v001t05a013-smasis2014-7694

1 Copyright © 2014 by ASME

AUTONOMOUS SCOUR MONITORING OF BRIDGES AND EMBANKMENTS USING BIO-INSPIRED WHISKER FLOW SENSOR ARRAYS

R. Andrew Swartz

Michigan Tech Houghton, MI, USA

Baibhav Rajbandari Michigan Tech

Houghton, MI, USA

Benjamin D. Winter Michigan Tech

Houghton, MI, USA

ABSTRACT Detection of damage to the boundary conditions of

structures can be equally important as detection of structural damage. Civil structures sit on foundations which are, ideally, constant over time and are integral to collapse prevention. Any processes that compromise the foundations or the soil around them also constitutes damage to the structure. Bridge structures as well as embankments near roadways and viaducts can be particularly prone to this kind of attack when high-velocity water flows transport sediment away from the bridge foundation (scour). This process can be difficult to detect because 1) it happens out of sight, underwater; and 2) scour holes tend to grow and shrink at time progresses and materials are either carried away or deposited by the water. In this study, use of a buried-rod scour detection system based on magnetostrictive and magnetic flow sensor arrays is investigated. For buried-rod scour detection systems, an array of small, flexible, strain-sensitive rod sensors is distributed around the foundations which generate dynamic signals they are waterborne and static signals when buried. The pattern of static and dynamic signals reveals the depth of scour around the structure. Magnetostrictive sensors are appealing for this application due to their robustness. In this paper the effectiveness signal processing and scour detection algorithms are explored for water-coupled magnetostrictive whisker sensors of varying geometries to determine their sensitivity and the thresholds for false alarms and missed alert conditions at varying flow rates. Experimental laboratory data is utilized for this study.

INTRODUCTION Structural health monitoring (SHM) is concerned with protection of a structure and its inhabitants from deleterious

effects imposed on it by usage and the environment. SHM is often aimed at detecting damage, often using autonomous change detection [1]. Damage to the structure consists of any negative changes that impact its ability to perform its intended purpose. The interaction of a structure with its boundary conditions can impact the ability of a SHM system to detect damage by altering the behavior of a structure in a manner that induces a measurable change that may or may not represent actual damage to the structure. For civil engineering structures that do not move from place to place, often it is desirous to have boundary conditions that are constant over time. These boundary conditions are the foundation and soil/rock supporting that foundation and they play an important part in enabling the structure to perform its intended function. When the boundary conditions are compromised, the structure is compromised as well. In that sense, monitoring of the soil around the foundation of a structure is as important as monitoring the structure itself when there is reason to believe that the soil may become compromised during the lifetime of the structure.

Bridges and roadways near flowing water courses are particularly prone to this kind of support degradation. Flowing water can carry away soil materials from around the supports of structures in a process called scour which is the leading historical cause of bridge collapse in the United States [2]. Mitigation and monitoring of scour around bride piers and abutments is a major concern and cost for state departments of transportation (DOTs) who are responsible for the safety of the motoring public. The most common practice for scour monitoring of these structures is biannual inspections in which qualified inspectors travel to the bridges and, through use of rods or sounding weights, determine the riverbed profile around the bridge supports. This practice is limited in that it is a biannual process, where the state of scour at these structures is

Proceedings of the ASME 2014 Conference on Smart Materials, Adaptive Structures and Intelligent Systems SMASIS2014

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always in flux. Scours holes form and refill frequently with high and low-flow events, meaning that the inspectors will almost certainly miss peak scour conditions leaving them to extrapolate what the peak scour risk conditions were since the last inspection. In addition, real-time alerts are not really possible with biannual inspections. When critical scour conditions exist, it is preferable to know in advance to be able to close the bridge and protect both inspectors and the motoring public. An autonomous SHM system directed at the supports to monitor for scour can provide real-time alerts as well as log peak scour conditions to help DOTs better manage the scour risk to bridges and plan maintenance and scour countermeasure strategies. Likewise, autonomous monitoring of riverbank stability is an important strategy to protect roadways where erosion can undermine their support.

Embedded instrumentation approaches have been developed for scour monitoring, but suffer from drawbacks that negatively affect their suitability for low-cost, autonomous operation [3]. Sonic depth sounders experience difficulty in turbulent waters, those with high levels of suspended sediments, or icy waters. Sliding collar devices do not depend on water quality, but only provide an indication of lowest scour level [3]. Subsurface, geophysical methods (e.g., continuous seismic-reflection profiling and ground penetrating radar) can be very effective in detecting real-time scour information, but require extensive time, knowledge, and training to interpret [4]. Buried RF-based scour sensors (buried devices that transmit alerts when their cover erodes away) give a very good indication of scour when it reaches a critical level but, like the sliding collar devices, are one shot devices; also, there is no way to distinguish between safe conditions (no scour) and failure of the buried electronic device [3].

