic emissions in reinforced concrete members. Kocur. .......... 23
S3-5 Data-driven Modeling of Damage Induced Acoustic Wave Propagation. Kontsos et al. ........... 24
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S3.6 Using AE to address the ensemble interactions in hierarchical microstructures where damage events present, while largely ignored. Qi et al.................................................................................... 25
Session 4: Material Characterization ...................................................................................................... 26
S4-1 A Novel Framework for Scale-bridging Characterization of Materials Mechanical Behavior Using Acoustic Emission..Kontsos et al. .............................................................................................. 27
S4-2 Using Acoustic Emission to Monitor Microstructural Damage During Deformation of Multiphase Steels. Fekete. ................................................................................................................. 28
S4-3 Microfracture Process in Bone Characterized by Acoustic Emission. Wakayama. .................... 29
S4-4 Characterization of Lüders Deformation in Low-Carbon Steel by Acoustic Emission and IR thermograph. Shiraiwa et al............................................................................................................... 30
S4.5 Acoustic Emission analysis in media with rapidly changing Green’s functions-experiments considering thermo-hydro-mechanical changes. Grosse et al. ........................................................... 31
S4.6 Investigation of AE from freezing/melting transitions. Azimov et al. ....................................... 32
Session 5: AE in Composite Materials and Structures ............................................................................. 33
S5-1 Extending Composite Overwrapped Pressure Vessels (COPV) Service Life using Acoustic Emission Testing and Stress Rupture Lifetime Prediction Models. Hay. .............................................. 34
S5-2 Distinguishability of failure mechanisms in carbon fiber reinforced plastics: Influence of acoustic emission sensor type. Sause and Horn. ................................................................................ 35
S5-3 Acoustic Emission Failure Mechanism Classification In Repaired Fiberglass/Epoxy Compression Specimens Using A Self-Organizing Map Neural Network. Barsoum et al............................................ 36
S5-4 Wave Propagation Study of Lamb Wave Modes in CFRP Plates. Ono and Gallego. ................ 37
S5.5 Acoustic Emission Testing of a Curved Pultruded Rod Stitched Efficient Unitized Structure (PRSEUS) Under Combined Loading. Khanolkar et al. (student paper). ............................................... 38
Session 6: AE in Civil and Geophysical Engineering Applications I ........................................................... 39
S6-1 AE in Saturated Rock under Plane Strain Compression. Makhnenko (student paper). ............ 40
S6-2 Damage assessment of civil engineering materials by means of transfer function on AE waveforms. Shiotani and Takada. ...................................................................................................... 41
S6-3 Evaluation of Prestressed Concrete Structures with Acoustic Emission. An Overview of NIST TIP at the University of South Carolina. Larosche et al. ............................................................................ 42
S6-4 Introducing Sifted b-Value Analysis and a New Crack Classification for Monitoring Reinforced Concrete Shear Walls by Acoustic Emission. Farhidzadeh and Salamone (STUDENT AWARD paper). .. 43
S6-5 Acoustic Emission Studies on a Highway Bridge Crossing Over Freight Rail Tracks. Parmar et al. . .............................................................................................................................................. 44
S6-6 Quantitative Acoustic Emission Monitoring of Reinforced Concrete Structures Using High-Fidelity Point Contact Sensors. Mhamdi and Schumacher. ................................................................. 45
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Session 7: AE in Civil and Geophysical Engineering Applications II .......................................................... 46
S7-1 Hydraulic Fracture Characterization and Location in Scale Model Tests Using Acoustic Emission. Hampton et al. (student paper).......................................................................................................... 47
S7-2 Using Acoustic Emission to Detect and Quantify Alkali Silica Reaction in Concrete. Pour-Ghaz et al. .............................................................................................................................................. 50
S7.3 Investigation of Fracture Energy and Fracture Process Zone in Concrete under Three-point Bending by Acoustic Emission. Ohno at al. ......................................................................................... 51
S7-4 Fracture Mechanics of Concrete by AE-SiGMA Analysis. Ohtsu and Mondoringin. .................. 52
S7-5 Fracture Mechanisms of Corrosion-Induced Cracks in Reinforced Concrete by SiGMA and BEM. Kawasaki et al. (STUDENT AWARD paper). ......................................................................................... 53
S7-6 AE Signal Detected during Leak from Gas Pipe in Sand. Yoshida et al. ..................................... 54
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ABSTRACTS
AEWG-54
May 21 – 22, 2012
PRINCETON, NJ
Session 1: New Systems and Industrial Applications
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S1-1 HPIS (High Pressure Institute of Japan Standard) recommended practice for acoustic emission evaluation of corrosion damages in underground tanks. Hashimoto et al.
HPIS (HIGH PRESSURE INSTITUTE OF JAPAN STANDARD)
RECOMMENDED PRACTICE FOR ACOUSTIC EMISSION EVALUATION
OF CORROSION DAMAGES IN UNDERGROUND TANKS
HASHIMOTO Yakobu1), UESAWA Michihiko1), MAEDA Minoru2), YUYAMA Shigenori3), YAMADA Minoru4), and SEKINE Kazuyoshi5)
NTT FACILITIES, INC.
G. H. Y. Bldg., 2-13-1, Kitaotsuka, Toshima-ku, Tokyo 170-0004, Japan
NTT GP-ECOcommunication, Inc
Granpark Tower 22F, 3-4-1, Shibaura, Minato-ku, Tokyo 108-0023, Japan
Nippon Physical Acoustics, Ltd.
8F Okamoto LK Bldg., 2-17-10, Higashi, Shibuya-ku, Tokyo 150-0011, Japan
National Research Institute of Fire and Disaster
4-35-3, Jindaiji-higashi-machi, Choufu, Tokyo 182-8508, Japan
Yokohama National University
79-5, Tokiwadai, Hodogaya-ku, Yokohama, Kanagawa 240-8501, Japan
Corresponding address: YUYAMA Shigenori, Nippon Physical Acoustics Ltd., 8F Okamoto LK Bldg., 2-17-10, Higashi, Shibuya-ku, Tokyo 150-0011, Japan (E-mail: [email protected])
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Abstract
A recommended practice for acoustic emission (AE) evaluation of corrosion damages in underground tanks will be issued by the High Pressure Institute of Japan (HPIJ), financially supported by the NTT Group. The practice describes test procedure, database, and evaluation criteria. More than 110 tanks were tested on the basis of the developed test procedure. After the AE tests, about ten tanks were opened and visual inspection and thickness measurements were performed. No corrosion damage was found in the tanks with very low AE activities (less than 100 hits per 10 m2 ), while very high AE activities (nearly 1,000 hits per 10 m2) were detected in the tank where significant corrosion damages (thickness losses) were observed. The detected hit distribution with regard to number of corresponding tanks also suggested a correlation between the AE activities and corrosion damages in underground tanks. The practice will be issued sometime in May 2012. This paper summarizes the test procedure, database and evaluation criteria described in the practice.
Keywords: Acoustic emission, Corrosion damage, Database, Recommended practice, Thickness measurement, Underground tank, Visual inspection
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S1-2 Intrinsically safe acoustic emission equipment opens the door to permanent monitoring applications in the oil and gas industry. Vallen and Thenikl.
INTRINSICALLY SAFE ACOUSTIC EMISSION EQUIPMENT
OPENS THE DOOR TO PERMANENT MONITORING APPLICATIONS
IN THE OIL AND GAS INDUSTRY
Hartmut VALLEN 1, Thomas THENIKL 1
1 Vallen Systeme GmbH, Icking, Germany, Phone +49 8178 9674400, [email protected]
Abstract The oil and gas industry is a very prospective target group for the permanent installation of acoustic emission (AE) instrumentation for monitoring various test objects for crack growth or active corrosion. According to directives and standards, electrical instrumentation installed in a potentially explosive atmosphere must be certified intrinsically safe. The development and certification process of intrinsically safe instrumentation needs great effort, which is not economical for the relatively small market for intrinsically safe AE instrumentation, nowadays. On the other hand, the non-availability of suitable intrinsically safe AE instrumentation could be a hindrance for the potential market growth. European Commission helps to overcome the problem: In the European research project titled "Cost effective corrosion and fatigue monitoring for transport products (CORFAT)" (www.corfat.eu), the German company Vallen Systeme GmbH is responsible for developing an intrinsically safe AE sensor system (ISAFE3). The ISAFE3 system is employed for detection of corrosion and fatigue crack of storage tanks used in surface transportation means like ships, mainly oil tankers, trucks and rail wagons. ISAFE3 is intended to be ATEX certified for use in most dangerous explosion zone 0 (where an explosive atmosphere can permanently be present) and for gas group IIC (where lowest spark energy can ignite an explosion). The sensor system must be sealed for immersion into oil at peak pressure up to 12 bar (caused by sloshing forces in an oil tanker).
This paper presents basics about the requirements of the applicable standards and the applications and an overview of the realization of ISAFE3.
Keywords: Acoustic emission; intrinsic safety; corrosion monitoring; fatigue crack detection
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S1-3 AE Monitoring on Surface Transport Products. Tscheliesnig.
AE MONITORING ON SURFACE TRANSPORT PRODUCTS
Peter Tscheliesnig
TÜV AUSTRIA SERVICES GMBH,
Deutschstrasse 10, 1230 Vienna/Austria
Phone : +43/1/61091-6630
e-mail : [email protected]
Abstract
Beside human errors the different technical degradation processes like corrosion and fatigue cracks are the most reasons for structural failures of all surface transport products like ships, road tankers and railway tank cars. To avoid the failure of those structural, maintenance and inspection have to be carried out on a time span basis. These activities are time consuming and consequently expensive, and a lot of examples exist, where weakened structures have failed during operation.
It is necessary to detect and identify evolving defects on time, which is only possible by monitoring of the structure and not periodic inspections carried out on time driven basis. A real time monitoring to detect cracks as well as corrosion would only be possible with Acoustic Emission (AE). Within an EC-funded project (SCP7-GA-2008-218637 “Cost effective fatigue and corrosion monitoring by means of Acoustic Emission on transport products”) an overall, innovative strategy for the maintenance and inspection will be developed.
The application of the key technology (AT) was adapted for the specific needs and was embedded in clear application rules for monitoring, follow-up inspection and preventive kind of inspection and maintenance for the ships, trucks and railway cars.
The results of the pre-tests in different laboratories of the partners and during shipping operations on-board of ships, trucks on road and railway cars during operation will be shown. Finally the specific and according their intrinsically safety certified equipment including sensors will be presented.
Keywords: Acoustic Emission Testing (AT), Monitoring vs. Inspection, Surface transport products (ship, trucks and railway cars), Intrinsically safe equipment
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S1-4 Corrosion monitoring in plate like structures using AE tomography. Koduru and Gonzalez.
CORROSION MONITORING IN PLATE LIKE STRUCTURES
USING AE TOMOGRAPHY
Jaya Koduru and Miguel Gonzalez
MISTRAS Group Inc.
Princeton Junction, NJ 08550
Abstract
Structural health monitoring (SHM) is fast becoming a reliable and economical solution for condition based maintenance of large structures like bridges, ships, aircrafts, pipelines, storage tanks etc. Fatigue cracks and Corrosion are the primary causes of damage in these structures. The applicability of acoustic emission (AE) technique for the location and occurrence of corrosion damages in metallic structures is well known, systematic tests have been conducted in the laboratory and in the field. The AE method can be used to detect corrosion growth on-line for periodically active corrosion or off-line for active corrosion during measurement. AE signals from a corrosion process are low amplitude signals compared to other damage emission mechanisms such as crack growth. AE corrosion signals can be of similar amplitudes as background noise. The advantage of the AE method is that it can locate the damages without taking the structure out of service. Utilizing tomographic approaches it is possible to obtain a damage map of the structure. A combination of source localization and tomographic algorithms is used to obtain the images. In this work, an aluminum plate is monitored for corrosion with a network of sensors. The AE tomogram of the plate is obtained at different stages of corrosion.
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S1-5 Gas linepipe full scale burst test monitoring by AE technique. Budano et al.
GAS LINEPIPE FULL SCALE BURST TEST MONITORING BY AE TECHNIQUE
Sergio Budano, Centro Sviluppo Materiali, Rome, Italy, [email protected] -
Phone +39 06 5055 298, Fax +39 06 5055 452
Roberto Piancaldini, Centro Sviluppo Materiali, Rome, Italy, [email protected]
Phone +39 06 5055 288, Fax +39 5055 801
Antonio Lucci, Centro Sviluppo Materiali, Rome, Italy, [email protected]
Phone +39 06 5055 760, Fax +39 5055 452
Giuseppe Giunta, eni spa, gas&power division, San Donato Milanese, Italy [email protected] Phone + 39 02 52031209 Fax: + 39 02 52051885
Abstract
In the framework of the eni gas&power research project oriented to the development of a reliable Acoustic Emission (AE) system for monitoring critical sections of transmission pipeline, a specific study has been carried out jointly with Centro Sviluppo Materiali (CSM) aimed at investigating AE features and applicability on steel pipelines widely used in the Oil&Gas industryFinal aim is to develop a reliable structural integrity assessment procedure based on the information gained from on-line pipeline AE monitoring.
Project results on laboratory scale elsewhere reported highlighted AE Energy and Energy rate are relevant parameters to discriminate proper fracture mechanism activated during brittle and ductile fracture in pipeline steels. These results were shown in previous AEWG-53.
The present study was planned to confirm these results moving from laboratory to full pipe scale: growth of Surface Flaws (S-Flaws) machined on single pipes wall was monitored by using AE technique while pipe internal pressure was increased until failure. In order to promote the activation of different fracture mechanisms, three burst tests were carried out at different conditions, in term of test temperature and internal pressurizing medium (water and air).
The results confirm that the selected parameters, AE Energy and AE Energy rate, give useful information that can be used to carefully predict both pipeline failure and damage evolution.
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S1-6 A Novel Portable Fiber-Optic based Acoustic Emission System. Momeni et al.
A NOVEL PORTABLE FIBER-OPTIC BASED ACOUSTIC EMISSION SYSTEM
Sepand MOMENI1, Dien NGUYEN2, Jaya KODURU1, Valery F., GODINEZ-AZCUAGA1
1 Mistras Group Inc., Princeton Junction, NJ, USA
[email protected], [email protected], [email protected]
2 Los Gatos Research Inc., Mountain view, CA, USA [email protected],
Abstract Monitoring acoustic emission (AE) in large structures at several locations over a long period of time is challenging as current technology is susceptible to EMI, shock, vibration and temperature, and signal attenuation over a long distances. Also, the current state of the art utilizes narrowband piezoelectric sensors to capture AE information; however, these sensors have large size and limited bandwidth which makes them difficult to use in applications like monitoring aircraft wings or turbine blades. Over the past few years, Fiber Bragg Grating (FBG) sensors have matured enough to be an excellent candidate for substituting conventional AE sensors where they cannot operate properly or there is a limitation for installation for them. For such reasons, Los Gatos Research Inc. and Mistras Group have been collaborating to develop a portable and field deployable fiber optic based AE system that operates with FBG sensors. A prototype 4-channel system has been developed and is being tested on a few applications such as fracture of aluminum and composite samples. Also, the response and sensitivity of the FBG sensors have been compared against that of conventional AE sensors and the results has been discussed.
Keywords: Optical Fiber, Acoustic Emission, Fiber Bragg Grating, Structural Health Monitoring.
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ABSTRACTS
AEWG-54
May 21 – 22, 2012
PRINCETON, NJ
Session 2: Crack Growth Monitoring in Steel
Structures
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S2-1 Fatigue Crack Growth Analysis From Acoustic Emission Data On The Navy H-60 Seahawk Helicopter Tail Gearbox. Barsoum el al.
