Multi-objective Optimization of Railway Transition Zones with Machine Learning
Application to Prefabricated Epoxy Asphalt Cured Track Bed
You Wu (TU Delft - Railway Engineering)
Chenguang Shi (Chongqing Jiaotong University)
Yunhong Yu (Southeast University)
Yulou Fan (Southeast University)
Jun Yang (Southeast University)
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Abstract
Transition zones in high-speed railways suffer from abrupt stiffness variations that induce irregular dynamic responses and accelerate infrastructure deterioration. This study presents a surrogate-assisted multi-objective optimization framework that combines finite element (FE) simulations, a neural network-based surrogate model, and the NSGA-II algorithm to address this challenge. A validated 3D FE model of prefabricated epoxy asphalt cured track beds (PEACT) was used to generate 341 layout scenarios covering 13 response parameters. These data were used to train a neural network, which served as a static surrogate predictor for evaluating layout performance during the optimization process. The results show that module layout has a limited effect on peak responses but significantly improves smoothness, with three categories of optimal configurations identified. Compared with direct FE-based optimization, the proposed framework achieves substantial computational efficiency and provides data-driven design guidance for PEACT transition zones. This framework exemplifies the potential of hybrid data–simulation approaches to enhance adaptive and efficient railway infrastructure design.
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