原文传递
Prediction of Track Deterioration Using Maintenance Data and Machine Learning Schemes
| 题名： | Prediction of Track Deterioration Using Maintenance Data and Machine Learning Schemes |
| 其他题名： | UIC(Union Internationale des Chemins de Fer).2009.Classification of lines for the purpose of track maintenance.UIC Code 714 R.Paris:UIC. |
| 正文语种： | 英文 |
| 作者： | Jun S. Lee |
| 关键词： | Track maintenance;Machine learning;Artificial neural networks;Support vector regression;Track quality index;Decision support system |
| 摘要： | The maintenance and renewal of ballasted track can be optimized in terms of time and cost if a proper statistical model of track deterioration is derived from previous maintenance history and measurement data. In this regard, quite a few models with simplified assumptions on the parameters have been suggested for the deterioration of ballasted track. Meanwhile, data driven models such as the artificial neural network (ANN) and support vector regression (SVR), which are basic ingredients of machine learning (ML) technology, were introduced in this study to better represent the deterioration phenomena of track segments so that the results can be directly plugged into the optimization schemes. For this purpose, the influential parameters of track deterioration have been selected based on the maintenance history, and two ML models have been studied to find the best combination of input parameters. Through numerical experiments, it was found that at least 2 years of maintenance data were needed in our case to obtain a stable prediction of track deterioration. |
| 出版年： | 2018 |
| 论文唯一标识： | P-72Y2018V144N09005 |
| 英文栏目名称： | TECHNICAL PAPERS |
| doi： | 10.1061/JTEPBS.0000173 |
| 期刊名称： | Journal of Transportation Engineering |
| 拼音刊名(出版物代码)： | P-72 |
| 卷： | 144 |
| 期： | 09 |
| 页码： | 23-31 |
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