seminar report (roll no. 143040044) - structural health monitoring of concrete structures

Structural Health Monitoring of Concrete Structures Submitted for CE694 (Seminar)

Master of Technology

in

STRUCTURAL ENGINEERING

Submitted by

Souradeep Sen

(Roll No. 143040044)

Under the supervision

of

Prof. Sauvik Banerjee

Department of Civil Engineering

Indian Institute of Technology Bombay

November, 2014

Abstract

Structural Health Monitoring is the field where the health of structures are assessed, monitored and evaluated from time to time, for the improvement in serviceability of these structures. In this seminar report, a basic understanding of the meaning, need, and methods of structural health monitoring (SHM) has been presented. Also, the SHM of concrete structures have been mentioned and case studies regarding these have been presented in a concise manner.

Methods regarding the uses of piezoceramic based sensors, Interferometric methods of frequency evaluation and self-diagnosis materials for structural health monitoring have been presented case-wise from the works of various authors. The results from the experiments conducted for the respective methods have been shown and discussed.

Acknowledgement

My indebtedness to Prof. Sauvik Banerjee is unlimited for his kind guidance, co-operation and help in selection of the topic, describing the topic in detail and presenting a large number of study materials which helped me in completion of this seminar report.

Souradeep Sen

Roll No. 143040044

Structural Health Monitoring of Concrete Structures

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Table of Contents

Introduction to Structural Health Monitoring........................................................................... 3 Case Studies ................................................................................................................................... 5

1) Wen-I Liao et al. (Structural health monitoring of concrete columns subjected to seismic excitations using piezoceramic-based sensors). .......................................................................... 5

Introduction ............................................................................................................................. 5 The Specimen .......................................................................................................................... 6 Method of measuring the Energy and Damage Index used in the experiment ........................ 9 Results after the shake table test ............................................................................................ 10

2) F.C. Ponzo et al. (Structural Health Monitoring of Reinforced Concrete Structures using Nonlinear Interferometric Analysis). ........................................................................................ 12

Introduction ........................................................................................................................... 12

The Simplified Method Proposal Method Proposed by Ponzo et al. ..................................... 13

Frequency and Damping Cofficient by IRF, Interferometric and S-transform Methods ...... 14

3) H. Inada et al. (Improvement on Health Monitoring System Using Self-diagnosis Materials for Practical Application). ......................................................................................................... 19

Introduction ........................................................................................................................... 19 Self-Diagnosis Materials and their Production ...................................................................... 20

Displacement measuring device using the Self-Diagnosis Materials .................................... 22

Conclusions .................................................................................................................................. 25

References .................................................................................................................................... 27


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Lists of Figures

Figure 1 Configuration of the tested column specimen in lateral view and front view. ................ 6 Figure 2 Test setup of the shake table test. .................................................................................... 7 Figure 3 TCU078 seismogram of the 1999 .................................................................................... 8 Figure 4 Location of the smart aggregates ..................................................................................... 8 Figure 5 Damage Pattern of PGA 600 gal and 900 gal ................................................................ 10 Figure 6 Sinusoidal wave response after the earthquake excitation at different PGA levels ...... 10 Figure 7 Damage index matrix of sensors after the earthquake excitation at different PGA levels....................................................................................................................................................... 11 Figure 8 (a) Results obtained by using the station located at the top floor as a reference station (b) Impulse Response Function obtained by using the bottom floor as a reference station (from Snieder and Safak (2006)) ............................................................................................................ 14 Figure 9 (Left) Numerical Model (Right) Input used for the analyses ........................................ 15 Figure 10 IRFs evaluated using the bottom floor as a reference station ...................................... 16 Figure 11 Nonlinear Interferometric Analysis performed on the top floor accelerometric recording and S-Transform evaluated on the single IRF .............................................................. 18 Figure 12 Schematic drawing of materials. ................................................................................. 20 Figure 13 Two types of sensors. .................................................................................................. 20 Figure 14 Result of tensile test and regression ............................................................................ 21 Figure 15 Manufacturing Process ................................................................................................ 22 Figure 16 Displacement of measuring device .............................................................................. 23 Figure 17 Measurement of Displacement during Shake-Table Tests .......................................... 24 Figure 18 Examples of test results. .............................................................................................. 24