FATIGUE CRACK GROWTH ANALYSIS FROM ACOUSTIC EMISSION DATA ON THE NAVY H-60 SEAHAWK HELICOPTER TAIL GEARBOX
Fady F. Barsoum, Jun Shishino, Ning Y. Leung, and Prathikshen N. Selvadorai
Embry Riddle Aeronautical University, 600 S. Clyde Morris Boulevard, Daytona Beach, FL 32114-3900
(386) 226-6618; [email protected]
Alan B. Timmons and William J. Hardman
Naval Air Systems Command, Research and Engineering,
Propulsion & Power, 48246 Shaw Road, Patuxent River, MD 20670
(301) 757-0306; [email protected]
(301) 757-0508; [email protected]
Eric v. K. Hill
Aura Vector Consulting, 3041 Turnbull Bay Road, New Smyrna Beach, FL 32168-5437
(386) 341-6382; [email protected]
Abstract
There have been multiple incidents of H-60 and H-60 variant helicopters that have experienced fatigue cracks in the tail rotor gearbox assembly, specifically in the bevel gear splines. NAVAIR built a fixture to conduct tail gearbox ground testing at the Propulsion and Power Drive Test Stand Facility at Patuxent River, building 1461. This paper discusses how acoustic emission (AE) data gathered from the first crack growth test on a Sikorsky H-60 helicopter tail rotor gear box were processed and classified using a Kohonen self-organizing map (SOM) neural network. The tail rotor gearbox bevel gear had a seeded fault in one spline that grew to a crack 110 degrees around the circumference of the shaft before the test was ended. The SOM was able to sort the AE data into five failure mechanisms including two types of fatigue crack growth -- plane strain and plane stress. Further AE testing is recommended in order to determine the fatigue life and the critical crack length for ultimate structural failure.
Keywords: Fatigue crack growth, acoustic emission (AE), neural network, Kohonen self-organizing map (SOM), rotating machinery
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S2-2 Detecting and Monitoring Thermal Shock/Thermal Fatigue Damage in High Energy Piping (HEP) by QAE NDI. Mizrahi and Rosenberg.
DETECTING AND MONITORING THERMAL SHOCK/THERMAL FATIGUE
DAMAGE IN HIGH ENERGY PIPING (HEP) BY QAE NDI.
I. Mizrahi, S. Rosenberg
Margan Physical Diagnostics Ltd., P.O.B. 8155 Netanya 42504, Israel1
Tel: +972-9-8655510, E-mail: [email protected]
Abstract Cold reheat piping (CRH) in power plants has received little attention over the years, due its low operational temperature (500-700˚F) which is below the creep range. Recently, excessive attemperation in both coal-fired plants and combined cycle plants has become a routine practice. Consequently, the number of failures (catastrophic and non-catastrophic) in CRH piping has increased. Most of these recent failures can be traced back to a thermal fatigue and thermal shock damage due to changes in the operation of attemperator sprays. These types of cracks are almost invisible to local NDE methods and therefore a crack can develop to through wall thickness before detection. Quantitative Acoustic Emission Non-Destructive Inspection (QAE NDI) method was implemented in CRH piping to address this serious problem. QAE NDI:
1. Reveals thermal shock/fatigue damage in its early stages of development and monitors its development.
2. Identifies the zone/s subjected to thermal shock/fatigue conditions. 3. Synchronizes the damage and the zone affected by the thermal shock/fatigue with the
operational parameters of the attemperator spray. 4. Assesses the proper working parameters of the attemperator spray for minimizing the
damage to the piping. In this article we will demonstrate the capabilities of the QAE NDI in several CRH piping examples. Keywords: QAE NDI, Detection, Monitoring, Attemperator, Thermal Shock, Thermal Fatigue
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S2-3 Prognostics of Fatigue Crack Behavior with Acoustic Emission An Overview of NIST TIP at the University of South Carolina. Ziehl et al.
PROGNOSTICS OF FATIGUE CRACK BEHAVIOR WITH ACOUSTIC EMISSION AN OVERVIEW OF NIST TIP AT THE UNIVERSITY OF SOUTH CAROLINA
*Paul Ziehl‡, Juan Caicedo‡, Adrian Pollock‡‡, Jianguo Yu‡, Boris Zarate‡,
Mozahid Hossain‡, Gustavo Ospina‡, and Fabio Matta‡
‡University of South Carolina
‡‡Mistras Group, Inc.
University of South Carolina, 300 Main Street, Columbia, SC, USA, 29208;
*email: [email protected]
*phone: 803 467 4030
Abstract
The University of South Carolina has conducted a number of investigations involving Acoustic Emission for the detection and assessment of fatigue crack growth in steel materials and related structural systems. The primary focus of the investigations is on structural steel that is typical of that found in existing bridges.
Compact tension specimens have been loaded in fatigue at a rate of two Hertz. Variables investigated include the load ratio and the specimen thickness. Prognostics models that are both deterministically and probabilistically based have been developed. Welded cruciform specimens have also been loaded in fatigue while monitoring with AE to assess the effect of crack growth in weldments. The source mechanism of AE for fatigue crack growth in these ductile steel bridge materials has been investigated to aid in the development of the prognostics models. Appropriate data management and filtering strategies are integral part of the investigations. Links between the laboratory specimens and actual bridge structures will be discussed.
Keywords: Structural Steel, Fatigue Crack Growth, Health Monitoring, Prognosis, Acoustic Emission
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S2-4 Fatigue Life Prediction In Steel I-Beams Using Neural Networks And Mathematically Modeled Acoustic Emission Data. Barsoum et al.
FATIGUE LIFE PREDICTION IN STEEL I-BEAMS USING NEURAL NETWORKS AND MATHEMATICALLY MODELED ACOUSTIC EMISSION DATA
Fady F. Barsoum and Prathikshen N. Selvadorai
Embry-Riddle Aeronautical University, 600 S. Clyde Morris Boulevard, Daytona Beach, FL 32114-3900
(386) 226-6618; [email protected]
Eric v. K. Hill
Aura Vector Consulting, 3041 Turnbull Bay Road, New Smyrna Beach, FL 32168-5437
(386) 341-6382; [email protected]
Abstract
The purpose of this research was to predict fatigue cracking in metal beams using mathematically modeled acoustic emission (AE) data. The AE data were collected from the cyclic loading of ten steel I-beams that were subjected to three point bending. The data gathered during these tests were filtered in order to remove long duration hits, multiple hit data, and obvious outliers. Based on the duration, energy, and amplitude of the AE hits, the filtered data were classified into the various failure mechanisms of metals using the NeuralWorks Professional II/Plus software based Kohonen self-organizing map (SOM) neural network. Amplitude data from the classified plastic deformation data were mathematically modeled herein using bounded Johnson distributions. A backpropagation neural network (BPNN) was then generated using MATLAB. This BPNN predicted the fatigue life at which the steel I-beams would ultimately fail based on the four Johnson distribution parameters – ε, λ, η, and μ – that best fit the mathematically modeled plastic deformation data.
Keywords: Acoustic emission (AE), fatigue cracking, Kohonen self-organizing map (SOM), bounded Johnson distribution, backpropagation neural network (BPNN)
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S2-5 Novel Optico-Acoustic Sensing System (NOAS) for Damage Identification. Vanniamparambil et al. (student paper).
Novel Optico-Acoustic Sensing System (NOAS) for Damage Identification Student : Prashanth Abraham Vanniamparambil
Institution : Drexel University Advisors : Antonios Kontsos (Mech. Eng. & Mechanics. Dept.) and Ivan Bartoli (Civil, Arch. & Envir. Eng. Dept.)
Introduction No single Nondestructive Testing (NDT) method is capable of performing complete structural evaluation due to extrinsic and intrinsic factors including challenging environment, complex geometries/materials and variable operational conditions. Difficulties in reliably detecting damage using e.g. the AE method are typically amplified in noisy environments, where the monitoring system often flags false positive warnings. In addition and to address such needs, a large array of transducers is typically required to perform efficient damage identification, adding considerable costs and effort to the monitoring strategy. In the case, in fact, when detailed prior investigation of critical "hot spots" is not available or is neglected before monitoring, even the distributed sensing network fails to track reliably the damage process. To address such challenges, a novel integrated SHM (I-SHM) approach is being developed at Drexel University and is proposed herein; the I-SHM approach is based on the effective combination of optical and acoustic NDT with both numerical simulations and loading information. The novel approach further relies on intelligent data fusion at the feature-extraction level combining heterogeneous parameters extracted from Acoustic Emission (AE) [1], Guided Ultrasonic Waves (GUW) [2] and Digital Image Correlation (DIC) [3]. Preliminary results of the proposed approach are presented herein and include crack growth detection in compact tension (CT) specimens and wire breaks monitoring in seven wire steel strand used in bridges. In addition to real time and post-mortem feature-based damage monitoring, a novelty damage detection procedure based on outlier analysis [4] is also used to correlate visual with acoustic features and: i) validate the recorded acoustic data, ii) quantify the observed damage, and iii) develop a robust SHM approach suitable for challenging environments and/or complex damage identification.
Proposed Framework The I-SHM (Fig.1) comprises four comprehensive steps. In the operational evaluation step, the type of damage, loading and environmental conditions are evaluated. In the data acquisition step, the three NDT systems are calibrated and different parametric inputs are fed for communication, triggering and synchronization. In the data post-processing step, filtration techniques are employed and feature extraction from the three NDT systems is executed. Finally, in data fusion, ensemble classifiers are established through correlation algorithms and fusion is carried out to identify the damage present in the structure.
Experimental Setup Case 1- Crack growth: The novel NDT setup was used to monitor the crack growth in the compact tension (CT) pre-cracked aluminum alloy specimens (Fig.2a). The CT specimens were loaded in tension under displacement control, while simultaneously recording AE, GUW and DIC data. A DiSP 4-channel AEWin system with four piezoelectric transducers (Pico) as shown in Fig.2b and preamplifiers with uniform gain of 40db were used to obtain AE and
GUW data. Different ultrasonic pulses centered at 100kHz, 250kHz and 500kHz were generated at a sampling rate of 10MHz using the WaveGen. The signals were band pass filtered in the 100kHz-2MHz range and pencil lead break test was carried out to calibrate the AE system. The DIC measurements were made using a GOM Aramis 3D 5M system. For a 65 x 55 mm FOV the cameras were positioned 485mm from the sample and were 176mm apart.
Case 2- Wire breaks: A similar NDT setup as in Case 1 was used to detect wire breaks in a seven wire steel strand commonly used
in post-tensioned concrete bridges. The load frame was programmed to hold the imposed displacement at specific
Fig 2: (a) CT dimension (b) Transducer placement
Fig 1: Proposed I-SHM Framework
Operational Evaluation
Data AcquisitionData Acquisition
GUW
Par 1Par 2
.
.
.Par N
DIC
Par 1Par 2
.
.
.Par N
AE
Par 1Par 2
.
.
.Par N
Post ProcessingData Post-Processing
Denoising/Compression
Alignment/Correlation
Post ProcessingData Fusion
Ensemble Novelty Features
FeatureExtraction
Decision
Damage Identification
load levels to facilitate triggering of GUW. Two Pico sensors with the same configuration as described previously were used. The GUW were generated with a 16 channel NI PXIe-1062Q with two additional Pico transducers. Ultrasonic pulses centered between 300kHz-500kHz with a step frequency of 50kHz at a repetition rate of 10Hz were generated using a function generator (Fgen). For a 175x150mm FOV, the DIC cameras were positioned 1150mm from the sample and were 472mm apart.
FEM Prediction of Wave Propagation Numerical simulations were employed prior to experiments to observe the wave propagation in the CT specimens (Fig.3) and optimize the position of sensors. The force-time history applied by the transmitter was a 5 cycles Hanning windowed tone burst centered at 500 kHz (Fig.3b). Finite element (FE) simulations carried out in ABAQUS showed that there are no wave perturbations when there is no crack growth (Fig.3c and Fig. 3d). Fig.3c and Fig.3d show the wave propagation after 6 μs and 18 μs respectively for the uncracked specimen. However, scattering effects are seen when the crack length increases. Fig.3e and Fig.3f show the wave propagation after 18 μs at crack lengths of 10mm and 20mm respectively.
Results 1. Damage Detection Crack growth and wire breaks were detected with the three NDT techniques used. DIC monitoring of crack growth resulted in three crack regions namely: i) no extension (Fig.4a), ii) slow crack growth (Fig.4b), and iii) unstable crack growth (Fig.4c). Additionally, the DIC system detected strains ahead of the crack tip, as well as the plastic wake around the crack as it extended. In the recorded AE (Fig.4f-g) the amplitude increased as the crack initiated and grew until the onset of unstable crack growth, after which the amplitude decreased. Similar trends were seen in other extracted AE features. GUW (not shown here) were also characterized by the decrease of their amplitude and RMS as the crack grew due to the scattering at the crack, as also shown in the FEM simulations (Fig.3). DIC monitoring of wire breaks in the bridge strands (not shown in this abstract) revealed strain accumulations at the pre-notched locations prior to breakage. The average strain in the strand increased as the strand was loaded up until the first breakage, after which the strain slightly dropped. Pertinent AE data for this test (Fig.4h-i) show distinct rises of amplitude and energy exactly when the breakages were recorded by the DIC system
2. Damage Location The use of multiple sensors permitted the application of source location algorithms. 2D planar location employed for
Fig.3 (a) FE model of CT specimen (b) Applied load (c) Wave simulation at 6μs at 0mm (d) wave propagation at 18μs at 0mm (e) wave propagation at 18μs at 10mm (f) wave propagation at 18μs at 20mm
0 2 4 6 8 10-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Time [microsec]
Ampl
itude
(a) (b) (c)
(d) (e) (f)
Transmitter
Sensor #3
Fig.4: DIC results with strain maps for (a) no crack growth (b) stable crack (c) unstable crack (d) Imposed load versus crack length measured by DIC (e) Crack rate . AE results for the crack growth (f) amplitude and (g) count time plot. AE results for
the wire breaks (h) amplitude and (i) energy time plot
(a) (b) (c)
(d) (e)
apao
Stable zone
to tp
Stable zone
Unstable zone
Unstable zone
(f)(g)
(h) (i)
the crack growth resulted in two clusters of events (Fig.5a) in the case of the CT sample. The top cluster corresponded to transmitted GUW and the center cluster of events corresponded to AE events due to the crack growth. Linear location algorithms were employed to locate the wire breaks (Fig.5b); they accurately show that the breaks occurred at the center of strand exactly at the pre-notch locations..
3. Classification\Identification Statistical pattern recognition (SPR) algorithms were employed to cluster the AE events recorded in order to identify the sources of these events (Fig.6). K-means and Cluster seeking algorithms used selected AE features such as duration, absolute energy, amplitude and peak frequency for clustering the AE events shown in Fig.6. In the crack growth case, activity is seen to pick up when the crack is initiated (75s) and when it becomes unstable (180s). In the wire strand case, the results show two distinct classes with jumps at times when breaks were monitored. 4. Outlier Analysis Finally, a data fusion approach was established by combining features from the GUW and the DIC techniques to
enhance the damage detection process. Extracted features such as time of arrival, RMS, amplitude, distance between a fixed origin and the crack tip and maximum strain were combined in a data fusion approach using outlier analysis (Fig.7). The analysis was based on the Mahalanobis distance D, where
A stepwise trend is clearly observed in all damaged conditions; crack growth is accurately classified as an outlier for all considered crack increments. The values of the Mahalanobis squared distances for the damage data are several orders of magnitude larger than the corresponding values of the undamaged data. Consequently, the
sensitivity to damage is considerably improved.
Conclusion A novel I-SHM approach was designed and successfully used to detect, locate and classify damage. Source identification was performed through both location and clustering algorithms and was validated with results obtained through the use of DIC. Finally, a novelty damage detection scheme based on multivariate analysis was tested to quantify developing damage. The multiple NDT techniques implemented in the I-SHM approach appear to operate successfully in a complementary manner. Further developments of the proposed approach are being carried out at Drexel University targeting civil, mechanical and aerospace structural health monitoring applications.
References 1. Carpinteri, A.L., G; Pugno, N. Engineering Fracture Mechanics, 2007. 74: p. 273-289. 2. Clarke, T., F. Simonetti, and P. Cawley. Journal of Sound and Vibration, 2010. 329(Copyright 2010, The
Institution of Engineering and Technology): p. 2306-22. 3. McCormick, N. and J. Lord. Materials Today, 2010. 13(12): p. 52-54. 4. Worden, K., G. Manson, and N.R.J. Fieller. Journal of Sound and Vibration, 2000. 229(Compendex): p.
