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Optimizing the Rail Profile for High-Speed Railways Based on Artificial Neural Network and Genetic Algorithm Coupled Method

School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China
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Sustainability 2020, 12(2), 658; https://doi.org/10.3390/su12020658 (registering DOI)
Received: 2 January 2020 / Revised: 14 January 2020 / Accepted: 14 January 2020 / Published: 16 January 2020
(This article belongs to the Section Sustainable Transportation)
Though the high-speed railways are seen as a sustainable form of transportation, the fact that the rail wear in high-speed railways negatively affects the running safety and riding comfort, as well as the maintenance of railways, has drawn a wide range of concerns among researchers and scholars. In order to reduce the rail wear and achieve the goal of sustainable transportation, this paper proposes an ingenious optimization program of rail profiles based on the artificial neural network (ANN) and genetic algorithm (GA) coupled method. The candidate solutions of the nonlinear GA programming model are regarded as the inputs of the trained ANN model. Meanwhile, the outputs of the trained ANN model serve as the objective functions of the GA model. The computational results show that the optimized rail profile not only has superior performances in terms of the wheel/rail wear and contact conditions, but also maintains good dynamic performances. Therefore, this study can provide the theoretical and practical basis for the design and the preventive grinding of rails in the high-speed railways. Also, the ANN-GA coupled model can be extended and further employed on the optimization of other rail profiles. View Full-Text
Keywords: artificial neural network; dynamic performance; genetic algorithm; high-speed railway; rail profile optimization; rail wear artificial neural network; dynamic performance; genetic algorithm; high-speed railway; rail profile optimization; rail wear
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Jiang, H.; Gao, L. Optimizing the Rail Profile for High-Speed Railways Based on Artificial Neural Network and Genetic Algorithm Coupled Method. Sustainability 2020, 12, 658.

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