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Chapter 1

Introduction to Structural Health Monitoring

The process of implementing a damage detection and characterization strategy for engineering structures is referred to as Structural Health Monitoring (SHM). Here damage is defined as changes to the material and/or geometric properties of a structural system, including changes to the boundary conditions and system connectivity, which adversely affect the systems performance. The SHM process involves the observation of a system over time using periodically sampled dynamic response measurements from an array of sensors, the extraction of damage-sensitive features from these measurements, and the statistical analysis of these features to determine the current state of system health. For long term SHM, the output of this process is periodically updated information regarding the ability of the structure to perform its intended function in light of the inevitable aging and degradation resulting from operational environments. After extreme events, such as earthquakes or blast loading, SHM is used for rapid condition screening and aims to provide, in near real time, reliable information regarding the integrity of the structure

Structural Health Monitoring (SHM) aims to give, at every moment during the life of a structure, a diagnosis of the state of the constituent materials, of the different parts, and of the full assembly of these parts constituting the structure as a whole. The state of the structure must remain in the domain specified in the design, although this can be altered by normal aging due to usage, by the action of the environment, and by accidental events. Thanks to the time-dimension of monitoring, which makes it possible to consider the full history database of the structure, and with the help of Usage Monitoring, it can also provide a prognosis(evolution of damage, residual life, etc.). If we consider only the first function, the diagnosis, we could estimate that Structural Health Monitoring is a new and improved way to make a Non- Destructive Evaluation. This is partially true, but SHM is much more. It involves the integration of sensors, possibly smart materials, data transmission, computational power, and processing ability inside the structures. It makes it


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possible to reconsider the design of the structure and the full management of the structure itself and of the structure considered as a part of wider systems.

In this seminar report, the methods of Structural Health Monitoring for Concrete Structures will be discussed, via various case studies accomplished by various institutes and people across the globe. The various methods have been successfully tested to be useful for the health monitoring of structures and have been presented in a compact manner.


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Chapter 2

Case Studies

1) Wen-I Liao et al. Structural health monitoring of concrete columns subjected to seismic excitations using piezoceramic-based sensors

Introduction

Throughout the life cycle of a concrete structure, important issues have to be addressed properly to ensure the safe operation of these structures. It becomes essential to perform structural health monitoring on these structures that can detect the amount of damage on these structures. After earthquake, it becomes very essential to monitor the health of the structure due to the earthquakes catastrophic nature. In recent years, piezoelectric materials have been successfully applied to the structural health monitoring of concrete structures due to their advantages of active sensing, low cost, quick response, availability in different shapes, and simplicity in implementation.

There are two major categories of piezoelectric-based health monitoring. The first includes the impedance-based approach, in which the impedance of piezoelectric transducers can be applied to the health monitoring of structures. Although, it is difficult to identify the distribution and severity of a large damaged region, the advantage of the impedance-

based approach is that it does not require knowledge of the modal parameters or other failure information of the structure.The second approach is the wave-based health monitoring approach (Okafor et al 1996, Saafi and Sayyah 2001), in which the wave-propagation properties are studied to detect and evaluate the cracks and damages inside concrete structures. Wang et al (2001) studied the debonding behaviors between steel rebar and concrete by using PZT (lead zirconate titanate) patches fixed on the rebar. Sun et al (2008) used a PZT patch transducer to


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initiate and receive elastic waves in the concrete, and obtained the modulus of elasticity by utilizing the wave-propagation characteristics.

Song et al (2004, 2007, 2008) developed smart aggregate (SA), an innovative multifunctional piezoceramic-based device, to perform structural health monitoring for concrete structures. The smart aggregates have been successfully utilized in the structural health monitoring of a two-story concrete frame structure (Gu et al 2007).