647-667.
Fig.6: SPR analysis (a) Crack growth (b) Wire breaks
Fig.7: Outlier analysis of Crack growth
1.0E+00
5.0E+07
1.0E+08
1.5E+08
2.0E+08
2.5E+08
3.0E+08
3.5E+08
0 10 20 30 40 50
Mah
aban
olis Distance
Number of Samples
0mm 18.5mm15mm10mm5mm
Threshold
Fig.5: (a) 2D location of crack growth AE events and GUW signals (b) Linear location of wire breaks AE events
a) b)
a) b)
18
S2-6 Structural Health Condition Monitoring of Rail Steel Using Acoustic Emission Techniques. Yilmazer and Papaelias.
STRUCTURAL HEALTH CONDITION MONITORING OF RAIL STEEL USING ACOUSTIC EMISSION TECHNIQUES
Pinar Yilmazer and Mayorkinos Papaelias
Centre for Railway Research and Education, University of Birmingham, UK Contact E-Mail: [email protected] (Pinar Yilmazer)
Abstract
This paper discusses the work towards the development of a sound methodology for the application of acoustic emission techniques for detecting and monitoring crack growth in rail steels. At the moment rails that are found to be damaged on the network are not immediately replaced. Instead a speed restriction is imposed and affected rails are clamped with special fish-plates until they are replaced, usually after a period of a few days. Unfortunately damaged rails prior to their replacement cannot be reliably assessed for further crack growth with any of the conventional non-destructive evaluation techniques (e.g. ultrasonic) and this is the reason that a speed restriction is imposed. Acoustic emission techniques could potentially be applied for the detection as well as continuous monitoring of further crack growth therefore removing the need of imposing strict speed restrictions which can be as low as 20MPH (32km/h). In order to validate the potential of the AE technique tests are carried out under laboratory conditions on three and four-point bending samples as well as tensile test specimens. Various signal processing methods will be examined using AEWin and Noesis software supplied by PAC and Envirocoustics respectively. The effect of rolling stock noise on the acoustic emission signal will also be investigated and methods for filtering the effect of unwanted background noise are under consideration.
Keywords: Acoustic Emission, Rail Steel, Crack Growth Monitoring
19
ABSTRACTS
AEWG-54
May 21 – 22, 2012
PRINCETON, NJ
Session 3: Signal processing and New Location Algorithms
20
S3-1 Numerical verification of AE tomography algorithm with new AE source location technique. Kobayashi and Shiotani.
NUMERICAL VERIFICATION OF AE TOMOGRAPHY ALGORITHM WITH NEW AE SOURCE LOCATION TECHNIQUE
Yoshikazu Kobayashi1†*and Tomoki Shiotani2
Department of Civil Engineering, College of Science and Technology, Nihon University, Japan
Graduate School of Business and Graduate School of Engineering, Kyoto University, Japan
†Speaker & corresponding author: e-mail: [email protected]
address: 1-8-14, Kanda-Surugadai, Chiyoda-ku, Tokyo, 101-8308, Japan
phone: +81-3-3259-0575, fax: +81-3-3259-0575
Abstract
Acoustic emission tomography can be applied to evaluate the integrity of civil engineering structures without any disturbance as to stimulate artificial elastic waves to the structures. When implement AE tomography, accurate identification of AE sources is the most crucial prerequisite to do the iteration procedure for obtaining velocity’s distribution over the structures. In conventional AE source location algorithm; however, as it has been assumed that AE wave ray-paths among signal sources and sensors are straight to simplify the computational process (SCHUBERT, 2006), large errors will be arose as damage increases within the structure. In actual the ray-paths among signal sources and sensors will not be straight since wave velocity does not distribute uniformly within/ over the structures due to the damage caused by the repetition of in-service loads or natural phenomena. Therefore, it is highly desirable to study more accurate source location algorithm to consider the resulted heterogeneous feature of wave velocity distribution over the structure.
The authors has been studied an algorithm of elastic wave tomography that takes the effect of inhomogeneous velocity distribution without source information. The most adequate ray-path is searched from all possible ray-paths in the structure by ray-trace procedure and source location is identified by selecting a nodal or relay point of minimum variance of AE generation times that are estimated from the result of the ray-trace in the method. In the presentation, numerical verification of the method is conducted and its accuracy is discussed in terms of number of signals and ray-path density.
Keywords: Acoustic emission tomography, new source location technique, wave ray-trace algorithm, system identification
References: Frank SCHUBERT, Tomography Techniques for Acoustic Emission Monitoring, ECNDT 2006 Proceedings.
21
S3-2 Development of a Probabilistic Acoustic Emission Source Location Algorithm. Schumacher et al.
Development of a Probabilistic Acoustic Emission Source Location Algorithm
Thomas Schumacher1, Daniel Straub2, Lassaad Mhamdi3
1Assistant Professor, University of Delaware 2Associate Professor, Technical University Munich, Germany
3PhD Student, University of Delaware Abstract: The perhaps most important step in quantitative Acoustic Emission (AE) analysis methods is the estimation of source locations. Since only the time arrivals at each sensor are observed, this represents a nonlinear inverse problem that is commonly solved by iterative procedures whereby a cost function is minimized. The main issue of these methods is that sources of uncertainty and variability in the material properties cannot be associated with any of the involved model parameters such as p-wave arrival times, p-wave velocity, sensor locations, etc., essentially treating all parameters as deterministic using mean values. This study introduces a Bayesian approach for a probabilistic source location algorithm using Markov Chain Monte Carlo (MCMC) simulation whereby all model parameters are described with probability density functions. The proposed methodology was implemented to estimate model parameters using examples employing arrival time data collected from concrete and reinforced concrete test specimens, and then to construct a predictive algorithm that is employed for future observations. Here we present the framework of the algorithm, results from the experiments, and a discussion of the capabilities and limitations of this new approach. Planned future research will be outlined. Keywords: Structural Health Monitoring, Acoustic Emission, Probabilistic source location algorithm, Markov Chain Monte Carlo simulation, Bayesian analysis Corresponding author: Thomas Schumacher, PhD Department of Civil and Environmental Engineering University of Delaware 301 DuPont Hall Newark, DE 19716 Phone: 302-831-4559 E-mail: [email protected]
22
S3-3 Method to Evaluate the Incompleteness due to Lossy Acquisition of AE. Fan and Qi (student paper).
1
Method to Evaluate the Incompleteness due to Lossy Acquisition of AE
Ming Fan, Gang Qi
Department of Mechanical Engineering, University of Memphis, Memphis, TN 38152 USA
I. INTRODUCTION In the process of signal acquisition, the impacts of the variation of monitoring conditions such as the threshold,
the sensor location and the gain of system on the measurements become crucial, and the incompleteness of signal is
an obvious consequence. The assessment of such impacts is beyond or not considered under the scope of frequency
analysis in acquisition. However, they may influence the measurements based on time-domain analysis substantially,
and it is often complicated to obtain meaningful measurements influenced by these conditions. It is even more so
when these conditions are in conjunction with the presence of continuous permanent damage involving acquisition
of the responses of material’s microstructures, which are normally dominated by contents in time-domain in the
acquisition, such as the number of events of acoustic emission (AE), which is been widely used to characterize and
evaluate material performance. The complications of these AE measurements involve stochasticity due to variations
of the hierarchical microstructures and the evolution of these structures under stress field, and they must be
considered in time-domain. In addition, these monitoring conditions are likely problematic in real life engineering measurements. The purpose of this work is to employ a novel multivariate approach, which was developed in our
recent works to takes into account the stochasticity involved in acquisition statistically[1, 2], to address impacts of
lossy acquisition on the evaluation of measurements.
II. LOSSY ACQUISITION AND INCOMPLETENESS IN TIME DOMAIN
1. Incomplete measurements
Let χ be the measurements in the process of a lossy acquisition, and let χREF be the reference measurement; and let
Δχ be the measurements difference between χREF and χ; let λ to be the ratio of Δχ to χREF so that,
(1) (2)
REF
REF
Assume χREF is the measurement of non-lossy acquisition, Δχ and λ are the absolute amount of loss and the
percentage of loss with respect to the selected reference, χREF, respectively. Thus, Δχ and λ are quantification of
incompleteness. In this work, λcon and λent symbolize the ratio based on conventional and our method, respectively.
2. Incompleteness sources
The sources of incompleteness may be the threshold setup and the attenuation effect, which results in the loss of
acquisition with low amplitude contents; another source is the gain of acquisition system, which may cause the
incompleteness by losing the acquisition with high amplitude contents. We conducted experiments to simulate the
effects caused by these sources, and lossy acquisition was obtained by filtering the acquired signals according to the
amplitude. For instance, when χ35 serves as the reference, χ55-, Δχ55- and λ55- represent the lossy acquisition missing
signals of amplitude less than 55dB, the net loss between 35~55 dB, and the percentage of loss, respectively.
Similarly, λ55+ represents the percentage of loss due to missing signals of amplitude exceeding 70 dB.
III. MULTIVARIATE REPRESENTATION OF ACQUISITION
Let B and B (normalized B) be,
11 1
B
1
:
N
ij M N
M MN
(3)
B ij M Nf
(4)
where βij (i = 1, 2, …, M, j = 1, 2, …, N) is the number of acquired signals over an observation interval of (0, i), and
j indexes the scales of the amplitude bandwidth. M is the total number of interval over which observations are made,
and N is number of scales that divide the bandwidth of the acquired signals. fij is the probability over an observation.
The probabilistic entropy serves as a measurement to assess acquired lossy acquisition. 10
1
ln(0.1 / ) for 1, , (5)ij ij
j
s f f i M
IV. SPECIMENS AND ACOUSTIC EMISSION MONITORING A total of 18 specimens, commercial polymeric bone cement, were tensioned to rupture at a crosshead
displacement rate of 1 mm/min. AE were monitored by gluing 3 sensors (Nano 30) to the specimen surface. The
sensor’s the resonant frequency and the operating frequency ranges are 140 kHz and 125-750 kHz, respectively. The
acquired signals were amplified by a preamplifier (AEP4: 40 dB gain) before inputting to an AE system (ASMY-5).
The maximum amplitude was 98.3 dB (rounded to 100 dB for simplicity).
V. RESULTS
2
1. Observations of lossy acquisition and incompleteness by conventional method
Figs. 1 and 2 show the measurements and incompleteness of lossy acquisition due to losing low and high
amplitude contents, and the measurements base on the accumulative number of AE events. In Fig. 1, λcon = 78.1%,
45.8%, and 35.5%, respectively, to the selected conditions of 6, 12, and 36 MPa as the threshold is 45dB.
Fig. 1. The variation of lossy signals and incompleteness of losing low amplitude contents by conventional method.
Fig. 2. The variation of lossy signals and incompleteness of losing high amplitude contents by conventional method.
2. Observations of lossy acquisition and incompleteness by multivariate approach
Figs.3 show same results of the lossy acquisition of χ40- to χ80- as those in Fig. 1 in the statistical spectra of the
acquisition. The losses of low amplitude contents are observed clearly from their absence.
Fig. 3. Spectra measurements due to lossy acquisitions of losing low amplitude contents χREF, χ55-, and χ80-.
Figs. 4 are observations similar to those in Figs. 3, but due to losing high amplitude contents.
Fig. 4. Spectra measurements due to lossy acquisitions of losing high amplitude contents χ90+, χ70+ and χ50+.
Figs. 5 show the variation of entropy with respect to the stress when the lossy acquisition is due to loss of low and
high amplitudes contents, respectively.
Fig. 5 The impact of incompleteness on entropy. (a) losing low amplitude contents; (b) losing high amplitude contents.
3. Incompleteness comparison: λ-λ curve
The impacts of lossy acquisition on λ are given in Figs. 6. In the case of losing low amplitude contents, λ
decreases significantly by our approach. For instance, λcon and λent are ~50% and ~14% (σ = 6MPa), respectively,
3
and λent < 5% (σ>12MPa) as indicated in the case of χ40- in Fig. 6a. Interestingly, the observations are nearly opposite in the case of losing high amplitude contents as shown in Fig. 6b.
Fig. 6 λ-λ curve for comparison of the impacts of lossy acquisition on incompleteness.
(a) loss of low amplitude contents (b) loss of high amplitude contents.
VI. DISCUSSION
1. The significance of lossy acquisition in time domain
The incompleteness is dependent on the contents of loss and the conditions, and the loss of low amplitude
contents seems to contribute more substantially to measure the random damage accurately than those of high
amplitude contents. This is because the pattern of entropy vs. measurement condition (load) curve varies: the
increase of the slope of the linear-increasing portion; translation of the knee position between the linear and stable
portions (Fig.5a). The ANOVA results are presented in Tables 1 and 2. The conceptual slope and the knee in the s-σ
curve were found to be associated with nucleation and initiation of permanent damage in very early loading stages[2],
which we had inferred to be the initial conditions. Emulation of measurements with loss of low amplitude contents
provides yet further evidence to the significance of the initial conditions established in early loading.
Table 1. ANOVA results of loss of low amplitude contents Table 2. ANOVA results of loss of high amplitude contents
2. The determinants of incompleteness ratio
λcon increases significantly because of a significant decrease of the total number of acquired signals when losing
low amplitude contents from 35 dB to 55 dB. Howerer, λent varies insignificantly because the probability distribution
of the acquisition remains little of no changes in comparing with reference. On the other hand, when losing high
amplitude contents, the amount of acquired signal varied slightly, and λcon remains nearly unchanged; however, λent
varied significantly. This is because the probability distribution of the acquisition becomes bias due to the loss of
high amplitude contents, although their quantity is limited. This biased probability distribution alters the entropic
values significantly, so does the incompleteness ratio. Hence, The probability distribution of acquisition need to be
verified when our method is employed to reduce the impact of incompletenss: if the probability space does not affect
by the incompletenss, our method can minimize the influence caused by it; on the contrary, if the probability space
significantly affected by the incompletenss, λent will be affected as well.
VII. CONCLUSION
Lossy acquisition in time-domain affects accurate measurements of physical conditions significantly, and in most
cases unavoidable, especially measurements involving randomness concerns that deserve statistical attention in
which conventional methods fail to account for. Multivariate approach established in our work is capable of doing
so by evaluating the effects of lossy acquisition on measurements. The result evidences that early damage plays
significant role in the performance of a solid material. We also conclude that an entropic scalar can be used to
manifest the trend in the measurements.
