Study on Data-Driven Identification Method of Hinge Joint Damage under Moving Vehicle Excitation
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 9, Issue 4
Abstract
The hinge joint is an important and fragile component of assembled hollow-slab bridges. Therefore, it is necessary to regularly identify hinge joint damage for guaranteeing the safety of assembled hollow-slab bridges. However, conventional hinge joint damage identification methods are time consuming and expensive. Therefore, this study proposes a data-driven hinge joint damage identification method under moving vehicle excitation to quantitatively identify hinge joint damage conveniently. First, we established a refined finite-element model of a hollow-slab bridge with damaged hinge joints and analyze the dynamic response of the bridge under vehicle loads. The Pearson correlation coefficient between the acceleration time history of the adjacent slabs was proposed as the damage index. Further, an ensemble learning algorithm called gradient boosted regression trees (GBRT) was employed to develop a model for identifying hinged joint damage. Finally, the performance of the model was thoroughly compared with commonly utilized machine-learning algorithms and the auto-encoder-based method. The results show that the proposed model exhibits the highest accuracy. Under different signal-to-noise ratio conditions, the model’s coefficient of determination () is always above 0.85, the mean absolute error (MAE) is below 4.40 cm, and the root mean squared error (RMSE) is below 7.91 cm. This confirms the feasibility of the model for quantitative and convenient identification of the damage height of hinged joints.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors greatly appreciate the financial support from National Key R&D Program of China (2021YFB1600302), Young Scientists Fund of China (Nos. 52078119 and 52008027), and General Project Supported by Natural Science Basic Research Plan in Shaanxi Province of China (No. 2021JQ-269).
References
Abdo, M. A. B. 2012. “Parametric study of using only static response in structural damage detection.” Eng. Struct. 34 (Jan): 124–131. https://doi.org/10.1016/j.engstruct.2011.09.027.
Al-saidy, A. H., F. W. Klaiber, T. J. Wipf, K. S. Al-Jabri, and A. S. Al-Nuaimi. 2008. “Parametric study on the behavior of short span composite bridge girders strengthened with carbon fiber reinforced polymer plates.” Constr. Build. Mater. 22 (5): 729–737. https://doi.org/10.1016/j.conbuildmat.2007.01.020.
Aydin, K., and O. Kisi. 2014. “Applicability of a fuzzy genetic system for crack diagnosis in Timoshenko beams.” J. Comput. Civ. Eng. 33 (7): 37–43. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000385.
Azizinamini, A. 2020. “Accelerated bridge construction.” J. Bridge Eng. 25 (12): 02020002. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001643.
Bousquet, O., and A. Elisseeff. 2002. “Stability and generalization.” J. Mach. Learn. Res. 2 (Mar): 499–526. https://doi.org/10.1162/153244302760200704.
Caddemi, S., and A. Morassi. 2011. “Detecting multiple open cracks in elastic beams by static tests.” J. Eng. Mech. 137 (2): 113–124. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000209.
Chen, S. Z., and D. C. Feng. 2022. “Multifidelity approach for data-driven prediction models of structural behaviors with limited data.” Comput.-Aided Civ. Inf. Eng. 37 (12): 1566–1581. https://doi.org/10.1111/mice.12817.
Chen, S. Z., D. C. Feng, and S. Zhen. 2021. “Reliability-based vehicle weight limit determination for urban bridge network subjected to stochastic traffic flow considering vehicle-bridge coupling.” Eng. Struct. 247 (Nov): 113116. https://doi.org/10.1016/j.engstruct.2021.113166.
Chen, S. Z., G. Wu, and D. C. Feng. 2019. “Damage detection of highway bridges based on long-gauge strain response under stochastic traffic flow.” Mech. Syst. Signal Process. 127 (Jul): 551–572. https://doi.org/10.1016/j.ymssp.2019.03.022.
Chen, S. Z., G. Wu, D. C. Feng, Z. Wang, and X. Y. Cao. 2020. “Multi-cross-reference method for highway-bridge damage identification based on long-gauge fiber Bragg-grating sensors.” J. Bridge Eng. 25 (6): 04020023. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001542.
Dan, D. H., Z. Xu, K. Zhang, and X. Yan. 2019. “Monitoring index of transverse collaborative working performance of assembled beam bridges based on transverse modal shape.” Int. J. Struct. Stab. Dyn. 19 (8): 1950086. https://doi.org/10.1142/S021945541950086X.
Hearn, G., and R. B. Testa. 1991. “Modal analysis for damage detection in structures.” J. Struct. Eng. 117 (10): 3042–3063. https://doi.org/10.1061/(ASCE)0733-9445(1991)117:10(3042).
ISO. 1995. Mechanical vibration–road surface profiles–reporting of measured data. ISO 8608. Geneva: ISO.
Jiang, H., X. Dong, Z. Fang, J. Xiao, and Y. Chen. 2020. “Experimental study on shear behavior of a UHPC connection between adjacent precast prestressed concrete voided beams.” J. Bridge Eng. 25 (12): 04020106. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001644.
Jiang, K., Q. Han, X. Du, and P. Ni. 2021. “A decentralized unsupervised structural condition diagnosis approach using deep auto-encoders.” Comput.-Aided Civ. Inf. Eng. 36 (6): 711–732. https://doi.org/10.1111/mice.12641.
