Vision and Support Vector Machine–Based Train Classification Using Weigh-in-Motion Data
Publication: Journal of Bridge Engineering
Volume 27, Issue 6
Abstract
Trains with a different number of carriages can induce stress responses with varying amplitudes in the long-span steel bridges, which consequently cause different levels of fatigue damage. To better evaluate the fatigue life of bridges, it is important to obtain the volume and types of different trains running on bridges. To overcome errors in identifying the different trains caused by electronic noise and, to more efficiently utilize machine learning techniques, the original train weigh-in-motion (WIM) time series are encoded into images. Subsequently, a support vector machine (SVM) based approach is proposed to classify trains with a different number of carriages. The method is divided into three steps: data conversion for image preprocessing, feature extraction for machine learning, and train category classification with SVM. In the image preprocessing step, the time history of the WIM train passing data is saved into image format. In the feature extraction step, the Histogram of Oriented Gradients (HOG) is obtained in row vectors for each image as input for machine learning. In the train carriage classification step, SVM is adopted as the machine learning model to predict different train types. To verify the proposed approach, train WIM data from the structural health monitoring (SHM) system of a suspension bridge are employed, and an accuracy of 97.5% is achieved in the classification of trains when considering noisy datasets. Compared with other state-of-the-art machine learning algorithms, i.e., AdaBoost, K-Nearest Neighbor (KNN), and Linear Classification (LC) Model, the SVM leads to the highest prediction accuracy and shortest computation time.
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Acknowledgments
This work was financially supported by Project POCI-01-0145-FEDER-007457, CONSTRUCT, Institute of R&D In Structures and Construction funded by FEDER funds through COMPETE2020, Programa Operacional Competitividade e Internacionalização (POCI) and by national funds through FCT, Fundação para a Ciência e a Tecnologia, and by the Project SAFESUSPENSE, Safety Control and Management of Long-Span Suspension Bridges (reference POCI-01-0145-FEDER-031054), funded by COMPETE 2020, POR Lisboa and FCT. The authors thank LNEC for providing the monitoring data.
References
Anaissi, A., N. L. D. Khoa, S. Mustapha, M. M. Alamdari, A. Braytee, Y. Wang, and F. Chen. 2017. “Adaptive one-class support vector machine for damage detection in structural health monitoring.” In Vol. 10234 of Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science, edited by J. Kim, K. Shim, L. Cao, J. G. Lee, X. Lin, and Y. S. Moon, 42–57. Cham, Switzerland: Springer.
Caetano, E., S. Silva, and J. Bateira. 2011. “A vision system for vibration monitoring of civil engineering structures.” Exp. Tech. 35: 74–82. https://doi.org/10.1111/j.1747-1567.2010.00653.x.
Dalal, N., and B. Triggs. 2005. “Histograms of oriented gradients for human detection.” In Vol. 1 of IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR’05), 886–893. Piscataway, NJ: IEEE.
Deng, L., L. Nie, W. Zhong, and W. Wang. 2021. “Developing fatigue vehicle models for bridge fatigue assessment under different traffic conditions.” J. Bridge Eng. 26 (2): 04020122. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001675.
Deng, Y., M. Zhang, D. M. Feng, and A. Q. Li. 2020. “Predicting fatigue damage of highway suspension bridge hangers using weigh-in-motion data and machine learning.” Struct. Infrastruct. Eng. 17 (2): 1–16.
Feng, D. C., W. J. Wang, S. Mangalathu, G. Hu, and T. Wu. 2021. “Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements.” Eng. Struct. 235: 111979. https://doi.org/10.1016/j.engstruct.2021.111979.
Feng, D. M., and M. Q. Feng. 2018. “Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection—A review.” Eng. Struct. 156: 105–117. https://doi.org/10.1016/j.engstruct.2017.11.018.
Fu, B., and D. C. Feng. 2021. “A machine learning-based time-dependent shear strength model for corroded reinforced concrete beams.” J. Build. Eng. 36: 102118. https://doi.org/10.1016/j.jobe.2020.102118.
Fujino, Y., B. Pacheco, S. Nakamura, and W. Pennung. 1993. “Synchronization of human walking observed during lateral vibration of a congested pedestrian bridge.” Earthquake Eng. Struct. Dyn. 22: 741–758. https://doi.org/10.1002/eqe.4290220902.