REFERENCES [1] G. Qi, Fan M, Wayne SF. “Measurements of a multi-component variate in assessing evolving damage states using a
polymeric material,” IEEE Trans. Instrum. Meas., vol.60, pp.206-213, 2011. [2] G. Qi, M. Fan, G. Lewis, and S. Wayne. “On addressing the hierarchical and evolutive microstructures in solids by an
innovative damage state variate.” J. of Materials Science, Mater Med, 23:217-228, 2012
Incompleteness Knee Slope ANOVA Result
χ REF 12MPa 0.12 No Significant
Difference χ 40+ 12MPa 0.14
χ 45+ 12MPa 0.13
χ 50+ 12MPa 0.20 No Significant
Difference χ 55+ 12MPa 0.21
χ 60+ 14MPa 0.19
Significant
Difference
χ 65+ 16MPa 0.22
χ 70+ 16MPa 0.21
χ 75+ 18MPa 0.21
χ 80+ 22MPa 0.20
Incompleteness Knee Slope ANOVA Result
χ REF 12MPa 0.12
No Significant
Difference χ 90- 12MPa 0.12
χ 80- 12MPa 0.13
χ 70- 12MPa 0.15
No Significant
Difference
χ 60- 12MPa 0.15
χ 50- 12MPa 0.14
23
S3-4 Time reverse modeling of acoustic emissions in reinforced concrete members. Kocur. TIME REVERSE MODELING OF ACOUSTIC EMISSIONS IN REINFORCED CONCRETE MEMBERS
Georg Karl KOCUR
ETH Zurich, Institute of Structural Engineering, Wolfgang-Pauli-Strasse 15, 8093 Zurich, Switzerland
Email: [email protected]
Abstract
In the last decade signal-based acoustic emission (AE) methods became to reliable tool for the localization of concrete cracking. Although multiple AEs may occur simultaneously in space during cracking, commonly applied onset-time-based methods rely on the P-wave part only. After cracking, the excited elastic waves interfere with each other and interact with boundaries. In that case, localization limited on the onset times will lead to a result with no physical meaning. The presented work aims at establishing a novel wave-propagation-based localization method, called the time reverse modeling (TRM). The TRM is recently applied in the area of geophysics and now transferred to signal-based AE analysis on unreinforced and reinforced concrete (RC) members. TRM uses signals obtained from physical experiments, such as AE recorded by piezoelectric sensors, as input. The signals are re-emitted numerically into a structure as sources in a time reversed manner. In the so-called inverse simulation the wavefronts interfere and appear as dominant concentration of energy at their origin, respectively the AE source. Destructive physical experiments on different scales; on a concrete cuboid (120×118×160 mm), a RC beam (120×200×1700 mm) and a RC slab (200×800×2620 mm) were carried out. AE signals were recorded for each experiment for the entire load history. Based on the recoded signals TRM is applied to image AE sources successfully in three dimensions. It can be demonstrated that TRM performs well on AE signals and leads to promising results independent of the scale of application.
Keywords: Time reverse modeling, acoustic emission analysis, elastic wave propagation, source localization, reinforced concrete
24
S3-5 Data-driven Modeling of Damage Induced Acoustic Wave Propagation. Kontsos et al.
Data-driven Modeling of Damage Induced Acoustic Wave Propagation
D. Servansky1, P.Abraham1, Ivan Bartoli2 and A.Kontsos1,*
1Department of Mechanical Engineering & Mechanics, Drexel University, Philadelphia, Pennsylvania
2Department of Civil, Architectural and Environmental Engineering, Drexel University, Philadelphia, Pennsylvania
Abstract
The "forward problem" of simulating acoustic emission (AE) is addressed herein by a combination of experimental, analytical and computational information to form a Finite Element (FE) damage induced wave propagation model. Technology advances related to the sensing part in Nondestructive Testing (NDT) for structural health monitoring of civil, mechanical and aerospace structures, allow better understanding of the mechanics and physics related to damage initiation and development in materials. Conversely, damage quantification in continua could contribute significantly in the design of the next generation of sensing systems for reliable inspection of infrastructure subject to external loading. In this context AE, being a part of the transient process towards a new equilibrium state following the sudden energy release caused by crack initiation, is simulated following a data-driven approach. Specifically, data obtained by in situ monitoring crack growth in a compact tension Aluminum alloy specimen subject to Mode I loading comprising full-field strain measurements from a Digital Image Correlation (DIC) system are used in a fracture mechanics context and compared to predictions of the crack-tip strain fields. The experimentally determined displacement/strain fields from DIC are then used to define parameters of a traction-separation law suitable to account for the equilibrium state following crack initiation. This law is subsequently used in a cohesive-zone model in FE simulations. To create a validated AE source model, a link is formed between static and dynamic FE analyses. Simulated AE signals at different locations of the specimen geometry are compared to AE waveforms experimentally recorded by using piezoelectric sensors. The results are evaluated in the context of the "inverse problem" of source identification given recorded NDT data.
Keywords: Modeling, Acoustic Emission, Finite Elements, Damage, Wave Propagation
*[email protected], phone 215-895-2297, fax 215-895-1478,
3141 Chestnut Street, Philadelphia, PA, 19104
web page: http://www.mem.drexel.edu/kontsos/
25
S3.6 Using AE to address the ensemble interactions in hierarchical microstructures where damage events present, while largely ignored. Qi et al.
Using Ae To Address The Ensemble Interactions In Hierarchical Microstructures Where Damage Events Present, While Largely Ignored
G. Qi,1,2 M. Fan,2 and J.Y. Li1
1 School of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin, 300222, China
2 Department of Mechanical Engineering, The University of Memphis, Memphis, TN 38016, USA
Abstract
The ensemble interactions in the evolving hierarchical microstructures where damage events present are the most, and yet largely ignored and poorly understood in the analysis of material failure. The present work quantified the correlation of evolving hierarchical damage using an approach of multivariate analysis. In this work, we defined a multi-variate ∆A to describe the state of damage. Using this multi-variate, we found that the responses of random damage events to pure tension and three-point bending can be categorized clearly into two distinct populations by Andrews’ plot. Similarly, we found the responses of random damage events to the stage of loading can also be categorized to be different populations. We found that the Spearman rank correlation is, in general, statistically significant across the components of ∆A variate. The strength of correlation of tension is much stronger under tension than that under bending, much stronger in the stage of pre-knee stage than that in the stage of post-knee and much stronger across the components with low scales of ∆A variate. Furthermore, we found that the strongly coupled ∆A variate can be transformed orthogonally to be decoupled principal components, PCs, in spacetime. A PC can be unidirectional, bi-directional, and interwoven, which may be used to indicate the extended hierarchy and cascading damage evolution.
26
ABSTRACTS
AEWG-54
May 21 – 22, 2012
PRINCETON, NJ
Session 4: Material Characterization
27
S4-1 A Novel Framework for Scale-bridging Characterization of Materials Mechanical Behavior Using Acoustic Emission..Kontsos et al.
A NOVEL FRAMEWORK FOR SCALE-BRIDGING CHARACTERIZATION OF MATERIALS MECHANICAL BEHAVIOR USING ACOUSTIC EMISSION
K.Hazeli, P.Abraham, J.Cuadra, E.Schwartz, R. Saralaya and A.Kontsos*
Department of Mechanical Engineering & Mechanics Drexel University, Philadelphia, Pennsylvania, USA
Abstract
Although a variety of experimental, analytical and computational tools is available to investigate the linear/non-linear, elastic/plastic, hysteretic/fatigue behavior of materials and various failure modes activated in them during loading, critical issues related to material microstructure, its evolution, as well its direct association with progressive deformation and damage development are of major importance in view of modern engineering applications and existing aging infrastructure. The objective of this talk is to present a novel framework based on the concurrent use of the Acoustic Emission (AE) method with both other nondestructive testing methods and material characterization techniques to assess accurately and quantitatively the initiation and development of reversible/irreversible microstructural changes in metallic materials used for both automotive and structural applications. Targeted mechanical tests and careful pre- and post-test microstructural quantification are combined with AE monitoring data to investigate key deformation and damage mechanisms in novel metallic alloys tested under both monotonic and cyclic loading conditions. The presented results demonstrate the sensitivity of the AE method in volume inspection of microstructural changes that affect the bulk mechanical behavior, which when in situ/ex situ validated, it further demonstrates the capability of the method for scale-bridging identification of structure-property-performance linkages.
Keywords: Mechanical testing, Characterization, Deformation, Damage, Acoustic Emission
*[email protected], phone 215-895-2297, fax 215-895-1478,
3141 Chestnut Street, Philadelphia, PA, 19104
web page: http://www.mem.drexel.edu/kontsos/
28
S4-2 Using Acoustic Emission to Monitor Microstructural Damage During Deformation of Multiphase Steels. Fekete.
USING ACOUSTIC EMISSION TO MONITOR MICROSTRUCTURAL DAMAGE DURING DEFORMATION OF MULTIPHASE STEELS
James R. Fekete Materials Reliability Division
National Institute of Standards and Technology 325 Broadway
Boulder, CO 80305 Phone: (303) 497-5204
e-mail: [email protected]
David McColskey National Institute of Standards and Technology
Marvin Hamstad
University of Denver, National Institute of Standards and Technology
Abstract
Increasing pressure to improve fuel economy and safety has led to significant increases in the strength of steels used to fabricate vehicle body structures. The steel industry has responded by developing advanced multiphase high strength sheet steels which are significantly stronger than conventional sheet steels, but they retain significant levels of formability required for fabrication into structural members. However, these steels also exhibit fracture behavior that is unusual for sheet steel and not predictable using conventional methods such as forming limit diagram analysis. The development of models that can predict fracture behavior of these materials is an important goal. Acoustic emission measured during deformation may provide insight on the damage evolution during deformation, and it can provide important input into these models, especially if microstructural influence can be discerned. In these experiments, two different multiphase steels with tensile strengths at the 1000 MPa level and with measurably different microstructures and fracture behavior were subjected to conventional and plane strain tensile testing. In these tests, acoustic emission (AE) from quasi-continuous AE was measured by sensors located on the samples. To date the primary AE data has been root-mean-square (RMS) versus displacement and load. Though the two materials had similar stress strain response, they had significantly different acoustic emission characteristics, and we are currently exploring the reasons for this behavior. Keywords: Multiphase steels, plane strain testing, tensile testing, RMS of AE vs. different steels, advanced high strength steel
29
S4-3 Microfracture Process in Bone Characterized by Acoustic Emission. Wakayama.
MICROFRACTURE PROCESS IN BONE CHARACTERIZED BY ACOUSTIC EMISSION
Shuichi WAKAYAMA; Department of Mechanical Engineering, Tokyo Metropolitan University, 1-1 Minami-Ohsawa, Hachioji-shi, Tokyo 192-0397
Email; [email protected]/ Tel; +81-(0)42-677-2714/ Fax; +81-(0)42-677-2701
Keita YASUI; Graduate School of Science and Engineering, Tokyo Metropolitan University
Takenobu SAKAI, Department of Mechanical Engineering, Tokyo Metropolitan University
Abstract
Microdamage in bovine bone under cyclic compression and torsion was monitored by acoustic emission technique. Cyclic compressive load was applied to the rectangular parallelepiped specimens along longitudinal direction, which corresponds to the major loading in life. Final fracture surfaces of all specimens were perpendicular to the radial direction. During cyclic compression tests, AE signals were detected by a wide-band AE sensor attached on the radial direction of samples and classified into loading and unloading AE. It was shown by the wavelet analysis that the peak frequency of AE signals corresponded to the resonant frequency along the radial direction of specimen. It was then found that these signals ware emitted from opening microcracks perpendicular to radial direction. It is important that those signals were detected during unloading. SEM observation of the cross section suggested that those AE signals were emitted from opening microcracks due to the wedge effect during unloading. Finally, it was clarified that damage was accumulated during unloading as well as loading in the compressive fatigue fracture of bone.
On the other hand, microcracks during static torsion tests of cylindrical bovine bone specimens were monitored by 2ch AE transducers. AE signals with opposite phase were detected before the maximum torque and those with same phase after the maximum. According to AE radiation pattern, the former corresponds to shear cracks and the latter opening cracks. These results were consistent with fracture behavior. The obtained results are useful for establishing the noninvasive diagnosis technique of bone fracture using acoustic information.
Keywords; Bone fracture, Microfracture process, Wavelet analysis, AE radiation pattern
30
S4-4 Characterization of Lüders Deformation in Low-Carbon Steel by Acoustic Emission and IR thermograph. Shiraiwa et al.
CHARACTERIZATION OF LÜDERS DEFORMATION IN LOW-CARBON STEEL BY ACOUSTIC EMISSION AND IR THERMOGRAPHY
Takayuki Shiraiwa1*, Manabu Enoki1, Su-Un Kim2 and Man-Yong Choi2
1 Department of Materials Engineering, The University of Tokyo
2 Center for Environment and Safety Measurement, Korea Research Institute of Standard and Science
* Corresponding author. Address: Room 317, Faculty of Engineering Bldg. IV, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan;
Tel.: +81 3 5841 7129; E-mail: [email protected]
Abstract
Acoustic emission (AE) method and infrared (IR) thermography have been used to study the development of Lüders band during uniaxial tensile tests at different strain rates in low-carbon steel. AE signals were recorded by four AE sensors. Two typical types of AE signals were detected during Lüders deformation. The first is burst-type signal caused by the formation and motion of Lüders band. The location results of burst-type AE signals provided good agreement with the motion of Lüders band. The second is continuous-type signal caused by the rapid sequence of overlapping events. The energy of continuous-type signal increased at high strain rate. Accordingly, the burst-type AE signals could not be accurately detected when the strain rate exceeded 10-3 s-1. Furthermore, thermal images were measured by IR camera. It revealed the number, orientation and velocity of Lüders band. These results were compared with optical observation on a mirror-finished specimen using digital camera and were supported by them. Therefore, the feasibility to observe Lüders band behavior in low-carbon steel using AE method and IR thermography was confirmed. The combination of AE method and IR thermography will permit the observation over a wide range of strain rate with easy handling and wide detection area. Moreover, evaluation of dislocation behavior in this material was attempted using the energy of continuous-type AE and Lüders band velocity measured by IR thermography and the location of burst-type AE signal. The combination of two methods has demonstrated a great potential for understanding of deformation mechanics and damage detection.
Keywords: Lüders band, Low-carbon steel, Infrared thermography, tensile test, strain rate
31
S4.5 Acoustic Emission analysis in media with rapidly changing Green’s functions-experiments considering thermo-hydro-mechanical changes. Grosse et al.
ACOUSTIC EMISSION ANALYSIS IN MEDIA WITH RAPIDLY CHANGING GREEN’S FUNCTIONS – EXPERIMENTS CONSIDERING THERMO-HYDRO-MECHANICAL CHANGES
Ronald Richter, Norbert Meier, Christian Grosse
Non-destructive Testing, Center for Building Materials
Technische Universität München,
Baumbachstr. 7
81245 Munich, Germany
+49-89-289-27221
Abstract
In most cases acoustic emission techniques are applied at more or less homogeneous media. More detailed AE analysis beyond the counting of events uses then homogeneous velocity models for 3D localization. If the material exhibit anisotropic wave properties the models become a bit more complicated but localization is still possible. In certain cases the materials properties are not time invariant and instead rapidly changing during an AE experiment. Localization becomes ambitious in these cases. The paper will present an example.
High performance concrete (HPC) is often used in tunneling, for columns of high raise buildings and similar structures, which require high compressive strength. However, when exposed to high temperature (fire) there is a strong degradation of mechanical properties of HPC and it shows unfavorable behavior i.e., explosive spalling of concrete cover. In several experiments the behavior of concrete specimen that were exposed to fire was monitored by acoustic emission techniques. Damage processes in concrete can be observed during the entire fire history including the localization and characterization of micro-cracking before failure. The changes of Greens’ functions due to fire load are related to thermo-hygro-mechanical effects like the drastic increase of pore pressure in combination with thermally induced stresses. This finally leads to explosive spalling being recorded by AE techniques.
Keywords: acoustic emission, fire, finite element simulation, micro-cracking, high-performance concrete
32
S4.6 Investigation of AE from freezing/melting transitions. Azimov et al.
INVESTIGATION OF AE FROM FREEZING/MELTING TRANSITIONS
Shavkat Azimov, Vladislav Petukhov, Farkhod Choriev, Abdukholik Lakaev
The Physical-technical Institute of Academy of Sciences of Republic of Tajikistan
Contact author: Shavkat Azimov
Address: 299/1 Ainy St., the Physical-technical Institute, Dushanbe, 734063,
the Republic of Tajikistan.
Phone: +992 918 676 995.
E-mail: [email protected].
Abstract AE during solutions freezing/melting processes is a well known phenomenon. Authors monitored AE through the freezing/melting transitions of glucose water solution at various concentrations. Molality varied from 0 to 3,7. Comparison of experimental AE rate and Temperature graph during pure ice melting showed the stationary AE generation that finished in the end of phase transition. Solutions of glucose in water at various concentrations have been frozen at different temperatures under the Raoult's law. The melting of frozen glucose solution of higher concentrations causes lower AE rate at all other equal conditions. It could be preliminary explained by glucose solution viscosity growth that reduces density jumps of liquid in micro volumes at the boundary of phases supposed being a source of AE events. Further details of these processes will be studied. Key words: freezing; melting; glucose solution; Raoult's law.
