Enhancing reliability assessment of curved low-stiffness track-viaducts with an adaptive surrogate-based approach emphasizing track dynamic geometric state

Abstract

Traditional track dynamic geometric state (TDGS) simulation incurs substantial computational burdens, posing challenges for developing reliability assessment approach that accounts for TDGS. To overcome these, firstly, a simulation-based TDGS model is established, and a surrogate-based model, grid search algorithm-particle swarm optimization-genetic algorithm-multi-output least squares support vector regression, is established. Among them, hyperparameter optimization algorithm’s effectiveness is confirmed through test functions. Subsequently, an adaptive surrogate-based probability density evolution method (PDEM) considering random track geometry irregularity (TGI) is developed. Finally, taking curved train-steel spring floating slab track-U beam as case study, the surrogate-based model trained on simulation datasets not only shows accuracy in both time and frequency domains, but also surpasses existing models. Additionally, the adaptive surrogate-based PDEM shows high accuracy and efficiency, outperforming Monte Carlo simulation and simulation-based PDEM. The reliability assessment shows that the TDGS part peak management indexes, left/right vertical dynamic irregularity, right alignment dynamic irregularity, and track twist, have reliability values of 0.9648, 0.9918, 0.9978, and 0.9901, respectively. The TDGS mean management index, i.e., track quality index, has reliability value of 0.9950. These findings show that the proposed framework can accurately and efficiently assess the reliability of curved low-stiffness track-viaducts, providing a theoretical basis for the TGI maintenance.

References

[[1]] Chen Z-W. Relationship between track stiffness and dynamic performance of vehicle-track-bridge system [J]. Vehicle System Dynamics, 2021, 59(12): 1825-1843

[[2]] Li H-J, Yan Z, Qin X-G, et al.. The research of arma prediction model on the prediction of railway track irregularity state [C]. International Conference on Computer Science and Artificial Intelligence (ICCSAI), 2013 26-30 Chengdu, China

[[3]] Xin T, Famurewa S M, Gao L, et al.. Grey-system-theory-based model for the prediction of track geometry quality [J]. Proceedings of the Institution of Mechanical Engineers, 2016, 230(7): 1735-1744

[[4]] Lasisi A, Attoh-Okine N. Principal components analysis and track quality index: A machine learning approach [J]. Transportation Research, Part C: Emerging Technologies, 2018, 91: 230-248

[[5]] Ren J-J, Zhang K-Y, Zheng J-L, et al.. Railway subgrade thermal-hydro-mechanical behavior and track irregularity under the sunny-shady slopes effect in seasonal frozen regions [J]. Journal of Central South University, 2022, 29(11): 3793-3810

[[6]] Jiang H, Zeng C, Peng Q, et al.. Running safety and seismic optimization of a fault-crossing simply-supported girder bridge for high-speed railways based on a train-track-bridge coupling system [J]. Journal of Central South University, 2022, 29(8): 2449-2466

[[7]] Gao T-C, Cong J-L, Wang P, et al.. Vertical track irregularity analysis of high-speed railways on simply-supported beam bridges based on the virtual track inspection method [J]. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 2021, 235(3): 328-338

[[8]] Cheng F, Wei K, Zhao Z-M, et al.. Track dynamic geometry state prediction for straight/curved low-stiffness urban rail transit viaduct lines based on virtual track inspection method [J]. Vehicle System Dynamics, 2022, 60(12): 4245-4268

[[9]] Li P-P, Wang Y. An active learning reliability analysis method using adaptive Bayesian compressive sensing and Monte Carlo simulation (ABCS-MCS) [J]. Reliability Engineering & System Safety, 2022, 221: 108377

[[10]] Yang J-S, Jensen H, Chen J-B. Structural optimization under dynamic reliability constraints utilizing probability density evolution method and metamodels in augmented input space [J]. Structural and Multidisciplinary Optimization, 2022, 65(4): 107

[[11]] Xu L, Zhai W-M. Probabilistic assessment of railway vehicle-curved track systems considering track random irregularities [J]. Vehicle System Dynamics, 2018, 56(10): 1552-1576

[[12]] Sajedi S, Liang X. Deep generative Bayesian optimization for sensor placement in structural health monitoring [J]. Computer-Aided Civil and Infrastructure Engineering, 2022, 37(9): 1109-1127

