The spatial offset of bridge has a significant impact on the safety, comfort, and durability of high-speed railway (HSR) operations, so it is crucial to rapidly and effectively detect the spatial offset of operational HSR bridges. Drive-by monitoring of bridge uneven settlement demonstrates significant potential due to its practicality, cost-effectiveness, and efficiency. However, existing drive-by methods for detecting bridge offset have limitations such as reliance on a single data source, low detection accuracy, and the inability to identify lateral deformations of bridges. This paper proposes a novel drive-by inspection method for spatial offset of HSR bridge based on multi-source data fusion of comprehensive inspection train. Firstly, dung beetle optimizer-variational mode decomposition was employed to achieve adaptive decomposition of non-stationary dynamic signals, and explore the hidden temporal relationships in the data. Subsequently, a long short-term memory neural network was developed to achieve feature fusion of multi-source signal and accurate prediction of spatial settlement of HSR bridge. A dataset of track irregularities and CRH380A high-speed train responses was generated using a 3D train–track–bridge interaction model, and the accuracy and effectiveness of the proposed hybrid deep learning model were numerically validated. Finally, the reliability of the proposed drive-by inspection method was further validated by analyzing the actual measurement data obtained from comprehensive inspection train. The research findings indicate that the proposed approach enables rapid and accurate detection of spatial offset in HSR bridge, ensuring the long-term operational safety of HSR bridges.
| [1] |
Zhao W, Yang F, Zhi Yet al.. Subgrade settlement recognition of high-speed railway based on multi-source detection data fusion. J Railw Sci Eng, 2024, 21(1): 396-405(in Chinese)
|
| [2] |
Abdu DM, Wei G, Yang W. Assessment of railway bridge pier settlement based on train acceleration response using machine learning algorithms. Structures, 2023, 52: 598-608
|
| [3] |
Chen Z, Zhai W. Theoretical method of determining pier settlement limit value for China’s high-speed railway bridges considering complete factors. Eng Struct, 2020, 209 109998
|
| [4] |
Chen Z, Zhang N, Zhang X. Settlement monitoring system of pile-group foundation. J Cent South Univ Technol, 2011, 18(6): 2122-2130
|
| [5] |
Bonopera M, Chang KC, Chen CCet al.. Fiber Bragg grating–differential settlement measurement system for bridge displacement monitoring: case study. J Bridge Eng, 2019, 24(10): 05019011
|
| [6] |
Zhuo Y, Gu J, Wang X. Intelligent settlement monitoring system of high-speed railway bridge. J Civ Struct Health Monit, 2019, 93307-323
|
| [7] |
Du Y, He X, Wu Cet al.. Long-term monitoring and analysis of the longitudinal differential settlement of an expressway bridge-subgrade transition section in a loess area. Sci Rep, 2022, 12(1): 19327
|
| [8] |
Kaloop MR, Li H. Monitoring of bridge deformation using GPS technique. KSCE J Civ Eng, 2009, 136423-431
|
| [9] |
Ma P, Li T, Fang Cet al.. A tentative test for measuring the sub-millimeter settlement and uplift of a high-speed railway bridge using COSMO-SkyMed images. ISPRS J Photogramm Remote Sens, 2019, 155: 1-12
|
| [10] |
Zhou L, Li X, Pan Yet al.. Deformation monitoring of long-span railway bridges based on SBAS-InSAR technology. Geod Geodyn, 2024, 15(2): 122-132
|
| [11] |
Wei G, Lei C, Gao Met al.. Data assimilation of PS-InSAR vertical deformation into a frost heave model to analyze subgrade deformation of high-speed railway in northwest China. Cold Reg Sci Technol, 2024, 218 104059
|
| [12] |
Zhao Q, Hu C, Xia Get al.. Research of highway bridge settlement monitoring technology based on machine vision. J Res Sci Eng, 2024, 6(7): 29-32
|
| [13] |
Zhan Y, Huang Y, Fan Zet al.. (2024) Computer vision-based pier settlement displacement measurement of a multispan continuous concrete highway bridge under complex construction environments. Struct Contr Health Monit, 2024, 1: 1866665
|
| [14] |
Teng HL, Meriles JI, Su Get al.. Development of a vision-based embedded system for monitoring of bridge settlement. J Infrastruct Syst, 2024, 30(2): 04024003
|
| [15] |
de Souza EF, Bragança C, Ribeiro Det al.. Drive-by damage detection methodology for high-speed railway bridges using sparse autoencoders. Railw Eng Sci, 2024
|
| [16] |
Wang ZL, Yang JP, Shi Ket al.. Recent advances in researches on vehicle scanning method for bridges. Int J Str Stab Dyn, 2022, 22152230005
|
| [17] |
Xu H, Chen XY, Chen Jet al.. Review of vehicle scanning method for bridges from 2004 to 2024. Int J Str Stab Dyn, 2024
|
| [18] |
He Y, Li Y, Xu L (2024) An integrated multisource and multiscale monitoring technique for assessing the health status of high-speed railway subgrade. Remote Sens 16(11):1972
|
| [19] |