To overcome the limitations imposed by current scour detection methods, a novel monitoring approach utilizing an embedded array of sensors located on the outer surface of the bridge foundations that can determine the sediment depth and profile around the foundation in real time. The sensor array is composed of bio-inspired, whisker-shaped magnetostrictive flow sensors that are highly rugged, self-powered, and able to detect water flow by bending. These sensors are installed in modular, wireless smart scour sensor posts (Fig. 1) that can be installed around bridge abutments or piers as needed and communicate to high-powered base stations that transmit scour data to DOT offices using a cellular data modem (Fig. 2). These sensors are mounted within the posts at varying heights. Those sensors located above the sediment level will be free to move with the current flow and will yield dynamic flow measurements. Those sensors located below the sediment line will be trapped and will return only static measurements. Knowledge of sensor depth will help to determine the sediment level in real time. Additional posts can be placed in embankments near roadways to monitor bank stability (Fig. 3). The automated data acquisition base station aggregates data from numerous posts within its physical communication range using a low-power wireless local area network (WLAN), and send scour alerts to relevant authorities, when appropriate.

In this paper, a simple algorithm for differentiating between static (buried) and dynamic (free) sensor signals for this scour monitoring system is presented. The algorithm is designed to utilize relatively small amounts of computer memory and processing power to make it suitable for embedded implementation in low-cost wireless sensor nodes

Fig. 1. Smart scour sensing post.

Fig. 2. Scour sensing post and base station layout around bridge abutments.

Fig. 3. Smart scour sensing post embankment stability monitoring

application.

placed inside of the smart scour sensing posts. Testing conducted in a controlled laboratory scour flume environment is performed to establish the limits of the algorithm in determining the scour depth particularly when water flow levels are very small. The next section will present the experimental approach, and is followed by results and discussion.

METHODS In this section, the experimental and signal processing

approaches utilized in this study are described. Laboratory Experimental Setup A one meter wide scour flume was used to investigate the

sensitivity of the magnetostrictive flow sensors in a scaled scour monitoring application where the water flow conditions could be carefully controlled. Because of the small size of the



flume (relative to field conditions), the scour posts had to be scaled down and PVC rods, each with three externally mounted whiskers, were used instead (Fig. 4). Two different whisker geometries were used. One geometry utilized a bare whicker with a rectangular cross section (Fig. 5a), the other utilized the same cross section enhanced with a plastic air-foil shaped

Fig. 4. Small scale smart sensor posts for riverbank stability

monitoring study in laboratory scour flume.

(a) (b)

Fig. 5. Magnetostrictive whisker flow sensors; (a) bare-metal sensor; (b) airfoil enhanced sensor.

covering mounted through the trailing edge of the foil cross section (Fig. 5b). Previous work has indicated that this geometry increased instability in the whisker leading to an increase in sensitivity for this application [5]. The rods were placed in sand such that one whisker was always buried, one always free, and one only partially exposed. Water flow rates were slowly increased and whisker behavior recorded for signal processing.

Signal Processing It is important to be able to autonomously differentiate

between static and dynamic data to classify the whisker sensors as either buried or free and in order to describe the state of scour using the smart scour sensing post approach. The method used should require relatively little in the way of computational overhead in order to be suitable for low-power embedded systems suitable for long-term field deployments (e.g., wireless sensing units). Fluid structure interaction for the whiskers will create significant dynamic signals when flow rates are high, but

becomes more difficult (and eventually impossible) to differentiate from electrical noise when flow levels approach zero. Examining basic statistics of the signal returned from the whisker, such as the standard deviation, are effective for highly dynamic data, but lack discernment for lower levels of dynamism. In addition, sensor errors, including intermittent rail

(a) (b)

(c) (d)

Fig. 6. Time history signal outputs from magnetostrictive whisker flow sensors; (a) buried sensor; (b) unburied sensor – low-flow rate; (c) unburied sensor – medium-flow rate; (d)

unburied sensor – high-flow rate.

conditions (short circuits) and disconnected transducers produce very high standard deviation signals as well.

Instead, simple frequency-domain measures are used to classify static versus dynamic signals. Whisker signals are converted into the frequency domain using a Fast Fourier Transform (FFT) algorithm and the sum of power spectral density (PSD) of the signal in the low-frequency range (0-5 Hz) is used to indicate dynamic versus static conditions. Experimentally derived thresholds are then established and used to classify the signals. Different thresholds are employed for each transducer geometry reflecting the higher level of fluid structure interaction associated with the modified airfoil sensor. Fit and validation data sets were recorded. In total, fifty sets of data were collected at eight different flow velocities. Each run was 60s long and collected using a sampling rate of 50Hz.

