A Data-Driven Framework for the Crashworthiness Optimization of Coupled Trains with Time-Sequence Predictive Modeling

The pursuit of higher rail speeds and increasing traffic density have intensified collision risks, making effective crashworthiness design a critical safeguard for mitigating accident consequences. Modern multi-car trains present a particular challenge, as their safety during collisions is governed by complex inter-vehicle interactions. While finite element (FE) methods offer high accuracy in simulating these scenarios, their prohibitive computational cost has limited their utility in rapid safety design and optimization. To address this issue, four machine learning-based framework model for rapid prediction of structural dynamic response was proposed. The surrogate model is coupled with multi-body dynamics to enable efficient multi-objective optimization of train crashworthiness. The AE-GRU model demonstrated excellent convergence, with final losses below 0.0009. More importantly, when applied to largescale optimization involving 10,000 iterations, the computational cost was reduced to under 25 % of that required by conventional FE analysis. This approach provides a robust and efficient data-driven foundation for enhancing collision safety in modern train systems.