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Optimizing Railway Track Tamping and Geometry Fine-Tuning Allocation Using a Neural Network-Based Solver
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-16 , DOI: 10.1016/j.autcon.2024.105958
Congyang Xu, Huakun Sun, Siyuan Zhou, Zhiting Chang, Yanhua Guo, Ping Wang, Weijun Wu, Qing He
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-16 , DOI: 10.1016/j.autcon.2024.105958
Congyang Xu, Huakun Sun, Siyuan Zhou, Zhiting Chang, Yanhua Guo, Ping Wang, Weijun Wu, Qing He
This paper introduces a Neural Network Solver (NNS) for Railway Geometry Rectification Linear Program Model (RGRLPM), integrating tamping and fine-tuning operations for millimeter-precision adjustments. The NNS, enhanced by a grad norm process for faster convergence, achieves rectification plans three times faster than the simplex method. Dynamic programming is applied to allocate adjustments between tamping and fine-tuning. Experiments reveal that reducing 10 m and 5/30 m chord offset limits to 0.4 times improves dynamic performance over manual schemes. At a 0.2 reduction factor, cumulative rectification decreases by 5.6%, and the Sperling index drops by 26.9%, highlighting superior efficiency and dynamic outcomes.
中文翻译:
使用基于神经网络的求解器优化铁路轨道夯实和几何微调分配
本文介绍了用于铁路几何校正线性规划模型 (RGRLPM) 的神经网络求解器 (NNS),它集成了夯实和微调操作,以实现毫米级精度调整。NNS 通过分级范数过程得到增强,可实现更快的收敛,实现校正计划的速度比单纯形法快三倍。动态编程用于在夯实和微调之间分配调整。实验表明,与手动方案相比,将 10 m 和 5/30 m 和弦偏移限制降低到 0.4 倍可以提高动态性能。在 0.2 的折减因子下,累积整流降低 5.6%,Sperling 指数下降 26.9%,突出了卓越的效率和动态结果。
更新日期:2025-01-16
中文翻译:
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使用基于神经网络的求解器优化铁路轨道夯实和几何微调分配
本文介绍了用于铁路几何校正线性规划模型 (RGRLPM) 的神经网络求解器 (NNS),它集成了夯实和微调操作,以实现毫米级精度调整。NNS 通过分级范数过程得到增强,可实现更快的收敛,实现校正计划的速度比单纯形法快三倍。动态编程用于在夯实和微调之间分配调整。实验表明,与手动方案相比,将 10 m 和 5/30 m 和弦偏移限制降低到 0.4 倍可以提高动态性能。在 0.2 的折减因子下,累积整流降低 5.6%,Sperling 指数下降 26.9%,突出了卓越的效率和动态结果。