RESULTS Sample sensor outputs (in raw voltage units) collected

from the airfoil enhanced whisker sensor are shown in Fig. 6. Mean values are removed for clarity. The buried sensor (static data) is also shown for comparison. In this figure, the dynamic nature of the signal can be seen qualitatively versus broadband white noise recorded for the buried sensor. PSD plots for the buried and the unburied whiskers show that much of the range of the fluid-structure interaction dynamics falls within a 0-5Hz bandwidth (Fig. 7), showing the frequency band of the fluid-coupled whisker structure. Excessive PSD energy outside of



this band can be used to help to identify sensor fault conditions in the field.

The success of the classification approach using the validation data set is presented in Fig. 8. In Fig. 8a, the percentage of sensor signals classified as dynamic signals versus gross water velocity in the flume is presented for the bare whisker sensor. Here, a high threshold is selected to try to

(a)

(b)

Fig. 7. PSD derived from sensor outputs for (a) buried and (b) unburied whisker sensors.

limit the number of false-positive results (i.e., limiting the reporting of scour when no scour exists). Success generally increases as flow rate increases, however sensitivity is an issue even at higher flow rates. Incorrect classifications exist for the unburied whiskers at significant rates (over 30%), though the classification of the buried whisker is always successful. Classification of the partially buried sensor is problematic. In Fig. 8b, the classification results for the airfoil enhanced sensor are presented. Here, sensitivity has been noticeable increased and the classifier is more successful for the unburied though false-positive results do increase slightly (i.e., the reporting of scour conditions when no-scour conditions exist). With the addition of the modified airfoil to the whisker, the partially buried sensor begins to resemble a fully unburied sensor in its behavior increasing the sensitivity of the system. Good

classification is obtained even at very low flow velocities using the modified airfoil enhanced whisker.

CONCLUSIONS In this study, it is shown that a relatively simple frequency-

domain approach is effective in classifying signals obtained

(a)

(b)

Fig. 8. Classification of signals as unburied whiskers (a) bare whisker sensor (b) airfoil enhanced whisker.

from a novel magnetostrictive scour sensing array to aid in identification of the state of scour around a bridge or roadway. The sum of the magnitude of the frequency content in a relatively narrow band (0-5Hz) can indicate the difference between static and dynamic whisker behavior. The addition of a modified airfoil to the whisker appears to greatly increase the sensitivity of the system to moving water even at low flow conditions and makes classification easier. However, false positive and false negative results will still occur necessitating the use of redundant measurements and statistical methods to indicate the most likely condition of the soil around the structure and this area represents an important focus for future work. Decision support tools should emphasize and understanding of this uncertainty when reporting results to decision makers.

ACKNOWLEDGMENTS This work is supported by the Commercial Remote

Sensing and Spatial Information Technologies program of the

Low-level, broadband noise

Fluid-structure interaction dynamics evident in 0-5 Hz

frequency band



Office of the Assistant Secretary for Research and Technology (OST-R), U.S. Department of Transportation (USDOT), Cooperative Agreement #RITARS-12-H-MTU, with additional support provided by the Michigan Department of Transportation (MDOT), the Maryland State Highway Administration (MDSHA), Michigan Technological University, the Michigan Tech Research Institute, and the Center for Automotive Research. The views, opinions, findings, and conclusions reflected in this paper are the responsibility of the authors only and do not represent the official policy or position of the USDOT/OST-R, MDOT, MDSHA, or any other entity. The authors would also like to acknowledge Brian Barkdoll (Michigan Tech) for his invaluable assistance.

REFERENCES 1. Farrar, C.R., and Worden, K., Structural Health

Monitoring: A Machine Learning Perspective. 2012, New York, NY: Wiley.

2. Kattell, J., and Eriksson, M., Bridge Scour Evaluation: Screening, Analysis, & Countermeasures. 1998, United States Department of Agriculture Forest Service Technology & Development Program, San Dimas, CA.

3. Lagasse, P.F., Richardson, E.V., Schall, J.D., and Price, G.R., Instrumentation for measuring scour at bridge piers and abutments. 1997, Report 396, Transportation Research Board, Washington D.C.

4. Gorin, S.R., and Haeni, F.P., Use of Surface-Geophysical Methods to Assess Riverbed Scour at Bridge Piers. 1989, Report 88-4212, U.S. Geological Survey, Water Resources Division, Reston, VA.

5. Day, S.R., Flatau, A., Na, S.M., and Swartz, R.A., The design and construction of a scour monitoring system. 2014.


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