33
ABSTRACTS
AEWG-54
May 21 – 22, 2012
PRINCETON, NJ
Session 5: AE in Composite Materials and Structures
34
S5-1 Extending Composite Overwrapped Pressure Vessels (COPV) Service Life using Acoustic Emission Testing and Stress Rupture Lifetime Prediction Models. Hay.
EXTENDING COMPOSITE OVERWRAPPED PRESSURE VESSELS (COPV)
SERVICE LIFE USING ACOUSTIC EMISSION TESTING AND STRESS RUPTURE
LIFETIME PREDICTION MODELS
Thomas R. Hay, PhD President
WavesinSolids LLC 2134 Sandy Drive Suite 14
State College, Pa 16803 814-237-1031
Abstract This paper summarizes the work that has been performed on acoustic emission (AE) testing of composite overwrapped pressure vessels (COPVs) over the last 20 years and associated stress rupture lifetime prediction models. Current research on AE of COPVs is also presented with emphasis on the technique’s ability to detect COPV failure mechanisms and development of the technology as a suitable cylinder recertification technique. Initial work was performed on Type 3 SCBA cylinders. Impact damage in the range of 170 to 670 Ft-Lbs was inserted into the SCBA cylinders. The cylinders were then subjected to a dynamic pressure loading schedule and acoustic emission was recorded. The analysis in this paper discusses the correlation between the known impact damage levels and the acoustic emission detected during the tests. A pathway for integration of acoustic emission test results into lifetime prediction models is presented. A COPV cylinder recertification program based on AE testing is also discussed. Keywords: Acoustic emission, composite cylinder, stress rupture, impact damage
35
S5-2 Distinguishability of failure mechanisms in carbon fiber reinforced plastics: Influence of acoustic emission sensor type. Sause and Horn.
DISTINGUISHABILITY OF FAILURE MECHANISMS IN CARBON FIBER REINFORCED PLASTICS: INFLUENCE OF ACOUSTIC EMISSION SENSOR TYPE
M. G. R. SAUSE and S. HORN
University of Augsburg, Institute for Physics, Experimental Physics II, D-86135 Augsburg,
phone ++49-821-598-3238, email: [email protected]
Abstract
Pattern recognition techniques can be applied to acoustic emission signals detected during loading of carbon fiber reinforced plastics (CFRP) in order to distinguish between different failure mechanisms. Based on frequency parameters, the distinction is made between matrix cracking, interfacial failure and fiber breakage. For such frequency analysis, the frequency dependent sensitivity of the acoustic emission sensor determines the bandwidth of the detected acoustic emission signals. To investigate the influence of the acoustic emission sensor in this respect, five different sensor types were tested under identical experimental setups. Resonant sensors, multi-resonant sensors and broad-band sensors were investigated. All sensors were first characterized using a large aluminum block as well as aluminum and CFRP plates in their response to a typical test source, like pencil lead break and piezoelectric pulser. In addition, all sensors were tested within the same fracture mechanics experiment of a double cantilever beam test. In contrast to the commonly used specimen length of 250 – 300 mm, a specimen length as large as 570 mm was used for the experiments. This increases the propagation length of the crack during loading and can also be used to assess the influence of signal propagation length in one experimental setup. In their sensitivity and frequency response all sensors were compared to one multi-resonant sensor acting as reference.
Keywords: AE Sensors, Pattern recognition techniques, Source identification
36
S5-3 Acoustic Emission Failure Mechanism Classification In Repaired Fiberglass/Epoxy Compression Specimens Using A Self-Organizing Map Neural Network. Barsoum et al.
ACOUSTIC EMISSION FAILURE MECHANISM CLASSIFICATION IN REPAIRED FIBERGLASS/EPOXY COMPRESSION SPECIMENS USING A SELF-ORGANIZING
MAP NEURAL NETWORK
F.F. Barsoum, Y. Zhang, N.G. Vasjaliya, M. Siachourou, T.A. Thomas and P.J. Barad
Embry-Riddle Aeronautical University, 600 S. Clyde Morris Boulevard, Daytona Beach, FL 32114-3900
(386) 226-6618; [email protected]
E.v.K. Hill
Aura Vector Consulting, 3041 Turnbull Bay Road, New Smyrna Beach, FL 32168-5437
(386) 341-6382; [email protected]
Abstract
Composite materials have been widely used on modern aircraft. However, laminated materials are prone to impact damage which requires repair on the damaged area. A literature review revealed that laboratory testing on repaired composites has rarely been done, and commonly used nondestructive testing methods like ultrasonic C-scan can only detect the defects of the repair but not the strength of it. This research utilizes the acoustic emission (AE) method and Kohonen self-organizing map (SOM) neural network to investigate the failure mechanisms of repaired composite specimens under compressive load. Eight fiberglass/epoxy specimens were manufactured, and a step repair was performed on the simulated impact damage area. The specimens were then compressively loaded to failure while AE data were obtained. The AE data were later classified into three failure mechanisms: matrix cracking, delaminations and fiber breaks using the SOM. Compression test results showed that with 5 layers being repaired on the 20 layer specimen, up to 94.6% of the original compressive strength was recovered, and the SOM was able to classify the failure mechanisms from the repaired composite specimens.
Keywords: Acoustic emission (AE), composite failure mechanisms, compressive strength, Kohonen self-organizing map (SOM), neural network
37
S5-4 Wave Propagation Study of Lamb Wave Modes in CFRP Plates. Ono and Gallego.
WAVE PROPAGATION STUDY OF LAMB WAVE MODES IN CFRP PLATES
Kanji Ono1 and Antolino Gallego2
1Department of Materials Science and Engineering, University of California, Los Angeles (UCLA), Los Angeles, CA 90095 United States
2Department of Applied Physics, University of Granada, Spain
Abstract
The paper presents the recent results of an experimental actuator-sensor scheme for measuring separately the S0 and A0 Lamb modes of Carbon Fiber Reinforced Plastic (CFRP) plates with three different kind of laminate lay-ups; unidirectional [0]8s (and [90]8s), cross-ply [0, 90]4s, and quasi-isotropic [0,45,-45,90]2s. Several ultrasound transmitters were used as actuator and short-duration high-voltage pulses were used for excitation. Two kinds of AE resonant sensors with resonance frequency around 260 and 500 kHz, respectively, were used to record the Lamb waveforms of the plates. By using sensors on both sides of the plate, the confirmation and separation of both modes, S0 and A0, were successfully carried out. This result was also confirmed by using the Wavelet Transform of the recorded signals. The experiment was carried out at several actuator-sensor distances, so both phase and group velocities and attenuation could be obtained. By means an appropriate fitting of a theoretical model of attenuation, including the geometrical attenuation, the damping factor of both modes was obtained for each laminate and sensing frequency. The experimental results for velocities and damping factors were appropriately compared with previously published and theoretical ones. The results form the basis of an improved Acousto-Ultrasonics technique for aerospace CFRP components.
Keywords: Acoustic Emission, Acousto-Ultrasonics, Composites, Lamb waves, Attenuation
38
S5.5 Acoustic Emission Testing of a Curved Pultruded Rod Stitched Efficient Unitized Structure (PRSEUS) Under Combined Loading. Khanolkar et al. (student paper).
Acoustic Emission Working Group-54: Princeton, NJ, USA, May 21-22, 2012
1
Acoustic Emission Testing of a Curved Pultruded Rod Stitched Efficient Unitized Structure (PRSEUS) Under Combined Loading
Student: Amey Khanolkar1*
Advisors: Dr. Jonathan Awerbuch1, Dr. Tein-Min Tan1, Dr. John Bakuckas2 and Mr. Andrew Bergan1
1Department of Mechanical Engineering and Mechanics, Drexel University, Philadelphia, PA 19104 2Federal Aviation Administration William J. Hughes Technical Center, Atlantic City, NJ 08405
*Email Contact: [email protected], *Phone: (267) 694-2715
I. ABSTRACT An extensive test program was recently conducted to evaluate the damage-containment capability of a full-scale graphite/epoxy fuselage panel fabricated using the Pultruded Rod Stitched Efficient Unitized Structure (PRSEUS) concept. The tests were conducted at the Full-Scale Airframe Structural Test Evaluation and Research (FASTER) facility, located at the Federal Aviation Administration William J. Hughes Technical Center, Atlantic City International Airport, NJ, in partnership with NASA Langley Research Center and the Boeing Co. The fuselage-like PRSEUS panel was subjected to multiple quasi-static tests, consisting of different combinations of internal pressurization, hoop, and axial loading. Testing was conducted in three major independent steps: i) with the pristine panel; ii) after introducing a barely visible impact damage (BVID); and iii) finally, with a two-bay through-the-thickness notch. Deformation and damage were monitored via strain-gages, photogrammetry, LVDTs, visually (via interior and exterior high resolution cameras), and ultrasonic and thermography inspections. The experimental procedure and test results are reported in [1]. As part of this test program, the acoustic emission (AE) technique was usedto detect and locate damage initiation and monitor subsequent damage progressionin the vicinity of the notch-tips. This was a particularly challenging task because of the numerous sources of AE, namely: i) the all-composite skin; ii) the all-composite substructure (frames, stringers, etc.); iii) the skin/substructure interface; iv) and the hundreds of stitches connecting the skin to the substructure, all in addition to the extraneous emission generated by the FASTER loading fixture. AE was monitored using a 16-channel Physical Acoustics Corporation (PAC) system, employing 8 R15I and 8 WDI sensors. Detailed post-test AE data analyses were conducted to characterize the AE signals generated during loading. Special attention was given to distinguishing between the emission generated by damage growth and that generated by the loading fixture and by the fretting among the numerous newly formed fracture surfaces (including stitches pullout, disbonding, matrix cracking, etc.). Such emission normally overwhelms and obscures the emission generated from critical damage formation. The AE results were correlated with the extent of notch tip damage progression, measured visually. The results indicate that AE was able to detect damage initiation at early stages of loading, locate the notch tip damage site, and monitor damage progression. While the extraneous emission generated a large number of low-intensity AE signals, the high-intensity signals generated at high load-levels provided a good measure for anticipating incipient fracture. Keywords: Stitched composite structure, acoustic emission, damage-containment, tracking damage progression, stress waves.
II. EXPERIMENTAL PROCEDURE AND RESULTS 2.1 Test Panel Configuration and Sensor Layout The overall size of the PRSEUS fuselage panel was 127-in long and 75-in wide, with a 90-in radius. It contained seven full-length rod-stiffened stringers and five foam-cored frames; all stitched to the composite skinin the dry pre-form state, and then infused and cured with a VARTM process. Some of the details of the complex construction are shown in Figure 1. The 16 AE sensors were installed on the panel’s exterior surface in two concentric circles of radii 24-in and 28-in with the 7.8-in long notch at the center. The location of the 16 sensors was dictated by the substructure of the panel and space availability. Hot glue was used for sensor mounting and pencil-lead break calibration tests were performed prior to each test. A threshold of 40 dB was used for AE data collection. 2.2 Applied Loads and AE Activity The test program consisted of multiple loadings in a phased approach, first in pristine state, then with a BVID, and finally, with a through-the-thickness two-bay notch. In the final phase of testing, the loading sequence included four stages: i) internal pressure (10.6 psi); ii) combined pressure and axial load (9.2 psi and 227 kip); iii) axial load (227 kip); and iv) combined pressure and axial load to fracture (9.2 psi and 469 kip). This loading sequence, along with the amplitude of 3-hit and 5-hit events, is shown in Figure 2. A total of 155,137 R15I hits (and 161,353 WDI hits) were recorded, yielding 6,165 three-hit and 991 five-hit events with the R15I sensors (and 4,480 three-hit and 832 five-hit events with the WDI sensors). High amplitude events were generated when the axial load surpassed the previous maximum applied load, indicating emission from newly-formed damage. The medium amplitude events (50 -
Acoustic Emission Working Group-54: Princeton, NJ, USA, May 21-22, 2012
2
70 dB) recorded during the unloading of the hoop load, (while holding the axial load at 227 kip) suggest fretting emission from the newly formed fracture surfaces. High intensity events were recorded until the catastrophic fracture of the panel.
Figure 1. Photographs of the PRSEUS test panel showing (a) view of pre-test interior surface of the panel with substructure, (b) frame cross-section, (c) stringer cross-section (red lines indicate stitches), (d) post-test view of the exterior surface of the panel showing damage containment and subsequent catastrophic fracture, and (d) interior view of the damage originating at the notch-tips. The interior view has been mirrored to compare the interior damage surface to the exterior damage surface.
Figure 2. Combined axial and hoop load applied to the panel, along with the accumulation of (a) 3-hit events and (b) 5-hit events.
2.3 Tracking Damage Progression A good correlation was established between the observed notch-tip damage progression and the AE results. AE served as an early warning for damage initiation: high intensity 5-hit events (about 75 dB in amplitude), which are presumably associated with new damage, occurred at axial load of 119 kip, well before first indication of visible notch-tip damage (which occurred at a pressure of 9.2 psi and axial load of 158.9 kip). Subsequently, AE provided real-time tracking of notch-tip damage progressionas seenin thehistograms of 3-hit and 5-hit event and event energy in Figure 3. At high axial load-levels, several low energy events were located throughout the test section. These low-energy events are believed to have originated from fretting of newly formed fracture surfaces, reflection of stress waves from the panel edges and sources other than notch-tips damage. Thus, the high-intensity AE signals served as a good indicator of the extent of damage in the composite PRSEUS panel. Analysis of the waveform features, primarily event amplitude, energy, duration, counts, and frequency spectra, is being conducted to identify the intensity of generated emission, and discern the modes of damage in the panel. These results will be reported during the AEWG Meeting. The differences in the AE data recorded by the R15I and WDI sensors will be discussed as well.
(a) Test Panel Interior Surface
(d) Post-fracture exterior surface (e) Post-fracture interior surface
(b) Frame Cross-Section A-A
(c) Stringer Cross-Section B-B
Foam-Core
PultrudedRod
Damage Containment
Stitches
Failed Stitches
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Acoustic Emission Working Group-54: Princeton, NJ, USA, May 21-22, 2012
3
Figure 3.Tracking of damage progression by R15I sensors at four axial load levels showing: (a) The 3-hit event location plots; (b) 3-hit event energy histograms; (c) 5-hit event location plots; and (d) 5-hit event energy histograms (e). The red line-segment indicates the location of the notch. The high-energy events were associated with the opening of the notch-tips and their location correlated well with visual observations.
III. CONCLUSION AE was applied during the testing of a complex PRSEUS-made fuselage panel subjected to combined loading of axial, pressure, and hoop loading. Monitoring AE proved to be a non-destructive testing method that could provide real-time indication of damage initiation and progression and early warning of imminent catastrophic fracture. The identification of the dominant modes of damage, using the conventional event feature analyses, is underway.
IV. REFERENCES
[1] Bergan, A., Bakuckas, J., Lovejoy, A., Jegley, D., Neal, B., Linton, K., Korkosz, G., Awerbuch, J., and Tan, T.M. (2012), “Full-Scale Test and Analysis Results of a PRSEUS Fuselage Panel to Assess Damage-Containment Features.” 2012 Airworthiness and Sustainment Conference, April 2-5, 2012, Baltimore, MD.
I) 178 kip R15I 3-hit events
3D LocationR15I 3-hit event
energy histogram
II) 266 kip
III) 340 kip
IV) 469 kip
R15I 5-hit events 3D Location
R15I 5-hit event energy histogram
(a) (b) (c) (d)Axial Load
Eve
nts
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nts
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rgy
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ABSTRACTS
AEWG-54
May 21 – 22, 2012
PRINCETON, NJ
Session 6: AE in Civil and Geophysical Engineering Applications I
40
S6-1 AE in Saturated Rock under Plane Strain Compression. Makhnenko (student paper).
