Comparison of ARIMA and LSTM in Predicting Structural Deformation of Tunnels during Operation Period
Accurately predicting the structural deformation trend of tunnels during operation is significant to improve the scientificity of tunnel safety maintenance. With the development of data science, structural deformation prediction methods based on time-series data have attracted attention. Auto Regres...
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| Format: | Article |
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MDPI AG
2023-06-01
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| Online Access: | https://www.mdpi.com/2306-5729/8/6/104 |
| _version_ | 1827737804468125696 |
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| author | Chuangfeng Duan Min Hu Haozuan Zhang |
| author_facet | Chuangfeng Duan Min Hu Haozuan Zhang |
| author_sort | Chuangfeng Duan |
| collection | DOAJ |
| description | Accurately predicting the structural deformation trend of tunnels during operation is significant to improve the scientificity of tunnel safety maintenance. With the development of data science, structural deformation prediction methods based on time-series data have attracted attention. Auto Regressive Integrated Moving Average model (ARIMA) is a classical statistical analysis model, which is suitable for processing non-stationary time-series data. Long- and Short-Term Memory (LSTM) is a special cyclic neural network that can learn long-term dependent information in time series. Both are widely used in the field of temporal prediction. In view of the lack of time-series prediction in the tunnel deformation field, the body of this paper uses historical data of the Xinjian Road and the Dalian Road tunnel in Shanghai to propose a new way of modeling based on single points and road sections. ARIMA and LSTM models are applied in comprehensive experiments, and the results show that: (1) Both LSTM and ARIMA models have great performance for settlement and convergence deformation. (2) The overall robustness of ARIMA is better than that of LSTM, and it is more adaptable to the datasets. (3) The model prediction performance is closely related to the data quality. ARIMA has more stable performance under the lack of data volume, while LSTM has better performance with high-quality data and higher upper limit. |
| first_indexed | 2024-03-11T02:35:43Z |
| format | Article |
| id | doaj.art-55c8b703455f456f94119cfbef712728 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-11T02:35:43Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-55c8b703455f456f94119cfbef7127282023-11-18T09:58:31ZengMDPI AGData2306-57292023-06-018610410.3390/data8060104Comparison of ARIMA and LSTM in Predicting Structural Deformation of Tunnels during Operation PeriodChuangfeng Duan0Min Hu1Haozuan Zhang2School of Mechanics and Engineering Science, Shanghai University, Shanghai 200444, ChinaSHU-SUCG Research Centre for Building Industrialization, Shanghai University, Shanghai 200072, ChinaSHU-SUCG Research Centre for Building Industrialization, Shanghai University, Shanghai 200072, ChinaAccurately predicting the structural deformation trend of tunnels during operation is significant to improve the scientificity of tunnel safety maintenance. With the development of data science, structural deformation prediction methods based on time-series data have attracted attention. Auto Regressive Integrated Moving Average model (ARIMA) is a classical statistical analysis model, which is suitable for processing non-stationary time-series data. Long- and Short-Term Memory (LSTM) is a special cyclic neural network that can learn long-term dependent information in time series. Both are widely used in the field of temporal prediction. In view of the lack of time-series prediction in the tunnel deformation field, the body of this paper uses historical data of the Xinjian Road and the Dalian Road tunnel in Shanghai to propose a new way of modeling based on single points and road sections. ARIMA and LSTM models are applied in comprehensive experiments, and the results show that: (1) Both LSTM and ARIMA models have great performance for settlement and convergence deformation. (2) The overall robustness of ARIMA is better than that of LSTM, and it is more adaptable to the datasets. (3) The model prediction performance is closely related to the data quality. ARIMA has more stable performance under the lack of data volume, while LSTM has better performance with high-quality data and higher upper limit.https://www.mdpi.com/2306-5729/8/6/104tunnelstructural deformationARIMALSTMprediction |
| spellingShingle | Chuangfeng Duan Min Hu Haozuan Zhang Comparison of ARIMA and LSTM in Predicting Structural Deformation of Tunnels during Operation Period Data tunnel structural deformation ARIMA LSTM prediction |
| title | Comparison of ARIMA and LSTM in Predicting Structural Deformation of Tunnels during Operation Period |
| title_full | Comparison of ARIMA and LSTM in Predicting Structural Deformation of Tunnels during Operation Period |
| title_fullStr | Comparison of ARIMA and LSTM in Predicting Structural Deformation of Tunnels during Operation Period |
| title_full_unstemmed | Comparison of ARIMA and LSTM in Predicting Structural Deformation of Tunnels during Operation Period |
| title_short | Comparison of ARIMA and LSTM in Predicting Structural Deformation of Tunnels during Operation Period |
| title_sort | comparison of arima and lstm in predicting structural deformation of tunnels during operation period |
| topic | tunnel structural deformation ARIMA LSTM prediction |
| url | https://www.mdpi.com/2306-5729/8/6/104 |
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