The piezoceramic based smart aggregates were distributed at strategic location prior to the casting of the column for the formation of an active sensing system for the health monitoring of

the column. A shake table was used to simulate the earthquake ground motion recorded in the Taiwan 1999 earthquake. The acceleration was increased gradually upto failure of the column. During the tests, the distributed smart aggregates and PZT patches embedded in the concrete columns were utilised to perform the structural health monitoring. One of the PZTs was used as an actuator for generating and propagating of waves and the others were used as sensors to detect the waves. If the propagation energy was attenuated at certain portions, it meant that the portion had cracks or voids. The decreased value of the transmission energy is proportional to the severity and extent of the damage.

Figure 1 Configuration of the tested column specimen in lateral view and front view.

The Specimen


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Figure 2 Test setup of the shake table test.

A flexural governed reinforced concrete column was tested on a shake table (Figure 2). The height and cross section of the column were 80 cm and 20 20 cm2, respectively. The rebar arrangement of the test columns was 4#3 longitudinal steel rebars with #3@10 cm stirrup. The average concrete strength was 210 kg cm2 and the

average yielding stresses for #3 rebar was 4100 kg cm2. The size of the top concrete

plate was 160 160

20 cm3 and the size of the reinforced concrete foundation was 120 50 40 cm3. The foundation was designed to remain elastic when the failure of the reinforced concrete column occurred. The horizontal displacements and response accelerations of the column were measured by linear variable displacement transducers (LVDTs) and accelerometers. A total mass of 1000 kg was put on the top plate in order to increase the inertia force. Two load cells were placed underneath the foundation to measure the vertical load and base shear during the test. Furthermore, in order to prevent the abrupt falling of the top concrete plate and additional lead blocks due to failure of the reinforced concrete column, four steel columns were provided in the four corners of concrete mass block for support. The input acceleration time history for all shake table tests is the E-W component of the record at TCU078 station of the 1999 Taiwan Chi- Chi earthquake (denoted as TCU078EW). Figure 3 below shows the seismogram of the acceleration time history. Corresponding to the input ground motion, the test

protocol is sequentially the peak ground acceleration (PGA) of 50 gal, 200 gal, 400 gal, 600 gal, and 900 gal, and the specimen failed at the test run of PGA=900gal.


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Figure 3 TCU078 seismogram of the 1999

In this test, PZT-type piezoceramic patches were embedded in a 1 inch cube of concrete (called smart aggregate). The piezoelectric strain constat d33 and piezoelectric voltage constants g33 of the PZT patches are 390 1012 C N1 and 24 103 V mN1, respectively. Piezoceramic patches are usually fragile and can be easily damaged by the vibrations subjected to the column. To protect the peizeoelectric transducers, the patch is coated with insulation in order to prevent moisture related damages. Only then it is embedded in the concrete block as a smart aggregate.

Figure 4 Location of the smart aggregates

The sensors were kept at the specific locations as it was speculated that damage would most likely occur at the places which have a tendency of formation of a plastic hinges. The actuator was kept at the centre of the column. (Figure 4)


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Method of measuring the Energy and Damage Index used in the experiment Wavelet packet analysis was used as a signal processing tool in order to analyse the signals detected by the smart aggregates. The sensor signal (say S) was decomposed by an n-level wavelet packet decomposing into 2n signal sets{X1, X2,., X2n }. If Eij is the energy of the decomposed signal, here i stands for time index and j for the frequency band XJ = [xj,1, xj,2, . . . , xj,m ] where m is the sampling data.

The energy vector at time index i can be given as Ei = [Ei,1, Ei,2, . . . , Ei,2n ].