[[13]] Yuan Y, Au F T K, Yang D, et al.. Active learning structural model updating of a multisensory system based on Kriging method and Bayesian inference [J]. Computer-Aided Civil and Infrastructure Engineering, 2023, 38(3): 353-371

[[14]] Chen L-J, Hong Y, Mangalathu S, et al.. Adaptive sampling approach based on Jensen-Shannon divergence for efficient reliability analysis [J]. Journal of Central South University, 2021, 28(8): 2407-2422

[[15]] Echard B, Gayton N, Lemaire M. AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation [J]. Structural Safety, 2011, 33(2): 145-154

[[16]] Echard B, Gayton N, Lemaire M, et al.. A combined importance sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models [J]. Reliability Engineering & System Safety, 2013, 111: 232-240

[[17]] Tong C, Sun Z-L, Zhao Q-L, et al.. A hybrid algorithm for reliability analysis combining Kriging and subset simulation importance sampling [J]. Journal of Mechanical Science and Technology, 2015, 29(8): 3183-3193

[[18]] Huang X-X, Chen J-Q, Zhu H-P. Assessing small failure probabilities by AK–SS: An active learning method combining Kriging and subset simulation [J]. Structural Safety, 2016, 59: 86-95

[[19]] Zhou T, Peng Y-B. Reliability analysis using adaptive Polynomial-Chaos Kriging and probability density evolution method [J]. Reliability Engineering & System Safety, 2022, 220: 108283

[[20]] HU Yun, XIAO Ceng-di, SHI Ya-ying. Reliability analysis for highly non-linear and complex model using ANN-MCM simulation [J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2018, 40(5). DOI: https://doi.org/10.1007/s40430-018-1163-z.

[[21]] Pan Q-J, Dias D. An efficient reliability method combining adaptive support vector machine and Monte Carlo simulation [J]. Structural Safety, 2017, 67: 85-95

[[22]] Nguyen N, Wang W C, Cao M T. Early estimation of the long-term deflection of reinforced concrete beams using surrogate models [J]. Construction and Building Materials, 2023 370

[[23]] He Y-C, Yang K, Wang X-Q, et al.. Quality prediction and parameter optimisation of resistance spot welding using machine learning [J]. Applied Sciences–Basel, 2022, 12(19): 9625

[[24]] Yuan F, Che J-X. An ensemble multi-step MRMLSSVR model based on VMD and two-group strategy for day-ahead short-term load forecasting [J]. Knowledge-Based Systems, 2022 252

[[25]] Zhu M-Y, Chen B, Gu C-S, et al.. Optimized multi-output LSSVR displacement monitoring model for super high arch dams based on dimensionality reduction of measured dam temperature field [J]. Engineering Structures, 2022, 268: 114686

[[26]] Sun X, Wang P, Xu J-M, et al.. Simulation of the inherent structural irregularities of high-speed railway turnouts based on a virtual track inspection method [J]. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 2023, 237(1): 114-123

[[27]] Park B, Choi Y T, Kim H. Improvement and in situ application of an evaluation method of ballasted-track condition using digital 2-D image analysis [J]. Applied Sciences, 2020, 10(21): 7946

[[28]] Xu S, An X, Qiao X-D, et al.. Multi-output least-squares support vector regression machines [J]. Pattern Recognition Letters, 2013, 34(9): 1078-1084

[[29]] Luo Y-J, Cui Y-A, Xie J, et al.. Inversion of self-potential anomalies caused by simple polarized bodies based on particle swarm optimization [J]. Journal of Central South University, 2021, 28(6): 1797-1812

[[30]] Chen S. Multi-output regression using a locally regularised orthogonal least-squares algorithm [J]. IEE Proceedings-Vision, Image, and Signal Processing, 2002, 149(4): 185

[[31]] Chen J-B, Sun W-L, Li J, et al.. Stochastic harmonic function representation of stochastic processes [J]. Journal of Applied Mechanics, 2013, 80(1): 011001

[[32]] Assigned Transport 2019 No. 17. Code for Technical specification for safety assessment before initial operation of urban mass transit-Part 1: Subway and light rail [S], 2019, Beijing: China, General Office of the Ministry of Transport (in Chinese)

[[33]] Cheng K, Lu Z-Z. Active learning Bayesian support vector regression model for global approximation [J]. Information Sciences, 2021, 544: 549-563

[[34]] Zhai W-M. Vehicle-track coupled dynamics models [M]. Vehicle-Track Coupled Dynamics, 2019, Singapore, Springer Singapore 17-149