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.

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