Root-mean-square deviation (RMSD) is a commonly used damage index to compare the difference between the signatures of the healthy state and damaged state. In the proposed approach, the damage index is formed by calculating the RMSD between the energy vectors of the healthy state and the damaged state. The energy vector for healthy data is Eh = [Eh,1, Eh,2, . . . , Eh,2n ]. The energy vector Ei for the damaged state at time index i is defined as Ei = [Ei,1, Ei,2, . . . , Ei,2n ]. The damage index (DI) at time i is defined as:

The proposed damage index represents the transmission energy loss portion caused by damage. When the damage index is close to 0, it means that the concrete structure is in a healthy state. The greater the damage index, the more serious the damage is.


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Results after the shake table test

Figure 5 Damage Pattern of PGA 600 gal and 900 gal

The shake table test causes the column to crack at the bottom and top. Figure 5 shows the failure pattern of the column specimen after the PGA runs near 600 to 900 gal. After this, the smart aggregates were removed for the structural health monitoring during and after the shake table test. From the sensor voltage graph (Figure 6) of the PZT-S2 used as the smart aggregate, it was seen that the peak of the peak sensor voltage decreased as the ground acceleration of the shake table kept increasing. This means that the propagated energy kept getting attenuated higher as the cracks in the column kept increasing in size and number.

Figure 6 Sinusoidal wave response after the earthquake excitation at different PGA levels


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The plot of the damage index matrix of sensors is shown in figure , and the values are demonstrated in Table 1 with the measured drift ratio of the column for comparison, where the drift ratio is defined as the relative displacement at the column top to the column bottom divided by the column height. After each test run of the shake table test, the sensor signals for health monitoring have been measured twice by the same sweep sine excitation. The damage indices shown in Table 1 were the mean values of the first and second measuring. The value shown in brackets after the damage index was the relative error between each measuring.

Figure 7 Damage index matrix of sensors after the earthquake excitation at different PGA levels


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2) F.C. Ponzo et al. Structural Health Monitoring of Reinforced Concrete Structures using Nonlinear Interferometric Analysis

Introduction The maintenance of a huge number of aged structures and infrastructures require a huge effort and present a huge task to accomplish, if a detailed analysis of seismic risk is to be done properly. It was seen that periodic visual inspection was rendering to be more and more ineffective. A specific task of the Italian research RELUIS-DPC 2010-2013 project, funded by the Department of Italian Civil Protection (DPC), dealt with the devising and implementing of a fast procedure (Ponzo et al., 2010) to obtain useful information about the damage evolution in a large number of strategic buildings during and after seismic events, by the help of just a few sensors. The two most important goal which to be obtained regarding the usage of sensors was feasibility and cost optimisation, so that it could be used in a more widespread manner. Significant research in the field of Non-destructive Damage Evaluation (NDE) methods has been done which have made use of the dynamic response changes in the structure. (Chen et al., 1995; Capecchi and Vestroni, 1999; Ponzo et al., 2010)

NDE methods are classified into Four Levels, depending on the amount of data that can be retrieved from the sensors.

(i) Level I methods, i.e. those methods that only identify if damage has occurred. (ii) Level II methods, i.e. those methods that identify if damage has occurred and

simultaneously determine the location of damage.

(iii) Level III methods, i.e. those methods that identify if damage has occurred, determine the location of damage as well as estimate the severity of damage.

(iv) Level IV methods, i.e. those methods that identify if damage has occurred, determine the location of damage, estimate the severity of damage and evaluate the impact of damage on the structure.

With the increase in level, the amount of data and algorithm required became more sophisticated. And so did the cost. Ponzo et al. (2010) had proposed an innovative approach for a simplified


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structural damage detection. Due to this, it was easier to obtain information about the health of the structure minutes after a seismic activity. The method includes the acquisition of structural dynamic response by a three-directional accelerometer which is to be installed on top floor of a structure. From the data, maximum acceleration, frequency variation and equivalent viscous damping can be obtained and hence be used to find the maximum inter-storey displacement.

The paper focused on the interferometric analyses (Snieder and Safak, 2006; Picozzi et al., 2011) useful to obtain the dynamic response of the monitored structure. Particularly, the Impulse Response Function (IMF) obtained by mean the interferometric analysis, applied on the data recorded on the monitored structure, was combined with the S-Transform (Stockwell et al., 1996) to perform a pseudo time-frequency analysis with the aim to automatize the procedure to evaluate both frequency and damping variation during earthquakes.

