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

Abstract

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 challenge. This 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 landscape. Central to this innovation is a novel model-updating methodology that synergistically melds artificial neural networks with an augmented particle swarm optimization. The 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 updates. Verification 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 revised. Furthermore, 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.

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