基于机器学习的重载铁路轨道质量指数分析方法OA

To address the low prediction accuracy of the Track Quality Index(TQI)in heavy-haul rail-ways,this study proposes an analysis method based on machine learning.First,a raw dataset compris-ing 303 381 track inspection and maintenance records collected between 2015 and 2023 from 2 906 sec-tions of a heavy-haul railway operated by a major energy enterprise is utilized.Following data cleaning,the dataset was categorized into four track section types based on geometric characteristics:neither gradi-ents nor curves,curves without gradients,gradients without curves,and both gradients and curves.Sec-ond,the data for each category were processed into sequential datasets spanning three consecutive months without maintenance interventions.Sequences exhibiting an excessively large range in the TQI were then eliminated using anomaly detection methods.Third,using the TQI and its seven components(left-rail longitudinal level,right-rail longitudinal level,left-rail alignment,right-rail alignment,gauge,cross level,and track twist in triangle form)from the first two months of each sequence as inputs,six machine learning models,such as Recurrent Neural Network(RNN),Long Short-Term Memory(LSTM),Gated Recurrent Unit(GRU),Multi-Layer Perceptron(MLP),Support Vector Machine(SVM),and linear regression,are leveraged to perform rolling predictions of the TQI for the third month.Finally,the predictive performance of the different models across various section types is comparatively analyzed.The results indicate that when using the six machine learning models to predict the TQI across the four section types,the coefficient of determination R² between the measured and predicted values ex-ceeds 0.94 for all models,suggesting highly accurate and reliable prediction performance.Among the six models,the RNN demonstrates particularly outstanding performance,achieving an average R² of 0.967 5 with a standard deviation of±0.000 8 across various test sets.Specifically,for the four representative track sections,the prediction R² values are 0.990 1,0.974 3,0.988 3,and 0.988 9,respectively.Com-pared to the other five models,the RNN yields more stable prediction results and exhibits greater adapt-ability to diverse scenarios.This study demonstrates that machine learning methods effectively meet the demands of TQI prediction in heavy-haul railways,providing a valuable reference for establishing scien-tific tamping maintenance schedules and advancing intelligent track maintenance practices.