Innovative approaches in high-speed railway bridge model simplification for enhanced computational efficiency

作     者：ZHOU Wang-bao XIONG Li-jun JIANG Li-zhong ZHONG Bu-fan 周旺保;熊利军;蒋丽忠;钟不凡

作者机构：School of Civil EngineeringCentral South UniversityChangsha 410075China National Engineering Research Center of High-speed Railway Construction TechnologyChangsha 410075China 

出 版 物：《Journal of Central South University》 (中南大学学报(英文版))

年 卷 期：2024年第31卷第11期

页      面：4203-4217页

核心收录：

学科分类：070801[理学-固体地球物理学] 07[理学] 0708[理学-地球物理学] 

基　　金：Project(2022YFC3004304)supported by the National Key Research and Development Program of China Projects(52078487,U1934207,52178180)supported by the National Natural Science Foundation of China Project(2022TJ-Y10)supported by the Hunan Province Science and Technology Talent Lifting Project,China Project(2023QYJC006)supported by the Frontier Cross Research Project of Central South University,China Project(SKL-IoTSC(UM)-2024-2026/ORP/GA08/2023)supported by the Science and Technology Development Fund and the State Key Laboratory of Internet of Things for Smart City(University of Macao),China 

主　　题：high-speed railway bridge engineering track-bridge system model simplified bridge model artificial neural networks particle swarm optimization seismic analysis 

摘      要：In the realm of high-speed railway bridge engineering,managing the intricacies of the track-bridge system model(TBSM)during seismic events remains a formidable *** study pioneers an innovative approach by presenting a simplified bridge model(SBM)optimized for both computational efficiency and precise representation,a seminal contribution to the engineering design *** to this innovation is a novel model-updating methodology that synergistically melds artificial neural networks with an augmented particle swarm *** neural networks adeptly map update parameters to seismic responses,while enhancements to the particle swarm algorithm’s inertial and learning weights lead to superior SBM parameter *** via a 4-span high-speed railway bridge revealed that the optimized SBM and TBSM exhibit a highly consistent structural natural period and seismic response,with errors controlled within 7%.Additionally,the computational efficiency improved by over 100%.Leveraging the peak displacement and shear force residuals from the seismic TBSM and SBM as optimization objectives,SBM parameters are adeptly ***,the incorporation of elastoplastic springs at the beam ends of the simplified model effectively captures the additional mass,stiffness,and constraint effects exerted by the track system on the bridge structure.