2019, Volume 5, Issue 2
Engineering >> 2019, Volume 5, Issue 2 doi: 10.1016/j.eng.2018.11.027
The State of the Art of Data Science and Engineering in Structural Health Monitoring
School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China
Next Previous
Abstract
Structural health monitoring (SHM) is a multi-discipline field that involves the automatic sensing of structural loads and response by means of a large number of sensors and instruments, followed by a diagnosis of the structural health based on the collected data. Because an SHM system implemented into a structure automatically senses, evaluates, and warns about structural conditions in real time, massive data are a significant feature of SHM. The techniques related to massive data are referred to as data science and engineering, and include acquisition techniques, transition techniques, management techniques, and processing and mining algorithms for massive data. This paper provides a brief review of the state of the art of data science and engineering in SHM as investigated by these authors, and covers the compressive sampling-based data-acquisition algorithm, the anomaly data diagnosis approach using a deep learning algorithm, crack identification approaches using computer vision techniques, and condition assessment approaches for bridges using machine learning algorithms. Future trends are discussed in the conclusion.
Keywords
Structural health monitoring ; Monitoring data ; Compressive sampling ; Machine learning ; Deep learning
Figures
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
References
[ 1 ] Li H, Ou J, Zhang X, Pei M, Li N. Research and practice of health monitoring for long-span bridges in the mainland of China. Smart Struct Syst 2015;15 (3):555–76. link1
[ 2 ] Ou J, Li H. Structural health monitoring in mainland China: review and future trends. Struct Health Monit 2010;9(3):219–31. link1
[ 3 ] Spencer BF, Ruiz-Sandoval ME, Kurata N. Smart sensing technology: opportunities and challenges. Struct Control Health Monit 2010;11(4):349–68. link1
[ 4 ] Wang H, Tao T, Li A, Zhang Y. Structural health monitoring system for Sutong cable-stayed bridge. Smart Struct Syst 2016;18(2):317–34. link1
[ 5 ] Chang PC, Flatau A, Liu SC. Health monitoring of civil infrastructure. Struct Health Monit 2003;2(3):257–67. link1
[ 6 ] Mufti AA. Structural health monitoring of innovative Canadian civil engineering structures. Struct Health Monit 2002;1(1):89–103. link1
[ 7 ] Ko JM, Ni YQ. Technology developments in structural health monitoring of large-scale bridges. Eng Struct 2005;27(12):1715–25. link1
[ 8 ] He X, Hua X, Chen Z, Huang F. EMD-based random decrement technique for modal parameter identification of an existing railway bridge. Eng Struct 2011;33(4):1348–56. link1
[ 9 ] He X, Fang J, Scanlon A, Chen Z. Wavelet-based nonstationary wind speed model in Dongting Lake cable-stayed bridge. Engineering (Lond) 2010;2 (11):895–903. link1
[10] Li H. SHM data science and engineering. In: Proceedings of the 5th Asia-Pacific Workshop on Structural Health Monitoring; 2014 Dec 4–5; Shenzhen, China. Beijing: Science Press; 2014. link1
[11] Baraniuk RG. Compressive sensing [lecture notes]. IEEE Signal Process Mag 2007;24(4):118–21. link1
[12] Donoho DL. Compressed sensing. IEEE Trans Inf Theory 2006;52 (4):1289–306. link1
[13] Candès EJ. Compressive Sampling. In: Proceedings of the International Congress of Mathematicians; 2006 Aug 22–30; Madrid, Spain; 2006. p. 1433–52.