The Simplified Method Proposal Method Proposed by Ponzo et al.

The method by Ponzo et al. (2010) began from a limited number of records obtained from the accelerometer on the top floor of the structure. It overcame certain limitations of Level I

methods. The method considers some parameters evaluated by the recorded signals: (i) Maximum Absolute Top Acceleration (MATA); (ii) variations of the fundamental frequency (iii) variation of the equivalent viscous damping, and provides a combination of these parameters to estimate the maximum interstorey drift by means of an empirical relationship. All signals are filtered with band-pass filter centred on the fundamental frequency of the monitored structure.

The Maximum Absolute Top Acceleration represents the first instrumental parameter. It was evaluated directy by the filtered signal (filtered by band filter) recorded by the accelerometer. An appropriate arrangement of recording sensors on the structure permits to reconstruct all displacement and rotation components of the floor. The other two instrumental parameters


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considered in the method are the percent variations (f1) between the fundamental frequency of the building before the seismic event finit and the minimum one fmin, corresponding to the maximum non linear behaviour of the building and the percent variations (f2) between initial and final frequency (ffin)

The frequencies were evaluated by using a STFT (Short Time Fourier Transform) applied to the signals. The final instrumental parameter considered is the variation of the equivalent structural viscous damping related to the first mode of vibration of the structure. For non-stationary signals, the damping can be found by the semi-probabilistic approach elaborated by Mucciarelli and Gallipoli (2007).

Frequency and Damping Cofficient by IRF, Interferometric and S-transform Methods

The structure which was selected is as shown in Figure 9. It had 5 storeys, with inter-storey height of 3m, representative of Italian Standard Buildings. The building considered had a plan of 12 m x 15m.In order to take into account the presence of infill panels within the structural R/C frames and their interaction with the columns, both the masonry strength and stiffness contribution had been considered by inserting two equivalent structural elements in the models. The mechanical characteristics of these elements were evaluated considering the Mainstone model (Mainstone, 1974).

Figure 8 (a) Results obtained by using the station located at the top floor as a reference station (b) Impulse Response Function obtained by using the bottom floor as a reference station (from

Snieder and Safak (2006))


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It was noted that using the top floor as a reference station it was possible to retrieve information about the wavefield propagated into the building while using the bottom floor as a reference station it was possible to extract the impulse response function of the building.( After deconvolution of the response in frequency domain)

Figure 9 (Left) Numerical Model (Right) Input used for the analyses

The damping can be evaluated before and after earthquake using the IRF (Impulse Response Function). Figure 10 shows the example of an IRF evaluated with bottom floor as reference. By using logarithmic decrement method, the viscous damping can be evaluated.


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Figure 10 IRFs evaluated using the bottom floor as a reference station

It was noted that from the IRFs evaluated at the top floor it was also possible to extract interesting information related to the fundamental frequency of the structure. In fact, comparing the results obtained from the fundamental frequency of the structure before and after the earthquake it was possible to note a shift of the frequency from 2.0Hz (before the earthquake) to 1.10Hz (after the earthquake). Applying the logarithm decrement method on the evaluated IRFs before and after the earthquake it was seen that the equivalent viscous damping factor varied from 5.22% (before the earthquake) to 9.40% (after the earthquake).

Picozzi et al. (2011) showed that it was possible to evaluate the IRF also from a windowed signal acting on a single time-window. It was seen that the time-varying behavior of the structure of the structure could be represented as frequency variation using both the IRFs evaluated from windowed signals and the related S-transform.

As discussed in the previous section, the method proposed by Ponzo et al. (2010) was based also on the frequency evaluation before, during and after an earthquake. In the method, a partially solved problem is the possibility to automatic evaluation of the fundamental frequency changes during the strong motion phase. Here a new approach for the automatic evaluation of the fundamental frequency over it is be shown. The fundamental frequency is constant for linear structural behavior. It can get lowered only by non-linear behavior. Hence, an upper bound for the frequency domain is maintained.


