Abstract
This study investigates the use of big data analytics in uncertainty quantification and applies the proposed framework to structural diagnosis and prognosis. With smart sensor technology making progress and low-cost online monitoring becoming increasingly possible, large quantities of data can be acquired during monitoring, thus exceeding the capacity of traditional data analytics techniques. The authors explore a software application technique to parallelize data analytics and efficiently handle the high volume, velocity, and variety of sensor data. Next, both forward and inverse problems in uncertainty quantification are investigated with this efficient computational approach. The authors use Bayesian methods for the inverse problem of diagnosis and parallelize numerical integration techniques such as Markov-chain Monte Carlo simulation and particle filter. To predict damage growth and the structure’s remaining useful life (forward problem), Monte Carlo simulation is used to propagate the uncertainties (both aleatory and epistemic) to the future state. The software approach is again applied to drive the parallelization of multiple finite-element analysis (FEA) runs, thus greatly saving on the computational cost. The proposed techniques are illustrated for the efficient diagnosis and prognosis of alkali-silica reactions in a concrete structure.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This study was partly supported by funding from the U.S. Department of Energy (DOE) through the Light Water Reactor Sustainability (LWRS) Program (Monitors: Vivek Agarwal and Bruce Hallbert, Idaho National Lab, Idaho Falls, Idaho). The support is gratefully acknowledged.
References
Abaqus version 6.14 [Computer software]. Dassault Systemes, Velizy-Villacoublay, France.
Araujo, A., et al. (2012). “Wireless measurement system for structural health monitoring with high time-synchronization accuracy.” IEEE Trans. Instrum. Meas., 61(3), 801–810.
Bao, Y., Beck, J. L., and Li, H. (2011). “Compressive sampling for accelerometer signals in structural health monitoring.” Struct. Health Monit., 10(3), 235–246.
Cai, G., and Mahadevan, S. (2016). “Big data analytics in structural health monitoring.” Int. J. Prognostics Health Manage., 7, 24.
Chakraborty, D., et al. (2009). “Damage classification structural health monitoring in bolted structures using time-frequency techniques.” J. Intell. Mater. Syst. Struct., 20(11), 1289–1305.
Chen, W.-Y., Song, Y., Bai, H., Lin, C.-J., and Chang, E. Y. (2011). “Parallel spectral clustering in distributed systems.” IEEE Trans. Pattern Anal. Mach. Intell., 33(3), 568–586.
Dean, J., and Ghemawat, S. (2008). “MapReduce: Simplified data processing on large clusters.” Commun. ACM, 51(1), 107–113.
Doucet, A., De Freitas, N., Murphy, K., and Russell, S. (2000). “Rao-Blackwellised particle filtering for dynamic Bayesian networks.” Proc., 16th Conf. on Uncertainty in Artificial Intelligence, Morgan Kaufmann, Burlington, MA, 176–183.
Farrah, S., Ziyati, H. E. M. E. H., and Ouzzif, M. (2015). “An approach to analyze large scale wireless sensors network data.” Measurements, 2(5), 7–12.
Farrar, C. R., and Worden, K. (2007). “An introduction to structural health monitoring.” Philos. Trans. R. Soc. London A, 365(1851), 303–315.
FLIR IR [Computer software]. FLIR Systems, Inc., Wilsonville, OR.
Gandhi, T., Chang, R., and Trivedi, M. M. (2007). “Video and seismic sensor-based structural health monitoring: Framework, algorithms, and implementation.” IEEE Trans. Intell. Transp. Syst., 8(2), 169–180.
Hadoop [Computer software]. Apache Software Foundation, Forest Hill, MD.
Haldar, A., and Mahadevan, S. (2000). Probability, reliability, and statistical methods in engineering design, Vol. 1, Wiley, New York.
Kallinikidou, E., Yun, H.-B., Masri, S. F., Caffrey, J. P., and Sheng, L.-H. (2013). “Application of orthogonal decomposition approaches to long-term monitoring of infrastructure systems.” J. Eng. Mech., 678–690.
Kezia, S. P., and Mary, A. V. A. (2016). “Prediction of rapid floods from big data using map reduce technique.” Global J. Pure Appl. Math., 12(1), 369–373.
Kiepert, J., and Loo, S. M. (2012). “A unified wireless sensor network framework.” 2012 IEEE Int. Systems Conf., IEEE, New York, 1–6.
