铁路有矿道床横向阻力智能感知研究

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中图分类号：TB9;U270.1 文献标志码：A 文章编号：1674-5124（2025）09-0044-07

Abstract： The traditional ofline detection method for lateral resistance of ballasted bed is time-consuming， labor-intensive，and has certain destructive effects on the track. The development of intelligent perception method for ballast bed lateral resistance holds significant implications for enhancing railway maintenance and operation efficiency. This study proposes an intellgent perception algorithm for ballast bed lateral resistance that integrates mechanistic modeling of stabilization operations with machine learning algorithm.The proposed method accurately characterizes the 11-DOF lateral coupling interactions among the dynamic track stabilizer， stabilization devices， and ballsted track. An RBF surrogate model-optimized parameter identification approach isdeveloped，integrating field test data to establish anend-to-end deep neural network （DNN） machine learning model correlating dynamic parameters with ballast bed lateral resistance.This hybrid mechanistic-data-driven framework enables real-time monitoring of lateral resistance.The research results indicate that the proposed real-time perception method for lateral resistance of the ballsted bed has good accuracy， with an absolute error of 2.13%-18.38% between the perception results and the measured data. Keywords： dynamic track stabilizer; ballasted bed lateral resistance; dynamics model; parameter identification; machine learning

0 引言

有砟轨道在车辆循环载荷和外部环境多种因素耦合作用下会发生累积变形和力学特性恶化，对列车运行安全造成重大威胁[1]。（剩余8040字）