Gunnstein, T. F., and R. Anders. 2019. “Evolution of load conditions in the Norwegian railway network and imprecision of historic railway load data.” Struct. Infrastruct. Eng. 15 (2): 152–169. https://doi.org/10.1080/15732479.2018.1504087.
Hayward, A. C. G. 2011. “Train loads on bridges 1825 to 2010.” Int. J. Hist. Eng. Technol. 81 (2): 159–191. https://doi.org/10.1179/175812111X13033852943273.
Ho, H., and M. Nishio. 2020. “Evaluation of dynamic responses of bridges considering traffic flow and surface roughness.” Eng. Struct. 225: 111256. https://doi.org/10.1016/j.engstruct.2020.111256.
Hu, W. H., Á Cunha, E. Caetano, R. G. Rohrmann, S. Said, and J. Teng. 2017. “Comparison of different statistical approaches for removing environmental/operational effects for massive data continuously collected from footbridges.” Struct. Control Health Monit. 24 (8): e1955. https://doi.org/10.1002/stc.1955.
Kong, X., and J. Li. 2018. “Vision-based fatigue crack detection of steel structures using video feature tracking.” Comput.-Aided Civ. Infrastruct. Eng. 33: 783–799. https://doi.org/10.1111/mice.12353.
Laory, I., T. N. Trinh, I. F. Smith, and J. M. Brownjohn. 2014. “Methodologies for predicting natural frequency variation of a suspension bridge.” Eng. Struct. 80: 211–221. https://doi.org/10.1016/j.engstruct.2014.09.001.
Lu, N., M. Noori, and Y. Liu. 2017. “Fatigue reliability assessment of welded steel bridge decks under stochastic truck loads via machine learning.” J. Bridge Eng. 22 (1): 04016105. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000982.
Marques, F., C. Moutinho, W. H. Hu, Á Cunha, and E. Caetano. 2016. “Weigh-in-motion implementation in an old metallic railway bridge.” Eng. Struct. 123: 15–29. https://doi.org/10.1016/j.engstruct.2016.05.016.
Marques, F., C. Moutinho, F. Magalhães, E. Caetano, and Á Cunha. 2014. “Analysis of dynamic and fatigue effects in an old metallic riveted bridge.” J. Constr. Steel Res. 99: 85–101. https://doi.org/10.1016/j.jcsr.2014.04.010.
Mustapha, S., A. Braytee, and L. Ye. 2018. “Multisource data fusion for classification of surface cracks in steel pipes.” J. Nondestr. Eval. Diagn. Progn. Eng. Syst. 1 (2): 021007. https://doi.org/10.1115/1.4038862.
Mustapha, S., A. Kassir, K. Hassoun, B. A. A. Modad, H. Abi-Rached, and Z. Dawy. 2019. “Joint crowd management and structural health monitoring using fiber optic and wearable sensing.” IEEE Commun. Mag. 57 (4): 62–67. https://doi.org/10.1109/MCOM.2019.1800631.
Narazaki, Y., V. Hoskere, K. Yoshida, B. F. Spencer, and Y. Fujino. 2021. “Synthetic environments for vision-based structural condition assessment of Japanese high-speed railway viaducts.” Mech. Syst. Sig. Process. 160: 107850. https://doi.org/10.1016/j.ymssp.2021.107850.
Ni, Y. Q., X. G. Hua, K. Q. Fan, and J. M. Ko. 2005. “Correlating modal properties with temperature using long-term monitoring data and support vector machine technique.” Eng. Struct. 27: 1762–1773. https://doi.org/10.1016/j.engstruct.2005.02.020.
Nishikawa, T., J. Yoshida, T. Sugiyama, and Y. Fujino. 2012. “Concrete crack detection by multiple sequential image filtering.” Comput.-Aided Civ. Infrastruct. Eng. 27 (1): 29–47. https://doi.org/10.1111/j.1467-8667.2011.00716.x.
Qu, C. X., T. H. Yi, X. J. Yao, and H. N. Li. 2021. “Complex frequency identification using real modal shapes for a structure with proportional damping.” Comput.-Aided Civ. Infrastruct. 2021: 1–15.
Santos, J. P., C. Crémona, L. Calado, P. Silveira, and A. D. Orcesi. 2016. “On-line unsupervised detection of early damage.” Struct. Control Health Monit. 23 (7): 1047–1069. https://doi.org/10.1002/stc.1825.