Xu W, Guo Y, You M. Intelligent identification of differential subgrade settlement of ballastless track system based on vehicle dynamic responses and 1D-CNN approach. Transp Geotech, 2024, 48 101302
|
| [20] |
Li Q, Dai BR, Yang Fet al.. Overview on existing and developing methods for track irregularity detection. J China Railw Soc, 2024, 467101-116(in Chinese)
|
| [21] |
Liu L, Su Y, Zhu Jet al.. Data fusion based EKF-UI for real-time simultaneous identification of structural systems and unknown external inputs. Measurement, 2016, 88: 456-467
|
| [22] |
Jaramillo VH, Ottewill JR, Dudek Ret al.. Condition monitoring of distributed systems using two-stage Bayesian inference data fusion. Mech Syst Signal Process, 2017, 87: 91-110
|
| [23] |
Chen C, Chen Q, Li Get al.. A novel multi-source data fusion method based on Bayesian inference for accurate estimation of chlorophyll-a concentration over eutrophic lakes. Environ Model Softw, 2021, 141 105057
|
| [24] |
Stover JA, Hall DL, Gibson RE. A fuzzy-logic architecture for autonomous multi-sensor data fusion. IEEE Trans Ind Electron, 2002, 43(3): 403-410
|
| [25] |
Fasano L, Latini D, Machidon Aet al.. SAR data fusion using nonlinear principal component analysis. IEEE Geosci Remote Sens Lett, 2020, 17(9): 1543-1547
|
| [26] |
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput, 1997, 9(8): 1735-1780
|
| [27] |
Shan J, Zhang X, Liu Yet al.. Deformation prediction of large-scale civil structures using spatiotemporal clustering and empirical mode decomposition-based long short-term memory network. Autom Constr, 2024, 158 105222
|
| [28] |
Xu Y, Yao W, Zheng Xet al.. A hybrid Monte Carlo quantile EMD-LSTM method for satellite in-orbit temperature prediction and data uncertainty quantification. Expert Syst Appl, 2024, 255 124875
|
| [29] |
Johny K, Pai ML, Adarsh S. A multivariate EMD-LSTM model aided with time dependent intrinsic cross-correlation for monthly rainfall prediction. Appl Soft Comput, 2022, 123 108941
|
| [30] |
Zhang Y, Li C, Jiang Yet al.. Accurate prediction of water quality in urban drainage network with integrated EMD-LSTM model. J Clean Prod, 2022, 354 131724
|
| [31] |
Hao W, Sun X, Wang Cet al.. A hybrid EMD-LSTM model for non-stationary wave prediction in offshore China. Ocean Eng, 2022, 246 110566
|
| [32] |
Chen Z, Zhai W, Yin Q. Analysis of structural stresses of tracks and vehicle dynamic responses in train–track–bridge system with pier settlement. Proc Inst Mech Eng Part F J Rail Rapid Transit, 2018, 232(2): 421-434
|
| [33] |
Dragomiretskiy K, Zosso D. Variational mode decomposition. IEEE Trans Signal Process, 2013, 62(3): 531-544
|
| [34] |
Xue J, Shen B. Dung beetle optimizer: a new meta-heuristic algorithm for global optimization. J Supercomput, 2022, 79(7): 7305-7336
|
| [35] |
Zhai W. Vehicle–track coupled dynamics models, 2021, Singapore, Springer
|
| [36] |
Zhai W, Xia H, Cai Cet al.. High-speed train–track–bridge dynamic interactions–Part I: theoretical model and numerical simulation. Int J Rail Transp, 2013, 1(1–2): 3-24
|
| [37] |
Zhai W, Wang S, Zhang Net al.. High-speed train–track–bridge dynamic interactions–Part II: experimental validation and engineering application. Int J Rail Transp, 2013, 1(1–2): 25-41
|
| [38] |
Zhai W, Wang K, Lin J. Modelling and experiment of railway ballast vibrations. J Sound Vib, 2004, 270(4–5): 673-683
|
| [39] |
Wang C, Zhan J, Wang Yet al.. Condition monitoring and quantitative evaluation of railway bridge substructures using vehicle-induced vibration responses by sparse measurement. Struct Contr Health Monit, 2024, 2024(1): 3271106
|
| [40] |
Li Y, Xiang H, Wang Zet al.. A comprehensive review on coupling vibrations of train–bridge systems under external excitations. Railw Eng Sci, 2022, 303383-401
|
| [41] |
Chen Z, Zhai W, Cai Cet al.. Safety threshold of high-speed railway pier settlement based on train-track-bridge dynamic interaction. Sci China Technol Sci, 2015, 58(2): 202-210
|
| [42] |
National Railway Administration (2014) PSD of ballastless track irregularities on high-speed railways. TB/T 3352-2014. China Railway Publishing House, Beijing
|
| [43] |
Xia H, Zhang N, Guo W. Dynamic interaction of train-bridge systems in high-speed railways, 2018, Berlin Heidelberg, Springer
|
| [44] |
Xue J, Shen B. A novel swarm intelligence optimization approach: sparrow search algorithm. Syst Sci Contr Eng, 2020, 8(1): 22-34
|
| [45] |
Qiao D, Li P, Ma Get al.. Realtime prediction of dynamic mooring lines responses with LSTM neural network model. Ocean Eng, 2021, 219 108368
|
| [46] |
Kingma D, Ba J (2015) Adam: A method for stochastic optimization. In: Proceedings of the 3rd International Conference for Learning Representations. San Diego, pp 1–15
|
Funding
National Natural Science Foundation of China(52178100)
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The Author(s)
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