[14] Bao Y, Beck JL, Li H. Compressive sampling for accelerometer signals in structural health monitoring. Struct Health Monit 2011;10(3):235–46. link1
[15] Peckens CA, Lynch JP. Utilizing the cochlea as a bio-inspired compressive sensing technique. Smart Mater Struct 2013;22(10):105027. link1
[16] O’Connor SM, Lynch JP, Gilbert AC. Compressed sensing embedded in an operational wireless sensor network to achieve energy efficiency in long-term monitoring applications. Smart Mater Struct 2014;23(8):085014. link1
[17] Huang Y, Beck JL, Wu S, Li H. Bayesian compressive sensing for approximately sparse signals and application to structural health monitoring signals for data loss recovery. Probab Eng Mech 2016;46:62–79. link1
[18] Bao Y, Shi Z, Wang X, Li H. Compressive sensing of wireless sensors based on group sparse optimization for structural health monitoring. Struct Health Monit 2018;17(4):823–36. link1
[19] Bao Y, Li H, Sun X, Yu Y, Ou J. Compressive sampling based data loss recovery for wireless sensor networks used in civil structural health monitoring. Struct Health Monit 2013;12(1):78–95. link1
[20] Zou Z, Bao Y, Li H, Spencer BF, Ou J. Embedding compressive sensing based data loss recovery algorithm into wireless smart sensors for structural health monitoring. IEEE Sens J 2015;15(2):797–808. link1
[21] Park JY, Wakin MB, Gilbert AC. Modal analysis with compressive measurements. IEEE Trans Signal Process 2014;62(7):1655–70. link1
[22] Yang Y, Nagarajaiah S. Output-only modal identification by compressed sensing: non-uniform low-rate random sampling. Mech Syst Signal Process 2015;56–57:15–34. link1
[23] Mascareñas D, Cattaneo A, Theiler J, Farrar C. Compressed sensing techniques for detecting damage in structures. Struct Health Monit 2013;12(4):325–38. link1
[24] Wang Y, Hao H. Damage identification scheme based on compressive sensing. J Comput Civ Eng 2015;29(2):04014037. link1
[25] Yao R, Pakzad SN, Venkitasubramaniam P. Compressive sensing based structural damage detection and localization using theoretical and metaheuristic statistics. Struct Control Health Monit 2017;24(4):e1881. link1
[26] Zhou S, Bao Y, Li H. Structural damage identification based on substructure sensitivity and l1 sparse regularization. In: Proceedings of the SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring; 2013 Mar 10–14; San Diego, CA, USA; 2013.
[27] Zhou XQ, Xia Y, Weng S. L1 regularization approach to structural damage detection using frequency data. Struct Health Monit 2015;14(6):571–82. link1
[28] Zhang CD, Xu YL. Comparative studies on damage identification with Tikhonov regularization and sparse regularization. Struct Control Health Monit 2016;23 (3):560–79. link1
[29] Hou R, Xia Y, Bao Y, Zhou X. Selection of regularization parameter for l1- regularized damage detection. J Sound Vibrat 2018;423:141–60. link1
[30] Bao Y, Li H, Chen Z, Zhang F, Guo A. Sparse l1 optimization-based identification approach for the distribution of moving heavy vehicle loads on cable-stayed bridges. Struct Control Health Monit 2016;23(1):144–55. link1
[31] Yuen KV, Mu HQ. A novel probabilistic method for robust parametric identification and outlier detection. Probab Eng Mech 2012;30(4):48–59. link1
[32] Yuen KV, Ortiz GA. Outlier detection and robust regression for correlated data. Comput Methods Appl Math 2017;313:632–46. link1
[33] Liu H, Shah S, Jiang W. On-line outlier detection and data cleaning. Comput Chem Eng 2004;28(9):1635–47. link1
[34] Gul M, Catbas FN. Statistical pattern recognition for structural health monitoring using time series modeling: theory and experimental verifications. Mech Syst Signal Process 2009;23(7):2192–204. link1
[35] Zhang Y, Meratnia N, Havinga PJ. Outlier detection techniques for wireless sensor networks: a survey. IEEE Comm Surv and Tutor 2010;12(2):159–70. link1
[36] Chang CM, Chou JY, Tan P, Wang L. A sensor fault detection strategy for structural health monitoring systems. Smart Struct Syst 2017;20(1):43–52. link1
[37] Kullaa J. Detection, identification, and quantification of sensor fault in a sensor network. Mech Syst Signal Process 2013;40(1):208–21. link1
[38] Peng C, Fu Y, Spencer BF. Sensor fault detection, identification, and recovery techniques for wireless sensor networks: a full-scale study. In: Proceedings of the 13th International Workshop on Advanced Smart Materials and Smart Structures Technology; 2017 Jul 22–23; Tokyo, Japan; 2017.