Santos, J. P., C. Cremona, A. D. Orcesi, and P. Silveira. 2017. “Early damage detection based on pattern recognition and data fusion.” J. Struct. Eng. 143 (2): 04016162. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001643.
Santos, J., C. Crémona, and P. Silveira. 2020. “Automatic operational modal analysis of complex civil infrastructures.” Struct. Eng. Int. 30 (3): 365–380. https://doi.org/10.1080/10168664.2020.1749012.
Siringoringo, D. M., S. Wangchuk, and Y. Fujino. 2021. “Noncontact operational modal analysis of light poles by vision-based motion-magnification method.” Eng. Struct. 244: 112728. https://doi.org/10.1016/j.engstruct.2021.112728.
Song, Y. S., Y. L. Ding, W. Zhong, and H. Zhao. 2018. “Reliable fatigue-life assessment of short steel hanger in a rigid tied arch bridge integrating multiple factors.” J. Perform. Constr. Facil 32 (4): 04018038. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001183.
Spencer, B. F., V. Hoskere, and Y. Narazaki. 2019. “Advances in computer vision-based civil infrastructure inspection and monitoring.” Engineering 5 (2): 199–222. https://doi.org/10.1016/j.eng.2018.11.030.
Sun, L., Z. Shang, Y. Xia, S. Bhowmick, and S. Nagarajaiah. 2020. “Review of bridge structural health monitoring aided by big data and artificial intelligence: From condition assessment to damage detection.” J. Struct. Eng. 146 (5): 04020073. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002535.
Sun, Z., T. Nagayama, and Y. Fujino. 2016. “Minimizing noise effect in curvature-based damage detection.” J. Civ. Struct. Health Monit. 6 (2): 255–264. https://doi.org/10.1007/s13349-016-0163-x.
Sun, Z., S. Ning, and Y. Shen. 2017. “Failure investigation and replacement implementation of short suspenders in a suspension bridge.” J. Bridge Eng. 22 (8): 05017007. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001089.
Sun, Z., and H. Sun. 2018. “Jiangyin bridge: An example of integrating structural health monitoring with bridge maintenance.” Struct. Eng. Int. 28 (3): 353–356. https://doi.org/10.1080/10168664.2018.1462671.
Sun, Z., and Z. L. Zou. 2016. “Towards an efficient method of predicting vehicle-induced response of bridge.” Eng. Comput. 33 (7): 2067–2089. https://doi.org/10.1108/EC-02-2015-0034.
Vapnik, V. 1999. The nature of statistical learning theory. New York: Springer.
Wang, F., S. Ning, and Z. Sun. 2020. “Experimental investigation on wear resistance of bushing in bridge suspenders.” J. Perform. Constr. Facil 34 (3): 06020001. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001429.
Wang, H., Q. Zhu, J. Li, J. Mao, S. Hu, and X. Zhao. 2019. “Identification of moving train loads on railway bridge based on strain monitoring.” Smart Struct. Syst. 23 (3): 263–278.
Wu, L., Y. Su, Z. Chen, S. Chen, S. Cheng, and P. Lin. 2020. “Six-degree-of-freedom generalized displacements measurement based on binocular vision.” Struct. Control Health Monit. 27 (2): e2458.
Xu, Y., and J. M. W. Brownjohn. 2018. “Review of machine-vision based methodologies for displacement measurement in civil structures.” J. Civ. Struct. Health Monit. 8 (1): 91–110. https://doi.org/10.1007/s13349-017-0261-4.
Xu, Y., K. Imou, Y. Kaizu, and K. Saga. 2013. “Two-stage approach for detecting slightly overlapping strawberries using HOG descriptor.” Biosyst. Eng. 115 (2): 144–153. https://doi.org/10.1016/j.biosystemseng.2013.03.011.
Ye, X. W., T. Jin, and B. C. Yun. 2019. “A review on deep learning-based structural health monitoring of civil infrastructures.” Smart Struct. Syst. 24 (5): 567–585.
Zaurin, R., T. Khuc, and F. N. Catbas. 2016. “Hybrid sensor-camera monitoring for damage detection: Case study of a real bridge.” J. Bridge Eng. 21 (6): 05016002. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000811.
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Journal of Bridge Engineering
Volume 27 • Issue 6 • June 2022
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© 2022 American Society of Civil Engineers.
History
Received: Jul 7, 2021
Accepted: Feb 14, 2022
Published online: Mar 29, 2022
Published in print: Jun 1, 2022
Discussion open until: Aug 29, 2022
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