AE IN SATURATED ROCK UNDER PLANE STRAIN COMPRESSION
R.Y. Makhnenko and J.F. Labuz*
University of Minnesota Abstract The study of coupled effects of fluid flow and skeleton deformation in porous geomaterials is critical for proper assessment of an earth structure related to extraction of geothermal energy or geologic storage of carbon. In fact, the presence of pore fluids can affect both the elastic parameters of the rock and the deformation process. For example, fluid in the rock may facilitate or delay material failure depending on the local conditions, which can be drained or undrained. Under a drained condition, pore pressure is maintained at a constant level; for an undrained case, the fluid does leave the rock so pore pressure is changing. The influence of these conditions on behavior of porous rock (Berea sandstone) is investigated by plane strain compression experiments with AE monitoring. The fluid-saturated rock is placed within the University of Minnesota plane strain apparatus and subjected to a multi-axial state of stress with pore pressure control to achieve globally drained or undrained response. The device is internally instrumented with eight AE sensors for detecting microseismic activity during application of axial load (deviatoric stress).
A significant difference in AE activity was observed between drained and undrained compression. For the drained test, the number of AE events per increment of axial load increased when the major principal stress reached approximately 70% of peak stress, similar to the behavior of dry specimens. However, in an undrained test, only a few microseismic events were recorded prior to failure of the specimen. The abrupt change in the slope of the AE rate happened only when the pore pressure in the rock decreased. This effect could be explained by the delayed tendency of rock to dilate under an undrained condition, and the fact that an increase in specimen volume, even though the mean stress is compressive, is accompanied by intense microcracking and an increase in AE rate. Time histories from the eight sensors and measurements of P-wave velocity provide accurate AE locations in the fluid-saturated rock.
Keywords: AE locations, AE rate, plane strain compression, drained/undrained response * corresponding author, e-mail: [email protected], address: Department of Civil Engineering, 500 Pillsbury Dr. SE Minneapolis MN 55455, phone: 612-625-9060
41
S6-2 Damage assessment of civil engineering materials by means of transfer function on AE waveforms. Shiotani and Takada.
DAMAGE ASSESSMENT OF CIVIL ENGINEERING MATERIALS BY MEANS OF TRANSFER FUNCTION ON AE WAVEFORMS
Tomoki Shiotani1†*and Yuta Takada2
1 Graduate School of Business and Graduate School of Engineering, Kyoto University, Japan 2 MC Student, Graduate School of Engineering, Kyoto University, Kyoto University, Japan
†Speaker & corresponding author:
e-mail: [email protected]
address: C1-2-236, Katsura Campus, Kyoto University, Nishikyo-Ku, Kyoto 615-8540, Japan
phone: +81-75-383-3261, fax: +81-75-383-3264
Abstract
Our research group have so far been clarified that deterioration progress of civil engineering materials such as geotechnical materials and concrete materials can be assessed with several waveform features of acquired AE activity as well as statistical values derived from clusters of AE data. Improved b-value (Shiotani et al. 1994, 2001a), grade (Shiotani et al. 2001b), RTRI ratio (Luo et al. 2002), i-Calm and i-Load (Shiotani et al. 2008) are those promising AE indices alerting impending failure. When apply those indices to the assessment of in-situ infrastructures, there has still a step to be taken e.g., although the author has been reported that central or peak frequencies of AE waveforms are decreased uniquely as internal damage evolve (Shiotani et al. in print), the values of frequency are also dependent on such monitoring conditions as sensors and media transmitted as well as travel distance, suggesting the difficulty for in-situ applications. In order to solve this issue and to make those indices as practical, the transfer function has been introduced. In the presentation, some basic studies on the AE wave transfer function relating to the concrete incorporating some damage levels are discussed.
Keywords: AE waveforms, damage assessment, frequency features, transfer function
References:
Shiotani, T. et al. (1994) Progress in Acoustic Emission VII, 529-534. Shiotani, T. et al. (2001a) Journal of Acoustic Emission, Vol. 19, 118-133. Shiotani, T. et al. (2001b) Construction and Building Materials, 15, 5-6, 235-246. Luo, X. et al. (2002) Progress in Acoustic Emission XI, 205-212. Shiotani, T. et al. (2008) Journal of Acoustic Emission, Vol. 25, 69-79. Shiotani, T. et al. ASCE Journal of Bridge Engineering, in pint.
42
S6-3 Evaluation of Prestressed Concrete Structures with Acoustic Emission. An Overview of NIST TIP at the University of South Carolina. Larosche et al.
Evaluation of Prestressed Concrete Structures with Acoustic Emission
An Overview of NIST TIP at the University of South Carolina
Aaron Larosche* Jesé Mangual, Mohamed K. ElBatanouny,
William Vélez, Paul Ziehl, Fabio Matta
University of South Carolina, 300 Main Street, Columbia, SC, USA, 29208;
*email: [email protected]
*phone: 512-789-3947
Abstract
The University of South Carolina is currently conducting a number of investigations involving Acoustic Emission as an evaluation method of prestressed concrete elements. Included in these investigations is the detection of the internal degradation process as a result of corrosion. It has been found that the process of corrosion affecting a prestressing strand is substantially different from that of a passive reinforcing bar. Both small and medium scale investigations are in the process of completion in which acoustic emission is used to assess the presence and rate of corrosion. Acoustic Emission is capable of detecting the onset of corrosion prior to traditional electrochemical techniques while remaining noninvasive.
AE is being used in the evaluation of Cyclic Load Tests of medium scale prestressed concrete bridge girders. Tested specimens include specimens which were subjected to corrosion while being monitored with AE as well as control specimens used to evaluate the current CLT method as recommended by ACI committee 437. The University has also completed a project investigating the behavior of bridge substructure construction typical on the State of South Carolina. AE was used during simulated seismic loading of 10 full scale prestressed pile to bent-cap specimens as well as one full scale 3-pile specimen. The results of these tests are being used to develop damage classifications of these structures which may be used in the event of seismic activity.
Keywords: Prestressed Concrete, Corrosion, Cyclic Load Test, Seismic Evaluation
43
S6-4 Introducing Sifted b-Value Analysis and a New Crack Classification for Monitoring Reinforced Concrete Shear Walls by Acoustic Emission. Farhidzadeh and Salamone (STUDENT AWARD paper).
1
Introducing Sifted b-Value Analysis and a New Crack Classification for Monitoring Reinforced Concrete Shear Walls by Acoustic Emission
Alireza Farhidzadeh, Salvatore Salamone Smart Structures Research Laboratory (SSRL), Department of Civil, Structural, and Environmental Engineering,
State University of New York at Buffalo, 212 Ketter Hall, Buffalo, NY, 14260
INTRODUCTIONThe damage of Reinforced Concrete (RC) shear walls as the main gravity and lateral force resisting systems is a
major problem for engineers. Failure of a shear wall may sometimes deal with homeland security if they happen in nuclear power plants or concrete bridges in crowded metropolises. Acoustic emission (AE) technique has shown promising progress in recent decades. However, lack of research on AE monitoring of RC shear walls is recognizable in literature. Among various methods in AE parametric evaluation, b-value is being used to demonstrate the condition of a concrete specimen [1]. Typically, b-value increases during the nucleation of micro-cracks, become quite constant when micro-cracks merge to localize macro-cracks, and decreases when the macrocracks begin to open [2]. However, existence of lots of fluctuation in the b-value trend due to reflections, huge amount of cracks, reinforcing bars, attenuation, unloading, etc. lead to difficulties in executive decision making. This issue is aggravated when a large specimen is subjected to a reversed cyclic loading where the scattering effect is large and hits from loading and unloading alter b-value trend on and on. Systematic crack mode classification incorporated with b-value analysis can also be used as an indicator on the status of damage in shear or tensile crack mode. The problem in classical crack classification based on rise time, amplitude, duration and ring-down count is the proportionality of tensile to shear cracks [3]. This paper addresses these two problems by making advantage of statistical tools, Gaussian smoothing on b-values and k-mean clustering. These approaches are exploited to introduce a “Sifted b-value (Sb)” analysis in which after appropriate feature extraction of signals, they will be screened by statistical k-mean clustering and then will be send to b-value analysis. The Sb-value analysis can report the damage statues of both crack modes and predict failure mechanism to clarify required retrofitting procedure.
TEST SETUP The test specimens were two large scale RC shear walls, SW1 and SW2, with the thickness of 0.2m and a height
to width ratio of 0.94 and 0.54, respectively [4]. Figure 1 shows the experimental setup and dimensions along with AE instrumentation. The in plane quasi-static reversed cyclic loading protocol consisted of 10 load steps (LS) starting from LS1 to LS10 for SW1 and 10 load steps starting from LS2 to LS11 for SW2. Each load step consists of two cycles. The force-displacement hysteresis loops and their corresponding backbone in Figure 2 show that SW1 reached its maximum capacity at LS9 and onset of nonlinearity was LS7 while in SW2 these features had occurred in LS10 and LS8, respectively [4],[5]. The main components of the AE system included an eight-channel high-speed data acquisition board (Physical Acoustics Corporation Micro-II PAC) and AEwin software for signal processing and recording. SW1 was instrumented with eight R15AE sensors and SW2 with four R15and four R6 These piezoelectric resonance transducers transform transient elastic waves to electric waveforms which are digitized and stored by AE system. The sensors were attached to one face of the wall using hot glue. Preamplifiers were set at 40 dB gain and analog bandpass filters were adjusted in the interval of 20 kHz to 400 kHz. Trigger levels of 35 dB and 48 dB were respectively selected for SW1 and SW2 to remove background noise in the day of test.
Figure 1. Test setup, specimens dimension, instrumentation and sensor
layout: (a) Laboratory setup (b) SW1 (c) SW2
Figure 2. Hysteresis loops for (a) SW1 (b) SW2
GAUSSIAN FILTERING ON B-VALUE AND RESULTS
The b-value is obtained using the frequency–magnitude distribution data by means of Gutenberg–Richter
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relationship, which is generally used in seismology. The Gutenberg–Richter formula in terms of AE technique is as follow:
logN = a – b(AdB /20) (Eq. 1)
where N is the incremental frequency (i.e. the number of AE events with amplitude greater than the threshold), a is an empirical constant, b is the b-value, and AdB is the peak amplitude of AE event in decibel. Groups of 70 hits were selected to start the analysis. Based on the highly fluctuated result of b-value, no decision could be possibly make. A typical solution is uniform moving average but in order to keep the local b-value drop during loadings, our proposed workaround is the Gaussian smoothing. Gaussian functions have the following properties that make them particularly useful in smoothing filters [6]: (1) The Gaussian function is symmetric about the mean, and the weights assigned to signal values decrease gradually with distance from the mean. (2) The width of the Gaussian function is determined by its spread parameter, i.e., the standard deviation. As the standard deviation decreases, Gaussian function does less smoothing. Conversely as the spread parameter increases, the amount of smoothing is increased. (3) The local extrema (e.g. b-value drop) observed at one standard deviation are also observable at the smaller standard deviations and no more local extrema are created as the spread parameter increases. Gaussian smoothing is literally the convolution of a Gaussian window and a 1-D vector of data. The Gaussian smoothing F(x) of a 1-Dimentional signal, f(x), is defined as:
퐹(푥) = 푓(푥) ∗ 푔(푥,휎) = 푓(휇)푔(푥 − 휇,휎)푑휇 = 푓(휇)1
√2휋휎푒푥푝 −
(푥 − 휇)2휎 푑휇 (Eq. 2)
where "*" denotes convolution with respect to x, g(x,σ) is the Gaussian function with the standard deviation σ, and μ is a dummy variable. These filtering parameters in addition to window span should be properly selected to clarify the trend. The results of raw b-value and smoothed b-value are illustrated in Figure 3 and Figure 4. These results confirm that micro-cracks were growing prior to LS3 and were localized to form macro-crack prior to LS5. From this load step onward, the macro-cracks opening is observed that was in good accordance with visual inspection.
Figure 3. Raw b-values before being processed (a) SW1; (b) SW2
Figure 4. Smoothed b-values for: (a) SW1 (b) SW2
NEW CRACK CLASSIFICATION AND RESULTS
Crack classification based on Japan Construction Material Standards JCMS-IIIB5706 [7] employs two parameters of the Average Frequency and the RA value as follow:
RA = (Rise time) / (Peak Amplitude) (Eq. 3) Average Frequency = (AE ring-down count) / Duration (Eq. 4)
Rise time, peak amplitude, and duration in Eq. 3 and Eq. 4 are depicted in Figure 5a. In this figure, ring-down count is shown by solid circles in intersection of threshold and AE signal. Cracks will be categorized as Figure 5b illustrates. However, a defined criterion (the inclined line) on the proportion of the RA value and the average frequency for crack classification has not been confirmed [3]. In the present work, the widely-used k-means pattern recognition method is utilized to classify the AE events in two dominant shear and tensile groups. This method aims at partitioning the data sets into k disjoint subsets (clusters) based on minimizing the summation of square distance of each data point in a subset to the center of the subset in which it is partitioned [8]. Indeed, several studies based on pattern recognition of AE events have determined that it is feasible to distinguish efficiently the events associated with various failure mechanisms. Suppose that a data set X={x1, x2, …, xN}, xn∈Rd, is available. k-means aims at partitioning this data sets into k disjoint subsets, C1, …, Ck, through minimizing the criterion or clustering error as follow [8]:
퐸(푚 ,푚 , … ,푚 ) = ∑ ∑ 퐼 푥 ∈ 퐶 푥 − 푚 (Eq. 5)
where mj is the center of jth cluster, Cj, and I(P)=1 if P is true and 0 otherwise. This criterion can be minimized
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through iterative procedure summarized in the following steps: (1) Initializing k centers,{m1,…, mk}, through randomly dividing the data set into k groups and calculating the mean value for each subset. (2) Assigning each data point, xn, to a cluster, Cj , that its center, mj, has the lowest Euclidean distance to the point among other cluster centers. (3) Re-computing the centers for k clusters, {m1,…, mk}. (4) If the change in centers in two proceeding steps is less than a certain threshold then algorithm is converged and terminate the iteration; otherwise go to step 2.
Figure 5. (a) AE parameters in an AE signal, (b) Signal
Classification [7]
Figure 6. (a) example of classification (b) number of
AE hits associated to tensile or shear mode
The systematic crack classification is done on SW1 that both modes were visibly occurred. The result of k-mean clustering and an example plot that shows how these cracks are classified at LS6 of SW1 are manifested in Figure 6. The dominancy of tensile cracks in the initial load steps and superiority of shear mode cracks from LS5 onward is evident is this figure. The length of tensile and shear mode cracks in each mode verify the outcomes. Finalizing the process by performing sifted b-value analysis lead to having knowledge about each crack mode, see Figure 7. The criticality of tensile cracks due to their monotonic reduction is elaborated in the results of Sb-value analysis. To verify this prediction, SW1 was pulled toward complete collapse. Figure 8 proves the flexural (tensile) failure mode.
Figure 7. Sifted b-value analysis results: (a) Tensile
cracks (b) Shear cracks
Figure 8. Tensile mode as the failure mode of SW1 in
LS11, monotonic pulling to collapse
CONCLUSION Reinforce concrete shear walls are among large structural elements that their acoustic emission monitoring is not straightforward due to large scattering effect. Gaussian smoothing is a powerful filter for clarifying the trend of b-value in order to monitor the transition of micro-cracks to macro-cracks. To monitor the majority of a crack mode in a shear wall, new crack classification based on k-mean analysis on RA and AF parameters resulted in promising outcomes. Sifted b-value (Sb) analysis can reveal the severity of damage in each crack mode to predict the failure mechanism and helps to select appropriate retrofitting scenario.
