Physics-guided life-cycle maintenance framework for rail corrugation

Abstract

Rail corrugation, a significant source of noise and reduced ride comfort, is mitigated by costly regular grinding. Developing optimal, cost-effective maintenance strategies is challenging due to complex mechanisms and various influential factors. This research introduces a life cycle maintenance (LCM) framework integrating a physical corrugation analysis model with a multi-property effect function under budgetary and railway standard constraints. The framework combines maintenance strategies and economic factors into a multi-objective optimisation problem, solved via a genetic algorithm, to identify optimal solutions across various operational scenarios. It also quantifies the impact of train operation schedules, offering insights for future management decisions. A suburban rail system case study demonstrates the framework’s adaptability by examining three engineering scenarios. Simulation results show that the framework can boost standard compliance ratios and rail quality while reducing maintenance budgets by up to 40%, 48%, and 64%, respectively. The findings illustrate the framework’s capability to optimise rail corrugation maintenance and improve train operation management across practical scenarios.