Efficient seismic response modeling for train-bridge systems: A time series mixer approach

IF 5.7 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software Pub Date : 2025-05-28 DOI:10.1016/j.advengsoft.2025.103957

Ping Xiang , Xiaonan Xie , Zhanjun Shao , Hongkai Ma , Peng Zhang , Han Zhao

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引用次数: 0

Abstract

This paper introduces an innovative surrogate modeling approach for predicting seismic responses in train-bridge coupled (TBC) systems, utilizing the Time Series Mixer (TSMixer) neural network. The model features a multi-layered architecture with sliding time windows, ensuring continuous, real-time analysis. We refined key performance metrics to address limitations in traditional response evaluation, particularly in handling phase discrepancies. The TSMixer model demonstrates high accuracy across a variety of seismic waves and system parameters, making it a valuable tool for rapid prediction in urban seismic scenarios. By offering swift and precise assessments, this approach has significant implications for improving the design efficiency of high-speed rail systems under seismic conditions. This research advances the field by introducing a robust method capable of capturing the nonlinear dynamics of TBC systems, fulfilling modern engineering demands for speed and precision in seismic analysis.

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列车-桥梁系统的有效地震反应建模：一种时间序列混合方法

本文介绍了一种利用时间序列混频器（TSMixer）神经网络预测列车-桥梁耦合（TBC）系统地震反应的创新代理建模方法。该模型具有多层结构和滑动时间窗口，确保了连续、实时的分析。我们改进了关键性能指标，以解决传统响应评估中的局限性，特别是在处理阶段差异方面。TSMixer模型在各种地震波和系统参数中都具有很高的精度，使其成为城市地震场景快速预测的宝贵工具。通过提供快速和精确的评估，该方法对提高高速铁路系统在地震条件下的设计效率具有重要意义。本研究通过引入一种能够捕获TBC系统非线性动力学的鲁棒方法推进了该领域的发展，满足了现代工程对地震分析速度和精度的要求。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Advances in Engineering Software 工程技术-计算机：跨学科应用

CiteScore

7.70

自引率

4.20%

发文量

169

审稿时长

37 days

期刊介绍： The objective of this journal is to communicate recent and projected advances in computer-based engineering techniques. The fields covered include mechanical, aerospace, civil and environmental engineering, with an emphasis on research and development leading to practical problem-solving. The scope of the journal includes: • Innovative computational strategies and numerical algorithms for large-scale engineering problems • Analysis and simulation techniques and systems • Model and mesh generation • Control of the accuracy, stability and efficiency of computational process • Exploitation of new computing environments (eg distributed hetergeneous and collaborative computing) • Advanced visualization techniques, virtual environments and prototyping • Applications of AI, knowledge-based systems, computational intelligence, including fuzzy logic, neural networks and evolutionary computations • Application of object-oriented technology to engineering problems • Intelligent human computer interfaces • Design automation, multidisciplinary design and optimization • CAD, CAE and integrated process and product development systems • Quality and reliability.