Big Data Analytics in Online Structural Health Monitoring
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Published
Nov 13, 2020
Guowei Cai
Sankaran Mahadevan
Abstract
This manuscript explores the application of big data analytics in online structural health monitoring. As smart sensor technology is making progress and low cost online monitoring is increasingly possible, large quantities of highly heterogeneous data can be acquired during the monitoring, thus exceeding the capacity of traditional data analytics techniques. This paper investigates big data techniques to handle the highvolume data obtained in structural health monitoring. In particular, we investigate the analysis of infrared thermal images for structural damage diagnosis. We explore the MapReduce technique to parallelize the data analytics and efficiently handle the high volume, high velocity and high variety of information. In our study, MapReduce is implemented with the Spark platform, and image processing functions such as uniform filter and Sobel filter are wrapped in the mappers. The methodology is illustrated with concrete slabs, using actual experimental data with induced damage
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Keywords
structural health monitoring, Online Monitoring, Big Data Analytics, non-destructive testing, MapReduce, Spark
References
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Araujo, A., Garca-Palacios, J., Blesa, J., Tirado, F., Romero, E., Samartn, A., & Nieto-Taladriz, O. (2012). Wireless measurement system for structural health monitoring with high time-synchronization accuracy. IEEE Transactions on instrumentation and measurement, 61(3), 801-810.
Bagavathiappan, S., Lahiri, B. B., Saravanan, T., Philip, J., & Jayakumar, T. (2013). Infrared thermography for condition monitoringa review. Infrared Physics & Technology, 60, 35-55.
Bao, Y., Beck, J. L., & Li, H. (2010). Compressive sampling for accelerometer signals in structural health monitoring. Structural Health Monitoring, 0(0), 1-12.
Baxes, G. A. (Ed.). (1994). Digital image processing: principles and applications. John Wiley & Sons.
Chakraborty, D., Kovvali, N., Wei, J., Papandreou- Suppappola, A., Cochran, D., & Chattopadhyay, A. (2009). Damage classification structural health monitoring in bolted structures using time-frequency techniques. Journal of Intelligent Material Systems and Structures, 20(11), 289-305.
Chen, W. Y., Song, Y., Bai, H., & Lin, E. Y., C. J.and Chang. (2011). Parallel spectral clustering in distributed systems. IEEE transactions on pattern analysis and machine intelligence, 33(3), 568-586.
Dean, J., & Ghemawat, S. (2008). Mapreduce: simplified data processing on large clusters. Communications of the ACM, 5(1), 107-113.
Desjardins, S. L., Londono, N. A., Lau, D. T., & Khoo, H. (2006). Real-time data processing, analysis and visualization for structural monitoring of the confederation bridge. Advances in Structural Engineering, 9(1), 141-157.
Farrar, C. R., Doebling, W., S., & Nix, D. A. (2001). Vibrationbased structural damage identification. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 359(1778), 131-149.
Gandhi, T., Chang, R., & Trivedi, M. M. (2007). Video and seismic sensor-based structural health monitoring: Framework, algorithms, and implementation. IEEE Transactions on intelligent transportation systems, 8(2), 169-180.
Jain, R., Kasturi, R., & Schunck, B. G. (1995). Machine vision (Vol. 5). McGraw-Hill New York.
Kallinikidou, E., Yun, H. B., Masri, S. F., Caffrey, J. P., & Sheng, L. H. (2013). Application of orthogonal decomposition approaches to long-term monitoring of infrastructure systems. Journal of Engineering Mechanics, 139(6), 678-690.
Kiepert, J., & Loo, S. M. (2012). A unified wireless sensor network framework. In Systems conference (syscon),2012 ieee international (p. 1-6).
López-Higuera, J. M., Cobo, L. R., Incera, A. Q., & Cobo, A. (2011). Fiber optic sensors in structural health monitoring. Journal of Lightwave Technology, 29(4), 587–608.
Mahadevan, S., Adams, D., & Kosson, D. (2012). Challenges in concrete structures health monitoring. In In proceedings, annual conference of the prognostics and health management society.
Nagy, P. B. (2016). Electromagnetic nondestructive evaluation. Ultrasonic and Electromagnetic NDE for Structure and Material Characterization: Engineering and Biomedical Applications, 169.
Nair, A., & Cai, C. S. (2010). Acoustic emission monitoring of bridges: Review and case studies. Engineering structures, 32(6), 1704-1714.
Naus, D. J. (2009). The management of aging in nuclear power plant concrete structures. Journal of Metals, 61(7), 35-41.
Rens, K. L., Wipf, T. J., & Klaiber, F. W. (1997). Review of nondestructive evaluation techniques of civil infrastructure. Journal of performance of constructed facilities, 11(4), 152-160.
Roux, S., Rthor, J., & Hild, F. (2009). Digital image correlation and fracture: an advanced technique for estimating stress intensity factors of 2d and 3d cracks. Journal of Physics D: Applied Physics, 42(21), 214004.
Sohn, H., Farrar, C., Hunter, N., & Worden, K. (2001, Jan.). Applying the lanl statistical pattern recognition paradigm for structural health monitoring to data from a surface-effect fast patrol boat (Tech. Rep.).
Yan, F., Royer, R. L., & Rose, J. L. (2010). Ultrasonic guided wave imaging techniques in structural health monitoring. Journal of Intelligent Material Systems and Structures, 21(3), 377-384.
Yu, L. (2012). Acoustic emission source localization on con-crete structures with focusing array imaging. In In 6th european workshop on structural health monitoring.
Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., Mc-Cauley, M., & Stoica, I. (2012, Apr.). Resilient distributed datasets: A fault-tolerant abstraction for inmemory cluster computing. In In proceedings of the 9th usenix conference on networked systems design and implementation (p. 2-2).
Zhang, J., Qiu, H., Shamsabadi, S. S., Birken, R., & Schirner, G. (2014, Jul.). Sirom3–a scalable intelligent roaming multi-modal multi-sensor framework. In Iin 38th ieee international conference on computers, software and applications (p. 446-455).
Section
Technical Papers