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When the frequency domain upper bound is established, just using ambient noise recordings, it is possible to use a limited domain for the interferometric analyses and for the S-Transform of the IRFs evaluated at the top level (using the windowed signal). At this purpose it is important to decide how is the length of the selected moving time-window and the related overlap. Generally, the time-window length is fixed as a function of the fundamental period of the structure:

w 10 T

where w (in seconds) is the moving time-window length and T is the fundamental period of the monitored structure. With regards to the moving time-window overlap, basing on the results obtained in this work, a good rule seems to be 50% of the considered time-window length.

In the following it is possible to find an example of application of the proposed procedure to automatic evaluate the fundamental frequency variation of the structure before, after and during the earthquake. The elastic starting frequency of the structure, as mentioned before, is equal to 2.0Hz with a period equal to 0.5sec. Using the rule established before it is necessary to use a moving time-window length greater than 5sec. It is worth noting that during the earthquake, if the structure exhibits a nonlinear


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Figure 11 Nonlinear Interferometric Analysis performed on the top floor accelerometric recording and S-Transform evaluated on the single IRF

It is worth noting from Figure 11 that the instantaneous fundamental frequency of the structure changes over time starting from a value equal to 2.0Hz, reaching a minimum frequency equal to 0.85Hz and concluding with a fundamental frequency equal to 1.10Hz. For each time-window the fundamental frequency can be automatically evaluated considering the S-Transform results. In fact, the fundamental frequency corresponds, for each time-window, to the frequency related to the maximum value of the S-Transform for.


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3) H. Inada et al. Improvement on Health Monitoring System Using Self-diagnosis Materials for Practical Application

Introduction Programming of sensors to memorise peak values of deformation of strain caused by the catastrophic disasters of earthquakes and other such calamities in Structural Health Monitoring is essentially required. Also, the ability of the sensors to retain this experienced information makes it possible to remove the need for continuous monitoring of the structures. In previous studies, the conductive fiber reinforced composite, the glass fiber reinforced plastics containing carbon black particles, has been confirmed to respond sensitively against applied strain and memorise the peak value. Because the percolation structure formed by carbon black causes irreversible change in resistance, the sensor maintains the electrical resistance value corresponding to the applied peak strain. Research has been conducted by the authors of the paper using the carbon materials as electrical conductive sensors. Earlier, conductive fibre reinforced composites, glass fibre reinforced concrete containing carbon black particles were tested and had responded positively. Due to the percolation structure formed by the carbon black, it leads to an irreversible change in the resistance. Hence, it can memorise the changed resistance values and hence the deformation can be extracted from the value of the changed resistance. These have been used on reinforced concrete structures with positive results.


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Self-Diagnosis Materials and their Production

Characteristics of materials The schematic drawing of self-diagnosis materials is shown in Figure 12. The rod-shaped FRP is composed of glass fibers and matrix phase consisting of thermoset epoxy. High-structure carbon black is dispersed into resin as conductive particles. The particle volume fraction is typically set at 5.8%. After being cured at 160C for 90min, the materials are carbonized through a pyrolysis process at 500C in N2 ambient. The carbonized composites were found to acquire high sensitivity and distinguished ability to memorize peak strain. The composite is utilized as a sensor by attaching grips and electrodes at both ends. In this study, two sizes of sensors as shown in Figure 13 are applied. As shown in the figure, two pairs of cables, connecting to outer current electrodes and inner voltage electrodes, are attached.

Figure 12 Schematic drawing of materials.

Figure 13 Two types of sensors.

Tensile tests were conducted on the sensors in order to evaluate its performance as strain sensors. A relation between the applied strain and the electrical resistance of the sensor was deduced as follows:

= ab ; a = 6.86103 , b = 0.384


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The resistance is represented as = (RR0) / R0 where R0 is the initial resistance value. The values of a and b were found by regression of the observed data.