REFERENCES [1] M. V. M. S. Rao and K. J. P. Lakshmi, “Analysis of b-value and improved b-value of acoustic emissions accompanying rock
fracture,” Current Science, vol. 89, no. 9, pp. 1577-1582, 2005. [2] I. S. Colombo, I. G. Main, and M. C. Forde, “Assessing Damage of Reinforced Concrete Beam Using ‘“ b -value ”’
Analysis of Acoustic Emission Signals,” Managing, no. June, pp. 280-286, 2003. [3] K. Ohno and M. Ohtsu, “Crack classification in concrete based on acoustic emission,” Construction and Building Materials,
vol. 24, no. 12, pp. 2339-2346, Dec. 2010. [4] J. F. Rocks, “Large Scale Testing of Low aspect Ratio Reinforced Concrete Walls,” M.Sc. Thesis, Department of Civil,
Structural and Environmental Engineering, University at Buffalo, NY, 2012. [5] A. Farhidzadeh, S. Salamone, B. Luna, and A. Whittaker, “Damage Assessment of a Reinforced Concrete Shear Wall by b-
value based Outlier Analysis,” Structural Health Monitoring, vol. 2012. [6] H.-C. Lin, L.-L. Wang, and S.-N. Yang, “Automatic determination of the spread parameter in Gaussian smoothing,” Pattern
Recognition Letters, vol. 17, no. 12, pp. 1247-1252, Oct. 1996. [7] Japan Construction Material Standards JCMS-IIIB5706. Monitoring Method for Active Cracks in Concrete by Acoustic
Emission. Japan: The Federation of Construction Material Industries. 2003. [8] A. Likas, N. Vlassis, and J. J. Verbeek, “The global k-means clustering algorithm,” Pattern Recognition, vol. 36, no. 2, pp.
451-461, Feb. 2003.
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44
S6-5 Acoustic Emission Studies on a Highway Bridge Crossing Over Freight Rail Tracks. Parmar et al.
Acoustic Emission Studies on a Highway Bridge Crossing Over Freight Rail Tracks
Devendra S Parmar*1, Stephen R. Sharp2, Terry Temutus3, and Richard Gostautas4
1Department of Electrical Engineering
Hampton University, Hampton, VA 23668, USA
(757) 728-6874; fax (757) 727-5189; e-mail [email protected]
2Virginia Center for Transportation Innovation and Research (VCTIR)
530 Edgemont Road, Charlottesville, VA 22903, USA
(434) 293-1913; fax (434) 293-1990; e-mail [email protected]
3 Mistras Group, Princeton Junction, NJ 08550
(609) 468-5737; fax: (609) 716-0706; email: [email protected]
4 Mistras Group, Princeton Junction, NJ 08550
(609) 716-4120; fax: (609) 716-0706; email: [email protected]
*Presenting Author
Abstract
Acoustic Emission (AE) investigations have been conducted on the concrete back wall of a bridge on a highway crossing over a freight rail line that serves a cargo terminal. The study has three basic objectives: (i) to detect the presence of micro cracks, (ii) to obtain as much information as possible on their locations; and (iii) to determine the contribution of freight traffic on the rail road that the bridge crosses in proximity of the test back wall. AE signals originating from the activity related with cracks were recorded with the help of 8 AE sensors strategically installed for the purpose at known locations on the wall. The AE data was recorded with the help of an 8 channel digital data acquisition (Micro-II Digital TM) system designed for on-site real time analysis. AE data analysis detected activity due both to the pre-existing and newly generated cracks. 2D and 3D locator software has been used to determine dynamics of crack activity. AE contributions from traffic on the road and freight traffic on rail road have been quantified for different test locations. The results of AE activity due both the contributions will be discussed. AE monitoring did give time and location of AE activity. The results lead to the conclusion that both the road and the rail road traffic volumes contribute to dynamic and quantifiable AE activity.
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S6-6 Quantitative Acoustic Emission Monitoring of Reinforced Concrete Structures Using High-Fidelity Point Contact Sensors. Mhamdi and Schumacher.
Quantitative Acoustic Emission Monitoring of Reinforced Concrete Structures
Using High-Fidelity Point-Contact Sensors
Lassaad Mhamdi 1 and Thomas Schumacher 2
1PhD Student, University of Delaware
1Assistant Professor, University of Delaware
Abstract:
A need exists for Structural Health Monitoring (SHM) approaches that are capable of producing quantitative feedback on fracture mechanisms in real-time. In this line, we present a methodology that combines some of the central concepts and main features used in seismology with the enabling capabilities of the Acoustic Emission (AE) technique in order to gain insight in ongoing fracture processes of reinforced concrete structures. The methodology we present is based on Moment Tensor Inversion (MTI), a key analysis tool used in seismology to infer on the physics of the source of fracture and its properties. The hybrid MTI technique we employ is simple in concept but requires accurate input data. For that reason, all the experiments we conducted in order to test the methodology were performed using high-fidelity Glaser/NIST-type point-contact sensors that have a broad-band response between 20 kHz and 1 MHz. The experiments consisted of creating artificial AE sources at different locations on 1 x 2 x 3 ft unreinforced and reinforced concrete specimens and collecting AE signals using the point-contact sensors mounted on the specimens. Here we present a general description of the proposed methodology with emphasis on the hybrid MTI technique. We also provide a description of the experiments conducted in the lab and the new sensors we used for data collection along with a discussion of capabilities and limitations.
Keywords: Acoustic Emission, Moment Tensor Inversion, Concrete, Point-contact sensor
Corresponding author: Lassaad Mhamdi Department of Civil and Environmental Engineering University of Delaware 301 DuPont Hall Newark, DE 19716 Phone: 302-831-2442 E-mail: [email protected]
46
ABSTRACTS
AEWG-54
May 21 – 22, 2012
PRINCETON, NJ
Session 7: AE in Civil and Geophysical Engineering Applications II
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S7-1 Hydraulic Fracture Characterization and Location in Scale Model Tests Using Acoustic Emission. Hampton et al. (student paper).
Hydraulic Fracture Characterization and Location in Scale Model Tests Using Acoustic Emission
Jesse Hampton, Luke Frash and Marte Gutierrez Department of Civil and Environmental Engineering
Colorado School of Mines Golden, CO 80401
Extended Abstract: Enhanced Geothermal Systems (EGS) requires the use of unconventional reservoir stimulation to properly acquire the heat transfer needed for geothermal energy production. Throughout reservoir stimulation, Acoustic Emission (AE) monitoring is commonly used to monitor and characterize the geometry of the hydraulic fracture and the fracture processes. EGS reservoirs consist of brittle heterogeneous Hot Dry Rock (HDR). Hydraulic fractures in brittle heterogeneous HDRs produce multiple acoustic emission signals from an irreversible release in stress energy during fracture propagation. Minimally confined samples of blocks of analogue rocks with single boreholes drilled through the center of the block were hydraulically fractured in scaled model laboratory tests to simulate EGS reservoir creation processes. Model test sample size was approximately 30x30x30 cm3. Six AE piezoelectric transducers manufactured by Physical Acoustics Corporation (PAC) with an operating range of 100 to 900 kHz were used and installed on the surface of the blocks via machined steel platens. The samples and platens are loaded inside a true-triaxial load cell as shown in Figure 1. Figure 2 shows machined steel platens used to apply the confining stresses on the faces of the cubic sample, two of the six AE sensors, and one of the preamplifiers. Low confining stresses will be used to keep platens and sensors in full contact with sample without applying significant internal stresses inside of each sample.
Figure 1. True-triaxial loading cell. Figure 2. Machined steel loading platens, two
sensors, and preamplifier.
48
Specific orientations and placement of sensors were chosen in order to minimize error and encompass, detect and characterize the entire fracture process and geometry. PAC’s AE source location software, AEwin, was used in order to triangulate event locations and perform waveform analysis. Three-dimensional location using multiple regression analysis was used with the six sensors in order to reduce error associated with typical four-sensor arrays. Pre-fracture sample characterization was performed using Auto-Sensor Tests (AST) and Pencil Lead Break (PLB) tests. PLBs were performed on each sample outside of the load cell without the use of steel platens due to access restrictions associated with placing sample and platens inside cell. ASTs create pulses emanating from each sensor on the structure. The pulsing sensor and the remaining sensors all receive the transmitted signal, and with a perfect arrival time to for each signal, highly accurate wave velocities were determined. The velocity structure of each sample was also characterized. With information gained from multiple ASTs and PLBs, and locations of sensors, attenuation curves for each sample were established. Each block sample was hydraulically fractured from a borehole using Teledyne Isco Syringe pumps. Multiple fracture wings were observed with AE source location during each tensile fracture test as shown in Figure 3. Fracture wing direction was also observed. Numerous stages of fracturing were performed in order to observe fracture extension and fracture closure. Stimulated Reservoir Volumes (SRV) were calculated from pressure and flow data and compared with the SRV associated with AE event cloud volume. Using pressure data, flow data, AE locations, magnitude of events, and signal strength, a confined SRV was calculated. Stress communication between fractured wells in the same block sample was observed. Waveform analysis was performed to show frequency characteristics of hydraulic fractures. Post-fracture ASTs and PLBs were performed in order to investigate a change from isotropic wave velocity to anisotropic wave velocity behavior in each sample due to the presence of fracture networks. AE source locations and the respective induced fracture geometry were verified visually by coring and cutting each block sample into thin slices, shown in Figures 4 and 5.
49
Figure 3. Approximately symmetric fracture wings developed after initial stage hydraulic fracturing (distance units are in millimeters). Color scale shows amplitude distribution for the acoustic emission
events.
Figure 4. Slicing samples with diamond table saw.
Figure 5. Internal slice of a fractured sample.
Main fracture stenciled for clearer view
50
S7-2 Using Acoustic Emission to Detect and Quantify Alkali Silica Reaction in Concrete. Pour-Ghaz et al.
Corresponding (current address): Mohammad Pour-Ghaz, North Carolina State University, Department of Civil, Construction, and Environmental Engineering, Campus Box 7908, 431 C Mann Hall, Raleigh, NC 27695-7908, 919.515.2235 (phone), 919.515.7908 (fax), [email protected]
Using Acoustic Emission to Detect and Quantify Alkali Silica Reaction in Concrete M. Pour-Ghaz1*, J. Weiss2
School of Civil Engineering, Purdue University, West Lafayette, IN, 47907 1Graduate Research Assistant, 2Professor of Civil Engineering
1. Introduction The present paper describes results of an experimental investigation that uses acoustic emission (AE) as a rapid method of assessment of reactivity and expansion potential of concrete due to alkali silica reaction (ASR). Proof of concept experiments on model systems (a two-phase composite made of Acetal aggregate inclusions and cement paste matrix) are performed followed by experimental investigation on reactive aggregate mortars. The results of this investigation indicate that AE can be used as a rapid method of assessment of potential for alkali reactivity.
2. Background The alkali-silica reaction is a chemical reaction that occurs when alkalis (coming primarily from the cement) react with certain amorphous silica components (coming primarily from the aggregates). This chemical reaction results in formation of an alkali-silica gel, which expands in the presence of water. This expansion can result in micro-cracking of the aggregates which is followed by cracking at the aggregate-matrix interface and in cement paste matrix. ASR is a major durability problem affecting US concrete infrastructure. A 1994 report of federal highway administration (FHWA) indicated that more than 36 States in the US were affected by ASR. Lake Townsend Dam which was the primary water supply for the City of Greensboro, North Carolina, is replaced due to ASR deterioration that severely compromised its hydraulic capacity. In 2006, 32 of the airfields owned by the Department of Defense (DOD) were suffering from ASR. The Air National Guard Channel Islands Site was replaced 14 years after construction at a cost of $16M. Testing for ASR reactivity before construction is enforced by owners of all major constructions projects, however, timely detection of potential for ASR reactivity remains a major challenge. Length change measurements are traditionally used for assessing the potential for reactivity and expansion due to ASR. Length change measurements are time consuming. Obtaining reliable results from ASTM C1293 can take more than a year. Accelerated length change measurement methods (such as ASTM C 1260) may not always yield reliable results: concrete mixtures that do not show alkali reactivity in the accelerated tests has been observed to suffer from ASR in the field! Traditional methods also many not provide understanding of the mechanism of ASR deterioration as they cannot be used to differentiate between the sources of cracking. Therefore there is a need for developing a rapid and reliable test method to assess potential for ASR.
3. Research Significance The present work utilizes AE as a method for rapid assessment of potential for alkali reactivity and expansion of concrete mixtures. This research is built on the hypothesis that cracking precedes the substantial portion of the volume change due to ASR. If the acoustic activity can be detected before length change occurs, it will be faster than current length change measurements. It may also be able to be used to separate different damage mechanisms and stages of ASR (such as aggregate and matrix cracking). Therefore using AE may improve our understanding of ASR damage formation. The use of AE as a rapid method of detection and quantifying ASR reactivity has the potential for standardization.
4. Proof of concept (Pour-Ghaz and Weiss 2010) The expansion of aggregates in cement-based composites is physically simulated using temperature changes. This model system consists of polymeric inclusions with a high coefficient of thermal expansion (COTE) in the cement paste matrix which has a relatively low COTE. Using this physical simulation, the extent of damage due to the thermal loading of the composite can be controlled and measured.
4.1. Experimental methods A composite with spherical polymeric (acetal) inclusions in a cement paste (water-to-cement ratio of 0.42) was used. The volume fraction of the inclusions was 33%. Two different inclusion sizes were used – 3 mm (≈ 1/9 in.) and 5 mm (≈ 1/5 in.) diameter. Five cylindrical samples were prepared with 2 cm (7/9 in.) diameter and 4 cm (1 5/9 in.) height. AE sensors were installed on the samples and the samples were placed in the temperature
controlled chamber. An additional sensor was installed on a cylindrical piece of aluminum which was used as a reference with similar dimensions and placed in the chamber along with the composites. Two different thermal loading histories were applied.
4.2. Results Figure 1 illustrates the results of AE test with the first thermal loading pattern. The acoustic energy increases linearly for both inclusions sizes after a temperature rise of 3 to 4oC (5.4 to 7.2oF) occurs. The composite with small inclusions shows higher cumulative acoustic energy. This occurs since the captured acoustic energy is related to an equal volume of the composite and a larger number of inclusions is present in the composite with the smaller inclusions size therefore more cracking is expected in composite with smaller inclusions. Aluminum does not show any acoustic activity which suggests that no acoustic energy is measured in this setup from any external source. The acoustic activity does not increase due to the decrease in the temperature suggesting that no damage is introduced in the material due to decrease in the temperature. Figure 2 illustrates the results of AE test with the second thermal loading history. No acoustic activity is observed in the aluminum sample. An increase in acoustic activity is observed in the composites with increase of the temperature in the first cycle. In the second cycle no additional acoustic activity is observed. In the third cycle, when the temperature is increased beyond the previous step acoustic activity is observed. This behavior is typical for brittle materials and is referred to as the Kaiser effect (Scott, 1991). The cyclic loading and monitoring the AE energy provides a method to obtain information about the preexisting damage in the material.
5. Using acoustic emission to detect and quantify damage due to alkali silica reaction (Pour-Ghaz et al. 2012)
Experimental study on mortar specimens was performed. Continuous length change measurements and AE were measured on the same sample. An aggregate that was categorized as reactive by multiple laboratories across the US was used in this experimental program.
5.1. Experimental method Two 14.0 inch (35.56 cm) tall and 4.0 inch (10.2 cm) in diameter cylindrical mortar specimens were tested. Ordinary portland cement (Type I) with a w/c of 0.47 and 55% reactive fine aggregate by volume was used. Two stainless steel rods (with 1.0 inch (2.54 cm) diameter) were used as waveguides to avoid contact between sensor and alkali solution. Another stainless steel rod (with 1.5 inch (3.81 cm) diameter) was used as LVDT rest-point. The sample was placed in 1N NaOH (according to ASTM C 1260) and experiments were performed at 38o+1oC (100+1.8oF). Simultaneous AE and length change measurements were performed. Figure 3 schematically illustrates the experimental setup.
