Comparison of ARIMA and LSTM in Predicting Structural Deformation of Tunnels during Operation Period

Chuangfeng Duan, Min Hu and Haozuan Zhang ()
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Chuangfeng Duan: School of Mechanics and Engineering Science, Shanghai University, Shanghai 200444, China
Min Hu: SHU-SUCG Research Centre for Building Industrialization, Shanghai University, Shanghai 200072, China
Haozuan Zhang: SHU-SUCG Research Centre for Building Industrialization, Shanghai University, Shanghai 200072, China

Data, 2023, vol. 8, issue 6, 1-18

Abstract: 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.

Keywords: tunnel; structural deformation; ARIMA; LSTM; prediction (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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