The example of results obtained by tensile test and estimated relation by the above equation is shown in figure 14. As shown in the figure, sensor shows a distinguished memory function, but has lower detectivity against small strain under 1000.

Figure 14 Result of tensile test and regression

Development of production method of materials

Due to the non-uniformity of the cross sectional shape and dispersion of carbon black in the resin, there is variability in the tensile tests performed on the materials. Initially, the materials were manufactured one at a time, thus leading to the variation in the properties. In later stages, the materials are made in continuous batches to maintain equal quality throughout. Also by continuous manufacturing, the production costs also reduce. The production system using pultrusion process has been employed, with the mass production line as shown in the figure. Carbon black is dispersed into resin and warmed well in temperature controlled bath of 80C in order to accelerate uniform impregnation into glass fiber. The composites are gradually shaped into rod and cured at the same time, by passing them through a heated die with inner Teflon-coating. As a result, two rod shaped materials with different diameters for two types of sensors are formed simultaneously as shown. The diameters of each composite are 1.5mm and 0.9mm, respectively.


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Figure 15 Manufacturing Process

Displacement measuring device using the Self-Diagnosis Materials

Development of device

The developed measuring device is as shown in Figure 16. The device is composed of small sized sensor with helical compression spring, aluminum cylinder and tensile rod, which are all set coaxially. The rod and the cylinder are connected to two fix points, and slides from side to side smoothly with each other. The sensor is installed in the cylinder for protection and made watertight. Both ends of the sensor are clamped to cylinder and rod via spring, and relative

displacement caused between two fix positions are distributed to sensor (SE) and spring (SP) allocated according to their stiffness. Setting the gauge length and stiffness of the sensor LSE and EASE, the constant of spring kSP, relation between displacement X and strain of sensor SE is represented as follows: X = (EASE + LSE kSP ) . SE kSP


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Therefore, sensitivity and allowable displacement of the device can be controlled by specification of spring. Experimental results shown below have been obtained by specifying allowable displacement as 10cm using spring with constant of 28.3N/mm.

Figure 16 Displacement of measuring device

.

Vibration tests of displacement measuring device

Vibration tests were conducted on shake tables where only one-directional excitation was applied. The aim was to evaluate the applicability and performance of the displacement measuring devices. Because the peak memory sensor is applied to static post-event measurement, its performance is generally evaluated through static tests. However, in the real world scenario, sensors are required to record the response of the target structures against dynamic loading such as earthquakes.

Figure 17 gives the outline of shake table experiments conducted. One fixed part of device is mounted on the fixed table with the other end on the shaking table, vibrating in the horizontal direction (unidirectional vibration). The frequencies used in the sine sweep were 1, 2 and 5 Hz with amplitudes of 10, 10 and 5 cm respectively. After oscillating in constant amplitude during certain period, amplitude is decreased gradually. Number of specimens for each frequency is three. Relative displacement of shaking table is measured by laser displacement meter for comparison.


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Figure 17 Measurement of Displacement during Shake-Table Tests

Figure 18 Examples of test results.

The examples of obtained time waveforms of displacement and electrical resistance of the sensors are shown in Figure 18. Variation of resistance is represented as above-mentioned variation ratio . In all test condition, the sensor increases its resistance value only against the displacement in tensile direction, and keeps peak value corresponding to the maximum displacement. Therefore, the sensor is confirmed to show the expected performance as peak memory measuring device even against dynamic loading. The sensor also shows apparent resistance variation up to the maximum frequency of 5Hz in the tests, which demonstrates enough capability to follow the response of general structures against external excitations such as earthquakes. In the result of higher frequency, slight phase delay in response of the sensor against displacement has been observed. The mechanism and the effect on the sensing accuracy are being investigated in foregoing studies.