[39] Ni K, Ramanathan N, Chehade MNH, Balzano L, Nair S, Zahedi S, et al. Sensor network data fault types. ACM Trans Sens Network 2009;5(3):29. link1
[40] Luo Y, Ye Z, Guo X, Qiang X, Chen X. Data missing mechanism and missing data real-time processing methods in the construction monitoring of steel structures. Adv Struct Eng 2015;18(4):585–601. link1
[41] Yang Y, Nagarajaiah S. Harnessing data structure for recovery of randomly missing structural vibration responses time history: sparse representation versus low-rank structure. Mech Syst Signal Process 2016;74:165–82. link1
[42] Zhang Z, Luo Y. Restoring method for missing data of spatial structural stress monitoring based on correlation. Mech Syst Signal Process 2017;91:266–77. link1
[43] Chen Z, Bao Y, Li H, Spencer BF. A novel distribution regression approach for data loss compensation in structural health monitoring. Struct Health Monit 2018;17(6):1473–90. link1
[44] Chen Z, Li H, Bao Y. Analyzing and modeling inter-sensor relationships for strain monitoring data and missing data imputation: a copula and functional data-analytic approach. Struct Health Monit. Epub 2018 Jul 25. link1
[45] Chen Z, Bao Y, Li H, Spencer BF. LQD-RKHS-based distribution-to-distribution regression methodology for restoring the probability distributions of missing SHM data. Mech Syst Signal Process 2019;121:655–74. link1
[46] Sohn H, Farrar CR, Hemez F, Czarnecki J. A review of structural health monitoring literature 1996–2001. Los Alamos: Los Alamos National Laboratory; 2003. link1
[47] Jung HJ. Bridge inspection and condition assessment using unmanned aerial vehicles and deep learning. In: Proceedings of the 7th World Conference on Structural Control and Monitoring; 2018 Jul 22–25; Qingdao, China; 2018.
[48] Li S, Wei S, Bao Y, Li H. Condition assessment of cables by pattern recognition of vehicle-induced cable tension ratio. Eng Struct 2018;155:1–15. link1
[49] Wei S, Zhang Z, Li S, Li H. Strain features and condition assessment of orthotropic steel deck cable-supported bridges subjected to vehicle loads by using dense FBG strain sensors. Smart Mater Struct 2017;26 (10):104007. link1
[50] Bao Y, Tang Z, Li H, Zhang Y. Computer vision and deep learning-based data anomaly detection method for structural health monitoring. Struct Health Monit. Epub 2018 Feb 19. link1
[51] Xu Y, Li S, Zhang D, Jin Y, Zhang F, Li N, et al. Identification framework for cracks on a steel structure surface by a restricted Boltzmann machines algorithm based on consumer-grade camera images. Struct Control Health Monit 2018;25(2):e2075. link1
[52] Fischer A, Igel C. Empirical analysis of the divergence of Gibbs sampling based learning algorithms for restricted Boltzmann machines. In: Proceedings of the 20th International Conference on Artificial Neural Networks: Part III; 2010 Sep 15-18; Thessaloniki, Greece. Berlin: Springer; 2010. p. 208–17. link1
[53] Liu Y, Cheng MM, Hu X, Wang K, Bai X. Richer convolutional features for edge detection. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition; 2017 Jul 21–26; Honolulu, HI, USA.
[54] Xu Y, Bao Y, Chen J, Zuo W, Li H. Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images. Struct Health Monit. Epub 2018 Apr 2. link1
京公网安备 11010502051620号
