Prediction of Metro Train-Induced Tunnel Vibrations Using Machine Learning Method
The tunnel vibration level is usually employed as a vibration source intensity of the empirical prediction method. Currently, the analogy test and data base are two main means to determine the vibration source intensity. To improve the accuracy efficiency, the machine learning (ML) method was introd...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/4031050 |
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| _version_ | 1832554780064481280 |
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| author | Zhuosheng Xu Meng Ma Zikai Zhou Xintong Xie Haoxiang Xie Bolong Jiang Zhongshuai Zhang |
| author_facet | Zhuosheng Xu Meng Ma Zikai Zhou Xintong Xie Haoxiang Xie Bolong Jiang Zhongshuai Zhang |
| author_sort | Zhuosheng Xu |
| collection | DOAJ |
| description | The tunnel vibration level is usually employed as a vibration source intensity of the empirical prediction method. Currently, the analogy test and data base are two main means to determine the vibration source intensity. To improve the accuracy efficiency, the machine learning (ML) method was introduced to predict the tunnel vibration responses. To acquire model training samples, the measurements were performed in 80 different running tunnel sections of Beijing metro lines. Two types of method, back propagation neural network (BPNN) and generalised regression neural network (GRNN) were employed, which can make full use of characteristics of measured samples and reduce the data noise. The results indicate that the prediction efficiency is high and the mean square errors of the two ML methods are acceptable. Accordingly, both of the ML methods can be used as the reference of vibration source intensity in metro train-induced environmental impact evaluation. GRNN has relatively better predicting ability than BPNN. |
| format | Article |
| id | doaj-art-53c5c6406c4a4003bc792cf253840d73 |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-53c5c6406c4a4003bc792cf253840d732025-02-03T05:50:31ZengWileyAdvances in Civil Engineering1687-80942022-01-01202210.1155/2022/4031050Prediction of Metro Train-Induced Tunnel Vibrations Using Machine Learning MethodZhuosheng Xu0Meng Ma1Zikai Zhou2Xintong Xie3Haoxiang Xie4Bolong Jiang5Zhongshuai Zhang6Key Laboratory of Urban Underground Engineering of Ministry of EducationKey Laboratory of Urban Underground Engineering of Ministry of EducationSchool of Civil EngineeringSchool of Civil EngineeringSchool of Civil EngineeringNational Engineering Research Center of Rail Transit Digital Construction and Measurement TechnologySchool of Civil EngineeringThe tunnel vibration level is usually employed as a vibration source intensity of the empirical prediction method. Currently, the analogy test and data base are two main means to determine the vibration source intensity. To improve the accuracy efficiency, the machine learning (ML) method was introduced to predict the tunnel vibration responses. To acquire model training samples, the measurements were performed in 80 different running tunnel sections of Beijing metro lines. Two types of method, back propagation neural network (BPNN) and generalised regression neural network (GRNN) were employed, which can make full use of characteristics of measured samples and reduce the data noise. The results indicate that the prediction efficiency is high and the mean square errors of the two ML methods are acceptable. Accordingly, both of the ML methods can be used as the reference of vibration source intensity in metro train-induced environmental impact evaluation. GRNN has relatively better predicting ability than BPNN.http://dx.doi.org/10.1155/2022/4031050 |
| spellingShingle | Zhuosheng Xu Meng Ma Zikai Zhou Xintong Xie Haoxiang Xie Bolong Jiang Zhongshuai Zhang Prediction of Metro Train-Induced Tunnel Vibrations Using Machine Learning Method Advances in Civil Engineering |
| title | Prediction of Metro Train-Induced Tunnel Vibrations Using Machine Learning Method |
| title_full | Prediction of Metro Train-Induced Tunnel Vibrations Using Machine Learning Method |
| title_fullStr | Prediction of Metro Train-Induced Tunnel Vibrations Using Machine Learning Method |
| title_full_unstemmed | Prediction of Metro Train-Induced Tunnel Vibrations Using Machine Learning Method |
| title_short | Prediction of Metro Train-Induced Tunnel Vibrations Using Machine Learning Method |
| title_sort | prediction of metro train induced tunnel vibrations using machine learning method |
| url | http://dx.doi.org/10.1155/2022/4031050 |
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