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Chapter 3

Conclusions Structural health monitoring including the dynamic identification techniques is getting a firm foothold in both the scientific as well as the civil community. The need for the assessment of the aged and important structures health has been on the rise for better longetivity of the structures and better serviceability. Various methods are being developed to assess the various properties of the structures which include concrete structures predominantly, given that most structures in the world are made of reinforced concrete.

The meaning of structural health monitoring, its needs and its objective were discussed initially. The various processed of structural health monitoring were mentioned along a few newly developed methods of assessing the health of a structure after an earthquake and similar phenomena were henceforth discussed.

In the first case study, a piezoelectric-based sensor system was described and developed for the structural health monitoring of concrete columns under seismic loadings. From the experimental results of the shake table test and the in situ cyclic loading test, the DI (damage index) obtained which were proposed for the specific sensing mechanism increased as the damage levels increased. Also the drift ratio of the columns had a similar variation as the damage indices. The proposed process of health monitoring can be used to predict the health of the structure and the level of damage after earthquakes and similar calamities.

In the second case study discussed, it was seen that techniques based on Fourier transform provide good results when the response of the system is stationary, but fail when the system exhibits a non-stationary, time-varying behaviour. In 1996, Stockwell introduced a new powerful tool for the signals analysis: the S-Transform. Compared with the classical techniques for the time-frequency analysis, this transformation shows a much better resolution and also offers a range of fundamental properties such as linearity and invertibility. The ability to investigate the non-stationary response of structures opens new scenarios, giving the opportunity

to explore new possibilities. The paper dealt with the combination of this S-transform and


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Interferometric Approaches in determining the change in the fundamental frequencies of the structure and the displacement from column to column on different stories.

In the third case study described, newly developed damage detection devices using self-diagnosis materials have been described. Their usage for damage detection for various structures is discussed. However, it had shown variation in quality due to absence of mass production lines. By the advent of pultrusion methods, the variational aspect of the sensors has been significantly reduced. The memory aspect of the sensors helps in the retention of the data after certain catastrophic events like earthquakes.


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References

Inada, H., Inada, Y. Okuhara, Y., Hayashi, Y. (2009). Improvement on Health Monitoring System Using Self-diagnosis Materials for Practical Application.

Hayashi, Y, Miyamoto, M, Nii, A, Suzuki, Y, and Morii, A. (2005). Structural health monitoring of hugetimber structures in Japan. Proc. International Conference on Structural Health Monitoring ofIntelligent Infrastructures. 1043-1048.

Inada, H, Kumagai, H, and Okuhara, Y. (2005). Development of monitoring techniques for concretestructures using self-diagnosis materials and wireless measurement systems. Proc. InternationalConference on Structural Health Monitoring of Intelligent Infrastructures. 619-626.

Inada, H, Kumagai, H, and Okuhara, Y. (2007). Health monitoring techniques to detect damage to concretestructures using self-diagnosis materials. Proc. International Conference on Structural HealthMonitoring of Intelligent Infrastructures.

Carkhuff B. and Cain R. (2003). Corrosion sensors for concrete bridges IEEE Instrum. Meas. Mag. 6 1924

Tseng K K and Wang L (2004). Smart piezoelectric transducers for in situ health monitoring of concrete

Wang C S, Wu F and Chang F K (2001). Structural health monitoring from fiber-reinforced composites to steel reinforced concrete Smart Mater. Struct. 10 54852

Liao, W., Wang, J., Song, G., Gu, H., Olmi, C., Mo, Y., Chang, K. and Loh, C. (2011). Structural health monitoring of concrete columns subjected to seismic excitations using piezoceramic-based sensors. Smart Mater. Struct., 20(12), p.125015

Ponzo,F. C., Ditommaso, R. & Auletta, G. (2012). Structural Health Monitoring of Reinforced Concrete Structures using Nonlinear Interferometric Analysis

seminar report (roll no. 143040044) - structural health monitoring of concrete structures

Documents

health of structures

health monitoring system

structural engineering

shm of concrete structures

respective methods

selfdiagnosis materials

seminar report

case studies