Mahadevan, S., Adams, D., and Kosson, D. (2014). “Challenges in concrete structures health monitoring.” Int. J. Prognostics Health Manage., 5, 57.
Meeker, W. Q., and Hong, Y. (2014). “Reliability meets big data: Opportunities and challenges.” Qual. Eng., 26(1), 102–116.
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E. (1953). “Equation of state calculations by fast computing machines.” J. Chem. Phys., 21(6), 1087–1092.
Nannapaneni, S., and Mahadevan, S. (2016). “Reliability analysis under epistemic uncertainty.” Reliab. Eng. Syst. Saf., 155, 9–20.
Neal, R. M. (2003). “Slice sampling.” Ann. Stat., 31(3), 705–767.
Neiswanger, W., Wang, C., and Xing, E. (2013). “Asymptotically exact, embarrassingly parallel MCMC.” ArXiv preprint arXiv:1311.4780.
Orlande, H., et al. (2011). “Tutorial 10 Kalman and particle filters.” Advanced spring school: Thermal measurements and inverse techniques, METTI5, Roscoff, France, 5.
Papasalouros, D., et al. (2012). “Health monitoring of a neg-micon nm wind turbine blade with acoustic emission.” Proc., 30th European Conf. on Acoustic Emission and 7th Int. Conf. on Acoustic Emission, Granada, Spain, 12–15.
Roberts, G. O., and Rosenthal, J. S. (2006). “Harris recurrence of metropolis-within-Gibbs and trans-dimensional Markov chains.” Ann. Appl. Probab., 16(4), 2123–2139.
Roshandeh, A. M., Poormirzaee, R., and Ansari, F. S. (2014). “Systematic data management for real-time bridge health monitoring using layered big data and cloud computing.” Int. J. Innov. Sci. Res., 2(1), 29–39.
Saltelli, A., et al. (2008). Global sensitivity analysis: The primer, Wiley, New York.
Sankararaman, S., and Mahadevan, S. (2013). “Bayesian methodology for diagnosis uncertainty quantification and health monitoring.” Struct. Control Health Monit., 20(1), 88–106.
Sankararaman, S., and Mahadevan, S. (2015). “Integration of model verification, validation, and calibration for uncertainty quantification in engineering systems.” Reliab. Eng. Syst. Saf., 138, 194–209.
Saouma, V., and Perotti, L. (2006). “Constitutive model for alkali-aggregate reactions.” ACI Mater. J., 103(3), 194.
Sohn, H., Farrar, C., Hunter, N., and Worden, K. (2001). “Applying the LANL statistical pattern recognition paradigm for structural health monitoring to data from a surface-effect fast patrol boat.”, Los Alamos National Laboratory, Los Alamos, NM.
Spark version 2.1.0 [Computer software]. Apache Software Foundation, Forest Hill, MD.
Tran, C. (2016). “Structural-damage detection with big data using parallel computing based on MPSOC.” Int. J. Mach. Learn. Cybern., 7(6), 1213–1233.
Ulm, F.-J., Coussy, O., Kefei, L., and Larive, C. (2000). “Thermo-chemo-mechanics of ASR expansion in concrete structures.” J. Eng. Mech., 233–242.
Yu, L. (2012). “Acoustic emission source localization on concrete structures with focusing array imaging.” 6th European Workshop on Structural Health Monitoring, Univ. of South Carolina, Columbia, SC.
Yu, L., and Lin, J.-C. (2015). “Cloud computing-based time series analysis for structural damage detection.” J. Eng. Mech., C4015002.
Zaharia, M., et al. (2012). “Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing.” Proc., 9th USENIX Conf. on Networked Systems Design and Implementation, Univ. of California, Berkeley, CA, 2.
Zhang, J., Qiu, H., Shamsabadi, S. S., Birken, R., and Schirner, G. (2014). “Sirom3–a scalable intelligent roaming multi-modal multi-sensor framework.” IEEE 38th Annual Computer Software and Applications Conf., IEEE, New York, 446–455.
Zhong, L., Tang, K., Li, L., Yang, G., and Ye, J. (2014). “An improved clustering algorithm of tunnel monitoring data for cloud computing.” Sci. World J., 2014, 1.
Information & Authors
Information
Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 4 • Issue 1 • March 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Dec 19, 2016
Accepted: Aug 16, 2017
Published online: Jan 16, 2018
Published in print: Mar 1, 2018
Discussion open until: Jun 16, 2018
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.