Kawaguchi, K., and Y. Bengio. 2018. “Generalization in machine learning via analytical learning theory.” Preprint, submitted February 21, 2018. http://arxiv.org/abs/1802.07426.
Kim, J. T., Y. S. Ryu, H. M. Cho, H. M. Cho, and N. Stubbs. 2003. “Damage identification in beam-type structures: Frequency-based method vs mode-shape-based method.” Eng. Struct. 25 (1): 57–67. https://doi.org/10.1016/S0141-0296(02)00118-9.
Li, Z., W. Lin, and Y. Zhang. 2023. “Real-time drive-by bridge damage detection using deep auto-encoder.” Structures 47 (Mar): 1167–1181. https://doi.org/10.1016/j.istruc.2022.11.094.
Liu, H., X. He, and Y. Jiao. 2018. “Damage identification algorithm of hinged joints for simply supported slab bridges based on modified hinge plate method and artificial bee colony algorithms.” Algorithms 11 (12): 198–212. https://doi.org/10.3390/a11120198.
Liu, X. D., W. S. Hai, X. L. Guo, Y. G. Yuan, and S. Z. Chen. 2022. “Fatigue lifespan assessment of stay cables by a refined joint probability density model of wind speed and direction.” Eng. Struct. 252 (Feb): 113608. https://doi.org/10.1016/j.engstruct.2021.113608.
Ma, X., Y. Lin, Z. Nie, and H. Ma. 2020. “Structural damage identification based on unsupervised feature-extraction via variational auto-encoder.” Measurement 160 (Aug): 107811. https://doi.org/10.1016/j.measurement.2020.107811.
Pandey, A. K., and M. Biswas. 1991. “Damage detection from changes in curvature modes shapes.” J. Sound Vib. 145 (2): 321–332. https://doi.org/10.1016/0022-460X(91)90595-B.
Rehman, S. K. U., Z. Ibrahim, S. A. Memon, and M. Jameel. 2016. “Nondestructive test methods for concrete bridges: A review.” Constr. Build. Mater. 107 (Mar): 58–86. https://doi.org/10.1016/j.conbuildmat.2015.12.011.
Teng, H. W., J. J. Jiang, and D. Guo. 2006. “Structure damage diagnosis based wavelet transform.” J. Wuhan Univ. Technol. 10 (2): 58–60.
Wang, J., C. Chen, H. Xiang, and X. Fan. 2017. “Performance of the transverse connectivity in simply supported girder bridges and its strengthening strategy.” J. Perform. Constr. Facil. 31 (5): 04017081. https://doi.org/10.1061/(ASCE)CF.1943-5509.0001071.
Wang, Q., Q. X. Wu, and B. C. Chen. 2014. “Experimental study on failure mode of hinged joint in assembly voided slab bridge.” Eng. Mech. 2021 (247): 113166.
Weinstein, J. C., M. Sanayei, and B. R. Brenner. 2018. “Bridge damage identification using artificial neural networks.” J. Bridge Eng. 23 (11): 04018084. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001302.
Zhan, J. W., S. X. Gao, F. Zhang, and Z. G. Liang. 2018a. “Damage evaluation method for hinged joints in slab-girder bridges using online dynamic responses.” China J. Highway Transp. 31 (7): 156–166.
Zhan, J. W., D. D. Wang, S. X. Gao, and K. Liu. 2018b. “A dynamic damage evaluation method for hinged joints in fabricated slab-girder bridges.” China Civ. Eng. J. 51 (6): 103–110.
Zhang, J., T. H. Yi, C. X. Qu, and H. N. Li. 2022. “Detecting hinge joint damage in hollow slab bridges using mode shapes extracted from vehicle response.” J. Perform. Constr. Facil. 36 (1). https://doi.org/10.1061/(asce)cf.1943-5509.0001694.
Zhou, Z. M., G. F. Yuan, and Q. Y. Tian. 2013. “Evaluation method for hinge joint damage in multi-slab girder bridge based on stiffness of hinge joint.” China J. Highway Transp. 26 (5): 121–130.
Zou, Y. S., B. B. Yuan, Y. H. Wang, and C. Chen. 2011. “Damage identification of hinged joints of prefabricated slab bridges based on transient dynamics analysis.” J. Chongqing Jiaotong Univ. 30 (1): 1–3.
Information & Authors
Information
Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 9 • Issue 4 • December 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Nov 2, 2022
Accepted: Jul 6, 2023
Published online: Aug 30, 2023
Published in print: Dec 1, 2023
Discussion open until: Jan 30, 2024
ASCE Technical Topics:
- Bridge components
- Bridge engineering
- Bridge management
- Bridge-vehicle interaction
- Construction engineering
- Construction methods
- Continuum mechanics
- Dynamic loads
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering mechanics
- Errors (statistics)
- Excitation (physics)
- Fastening
- Finite element method
- Hinges
- Joints
- Mathematics
- Methodology (by type)
- Motion (dynamics)
- Numerical methods
- Solid mechanics
- Statistics
- Structural dynamics
- Structural engineering
- Structural members
- Structural systems
Authors
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