5.2. Results and discussion Figure 4 illustrates the results of simultaneous length change and acoustic measurements. A sudden rise in acoustic energy is observed after 5 days. This increase in acoustic energy is not accompanied with length change. Based on previous findings of other researchers (Adler, 2008), it is hypothesized that this increase in AE activity is due to cracking within the aggregates. The rate of acoustic activity decreases between 5 to 12
Figure 1: Results of acoustic emission test with triangular thermal loading
Figure 2: Results of acoustic emission test with cyclic loading
days. It is speculated that this decrease is related to the period during which not cracking occurs and only formation and deposition of gel in existing cracks occurs. After 11-12 days the length change begins. An appreciable increase in the rate of acoustic activity is also observed. It is expected that the cracking that is occurring during this period is likely caused by expansion of gel that is deposited in the previously formed cracks and around the aggregates resulting in interface and matrix cracking. After approximately 20 days the rate of acoustic activity decreases which might be caused by one or combination of the following factors: (1) The formation of new cracks that has resulted in opening of more space for ASR gel deposition and therefore reduction in cracking, (2) attenuation of elastic stress waves due to formation of new cracks inside the sample, (3) formation of cracks and gel at the interface of the waveguides and sample, (4) a combination of these effects. After the second increase in the acoustic energy, the specimen has only expanded approximately 0.06% which is far below the criteria used in ASTM C 1260. While more research is needed, the data clearly indicates that AE has the potential to be used as a rapid method of assessment of susceptibility of aggregates to ASR. Further work is needed to examine the potential benefits and short comings of the AE measurement approach when applied to different systems and to fully relate the acoustic signals to meso-scale and microstructural behavior.
6. Conclusion The experimental results indicate that acoustic emission has the potential to be used as a rapid method of assessment of susceptibility of aggregates to ASR and can also be utilized to obtain information about the history of the sample and level of damage in the field applications. It was shown that using acoustic emission cracking due to ASR can be captured as early as five days after exposure of material to NaOH while the 0.1% expansion was detected after 18 to 20 days. In the present experimental program, the ASR testing was accelerated and a highly reactive aggregate was used. When lower reactivity aggregate is used the time of reach to 1% expansion will increase significantly and therefore using acoustic emission can significantly improve the diagnostic time.
7. References Adler I. Personal Communication, 2008. West Lafayette, IN. Pour-Ghaz M, Weiss J. Quantifying damage due to aggregate expansion in cement matrix. in: Ideker JH, Radlinska A, editors. Advances in the Material Science of Concrete. ACI Special Publication. Chicago, IL; 2010. SP-270, p. 101-114. Pour-Ghaz M, Spragg R, Castro J, Weiss J. Can acoustic emission be used to detect alkali silica reaction earlier than length change tests? 14th International Conference on Alkali-Aggregate Reaction, Austin, TX, May 20-25, 2012. Scott IG. Basic Acoustic Emission. Nondestructive Testing Monographs and Tracts, 6. 1991. Gordon and Breach Science, New York.
Figure 3: experimental setup for detecting ASR using acoustic emission
Figure 4: Simultaneous length change and acoustic measurements on ASR specimen
51
S7.3 Investigation of Fracture Energy and Fracture Process Zone in Concrete under Three-point Bending by Acoustic Emission. Ohno at al.
Investigation of Fracture Energy and Fracture Process Zone in Concrete under Three-point Bending by Acoustic Emission
Kentaro OHNO1), Kimitaka UJI1), Atsushi UENO1) and Ayano Nakashima1)
Dept. of Civil and Environmental Engineering, Tokyo Metropolitan University, Japan
1-1 Minamiosawa, Hachioji, Tokyo, 192-0397, Japan
E-mail: [email protected], Phone: +81-42-677-1111
Abstract
The size effect on the fracture of concrete has been widely studied. Fracture energy is effective parameter for expression of the size effect in concrete. The methods of experimental determination of fracture energy in concrete are suggested by RILEM and JCI (Japan Concrete Institute). These methods are carried out to measure applied load and crack mouth opening displacement (CMOD) under three-point bending of notched concrete beam. However, these methods contain the size effect.
Fracture energy releases during crack propagation, micro-cracks create at around the crack tip. This area is called fracture process zone. Fracture energy associates with development of fracture process zone. The formation and growth of micro-cracks in concrete are associated with the release of strain energy. Therefore, the area of fracture process zone can estimate by acoustic emission (AE) method.
In this study, AE method is applied to three-point bending of notched concrete beam to investigate relation between fracture energy and area of fracture process zone. Dimensions of concrete specimen are 100 x 100 x 400 mm with different notches of 30, 50, 70 and 80 mm. These notches are located at center of the bending span. Specimens contain different maximum aggregate sizes of 5, 10 and 20 mm to investigate difference of material property in concrete for fracture energy and area of fracture process zone.
As a result, fracture energy in concrete varies with specimen geometry (notch length) and maximum aggregate size in concrete. Then, fracture process zone is estimated based on locations of AE events with high energy. It is realized that fracture energy and area of fracture process zone are influenced by boundary or back surface of specimen. In addition fracture energy becomes low with increasing notch length, area of fracture process zone in ligament decreases at near the boundary of specimen.
Keywords Acoustic Emission, Concrete, Fracture Energy, AE Energy, SiGMA analysis,
52
S7-4 Fracture Mechanics of Concrete by AE-SiGMA Analysis. Ohtsu and Mondoringin.
Fracture Mechanics of Concrete by AE-SiGMA Analysis
Masayasu Ohtsu1 and M. R. I. A. J. Mondoringin2
1 Professor, Graduate School of Science & Technology, Kumamoto University,
Kurokami 2-39-1, Chuo-ku, Kumamoto 860-8555, Japan
Phone +81-96-342-3542, Fax. +81-96-342-3507, e-mail: [email protected]
2 Graduate Student, Graduate School of Science & Technology, Kumamoto University
Abstract
In order to study fracture mechanics of concrete, mechanisms of tensile fracture at the meso-scale is identified by applying the SiGMA (Simplified Green's functions for Moment tensor Analysis) to concrete samples. It is found that the crack classification of the tensile crack and the shear crack in the SiGMA analysis is different from the mode I and the mode II cracks in fracture mechanics. The crack classification by the moment tensor is based on crack displacements (motions) on the crack surface at the meso-scale, while the modes in fracture mechanics represent crack propagation from the crack-tip at the macro-scale. It is found that the extension of the tensile cracks at the macro-scale is dominantly governed by mode I failure, even though all the types of tensile, mixed-mode and shear cracks are observed at the meso-scale in the SiGMA analysis. Further it is clarified that the facture process zone is created in a cross-section under constant tensile stress in the split-tensile test and the direct tensile test in concrete. As a result, similar nucleation processes of the facture process zone could reasonably lead to comparable tensile strengths in the both tests.
Keywords: Moment tensor analysis, Fracture mechanics of concrete, Fracture process zone, split-tensile test, tensile strength
53
S7-5 Fracture Mechanisms of Corrosion-Induced Cracks in Reinforced Concrete by SiGMA and BEM. Kawasaki et al. (STUDENT AWARD paper).
Application to Student Award in AEWG-54 (Extended Summary) TITLE: Fracture Mechanisms of Corrosion-Induced Cracks in Reinforced Concrete by SiGMA and BEM Yuma Kawasaki1), Takahisa Okamoto2), Kazuyuki Izuno2) and Masayasu Ohtsu3) 1) Applicant, Ph. D Student,
2) Co-author, Professor, Ritsumeikan University, Noro-Higashi1-1-1, Kusatsu, Shiga 525-8577, Japan 3) Co-author, Professor, Graduate School of Science and Technology, Kumamoto University, 2-39-1, Kurokami, Kumamoto
860-8555, Japan
Abstract
Nucleation of corrosion-induced cracks in concrete can be detected and analyzed by SiGMA (Simplified Green’s functions
for Moment tensor Analysis) of AE, by which crack kinematics of locations, types and orientations are quantitatively identified.
The procedure was applied to clarify fracture mechanisms of corrosion-induced cracks in reinforced concrete. In order to
compare with cracking mechanisms identified, a stress analysis by the boundary element method (BEM) was conducted.
Crack locations and orientations identified by SiGMA are in remarkable agreement with results of the BEM analysis. It is
demonstrated that fracture mechanisms of the corrosion-induced cracks are practically clarified by SiGMA.
1. Introduction
One of the critical deteriorations in reinforced concrete is corrosion-induced cracks due to expansion of corrosion products
in reinforcing-steel bars (rebars). For making decision on maintenance and repair, kinematical information of fracture
mechanisms in concrete due to corrosion of rebar is significantly important. Expansion caused by corrosion products
generates micro-cracks in concrete, of which mechanisms can be investigated experimentally by acoustic emission (AE) [1]. In
order to study fracture mechanisms of corrosion-induced cracks, SiGMA (Simplified Green’s functions for Moment tensor
Analysis) [2], [3] is applied to the corrosion process in reinforced concrete. To compare with cracking mechanisms identified
by SiGMA, a numerical analysis by the two-dimensional boundary element method (BEM) is performed for stress analysis.
Thus, fracture mechanisms of corrosion-induced cracks are quantitatively evaluated by SiGMA and BEM.
2. Experiment and BEM model
A sketch of a reinforced concrete (RC) beam tested is shown in Fig.1. In a beam of dimensions 400 mm x 100 mm x 75
mm, one deformed rebar of nominal 13 mm diameter was embedded with 20 mm cover-thickness and the maximum gravel
size of concrete was 10 mm. To promote the corrosion, a notch of dimensions of 150 mm x 10 mm x 1 mm was set at the
bottom of the mold. The compressive strength of concrete after 28-day water curing was 36.0 MPa. Beams were cyclically
placed into a container filled with 3 % NaCl solutions for a week and subsequently taken out of the solution to dry ambient
Rebar
x 100 150
400 150
13
20 [mm]
75 y
z
100 50
AE sensors
position
Concrete 1CH
2CH
6CH
5CH 4CH
3CH
x
y
z
Fig. 1 Sketch of RC specimen tested. Fig. 2 Set of AE sensors.
y Notch
temperature for another week. In one beam, AE measurement was
continuously conducted using an AE measuring system (DiSP, PAC). Six AE
sensors (R15, PAC) of 150 kHz resonance were attached to the surface of the
beam as shown in Fig. 1. The frequency range of the measurement was 10
kHz to 2 MHz. AE signals were amplified with 40 dB gain in a pre-amplifier
and 20 dB gain in a main amplifier and the threshold level was set to 40 dB
gain.
A BEM model is illustrated in Fig. 3, which corresponds to a half portion
of a cross-section in RC beam. The boundary corresponding to the notch is
set to be stress free from constraints as shown. Expansive pressure due to
corrosion products was applied at the cavity, corresponding to rebar. The
pressure was assumed as 1 MPa and applied boundary nodes were determined
from a diffusion analysis of chloride contents by the finite element method
(FEM). This implies that as a simulation analysis of the corrosion process,
corroded area in rebar increases with the increase in penetration of chloride
ions in concrete.
3. Results and discussion
Results of FEM of a half portion of a cross-section at 42 days are
shown in Fig. 4. It was confirmed that chloride contents exceed 1.2kg/m3 at
-33.75° orientation. Thus, expansive pressure (1 MPa) was applied to 5 points
as shown in Fig. 3.
Results of the BEM at 42 days are shown in Fig. 5. In the case of the
horizontal pressure applied in Fig. 5 (a), the highest stress and the second
highest stress are observed toward -78.75° and -45° orientations, respectively. It may suggest the case that the spalling cracks
propagates following the surface cracks. The spalling cracks are often observed in the case that the surface cracks are
arrested by aggregate. In the case of the vertical pressure applied in Fig. 5 (b), the highest circumferential stress is only
observed toward -78.75° orientation, suggesting nucleation of the surface crack. These results imply that the radial
pressure due to the corrosion products could generate the surface crack. The possibility to generate the spalling crack might
be stimulated by the horizontal pressure due to expansion of the corrosion products.
Fig. 3 BEM model.
+α -α
Notch
50 100
0~1.2
Fig. 4 Results of FEM analysis at 42 days.
x (mm)
2.4~3.6 1.2~2.4
Salinity (kg/m3) 3.6~
Rebar
Applied pressure
Fig. 5 (a) Results of BEM (Horizontal pressure)
Angle (degree)
-45 -22.5 0 22.5 67.5 45 90
0.6
0.2
0.0
0.8
-90 -67.5
Circ
umfe
rent
ial s
tress
(MPa
)
Fig. 5 (b) Results of BEM (Vertical pressure)
Angle (degree)
-45 -22.5 0 22.5 67.5 45 90
0.4
0.2
0.0
0.6
-90 -67.5
Circ
umfe
rent
ial s
tress
(MPa
)
0.4
-78.75
-45
78.75
Results of the SiGMA analysis at 42 days elapsed are shown in Fig. 6. Mostly events are located surrounding the rebar.
The events which are located at -78.75° orientation are mostly classified into tensile (opening) cracks. Additionally, shear
cracks (sliding) are observed at 78.75° orientation, after tensile
cracks observed. Referring to Fig. 5 and Fig. 6, the vertical pressure
could facilitate as crack-opening action of the surface crack,
releasing the horizontal pressure. This result clarifies why the
diagonal cracks were generated, following the surface cracks in the
experiment.
Results of the stereo-microscope of cross-section at 56 days are
shown in Fig. 7. Three micro-cracks were observed at the cross
section toward the bottom of RC beam. Orientation of these cracks
is about -78.75°. Thus, referring to Figs. 5-7, it is confirmed that
tensile cracks are observed at -78.75° orientation as the surface
cracks and the spalling cracks.
4. Conclusions
Mechanisms of corrosion-induced cracks in RC are studied
analytically and experimentally. Micro-fracture mechanisms for
nucleating the surface cracks and the spalling cracks due to expansion of the corrosion products are identified by SiGMA.
According to the results, the surface crack is initiated as tensile cracks, whereas mixed-mode and shear cracks are less active.
These tensile cracks mostly dominate at the bottom of the beam, especially at -78.75° orientation. After the surface crack is
nucleated, the spalling crack is prone to extension as tensile cracks. Then, the mixed-mode and shear cracks are slightly
dominant, again around at 78.75° orientation. The results confirm a promise to identify the fracture mechanisms of
corrosion-induced cracks by BEM and AE-SiGMA analysis.
References [1] Y. Kawasaki, Y. Tomoda and M. Ohtsu., “AE monitoring of corrosion process in cyclic wet-dry test” J. of Construction and
Building MATERIALS, Vol. 24, Issue.12, pp.2353-2357, 2010. [2] Ohtsu, M., “Simplified Moment Tensor Analysis and Unified Decomposition of Acoustic Emission Source : Application to
In Situ Hydrofracturing Test,” J. of Geophysical Research, Vol. 96, No. B4, pp. 6211-6221, 1991. [3] Ohtsu, M., Okamoto, T. and Yuyama, S.,”Moment Tensor Analysis of Acoustic Emission for Cracking Mechanisms in
Concrete,” ACI Structural Journal, 95(2), pp. 87-95, 1998.
Fig. 6 Results of the SiGMA analysis at 42 days elapsed.
(a) Side view (b) Sectional view
(a) Observed
location
(b) Cross-section
Fig. 7 Results of Stereo-microscope at 56 days.
54
S7-6 AE Signal Detected during Leak from Gas Pipe in Sand. Yoshida et al.
AE Signals Detected during Leak from Gas Pipe in Sand
K. Yoshida, K. Tsuri and H. Nishino
Faculty of Engineering, The University of Tokushima
2-1 Minamijosanjimacho, Tokushima 770-8506, Japan
Abstract
The characteristics of the AE activities detected during gas leak from pipes with the artificial defects have investigated. The artificial defects consisted of the straight-type pinholes of 0.2 mm and 0.5 mm diameter, the stepwise-type pinholes with 0.5 mm upper- and 0.3 mm lower-diameter, 1.5 mm depth, and the slit-types of 0.24 mm width, 1 mm and 2 mm length, respectively. It is important that AE signals during the gas leak is the continuous-type. AE parameters used to express the characteristics of the gas leak are the mean amplitude and the peak frequency with increase of the gas pressure. In the case of the gas leak without the obstacles, we found out the unique AE behaviors the screech tone to depend on the peak frequency and self-excited oscillation to depend on the active mean amplitude.
In this paper, the gas leak in sand was conducted on the same condition as that without obstacles. AE characteristics that showed the screech tone and the active mean amplitude disappeared except the slit-type defect. That is the effect of the obstacles on the gas flow near the outlet of the gas leak. It is possible and valuable to monitor the gas leak from the slit-type defect.