Data-Driven Deep-Learning Model for Predicting Jacking Force of Rectangular Pipe Jacking Tunnel
Yongsuo Li, Xiaoxuan Weng, Da Hu, Ze Tan, Kai Qi, Jing Liu
Abstract
The advancement of computer technology has led to the increased utilization of new algorithms, such as machine learning, in various fields including underground engineering. The estimation of jacking force plays a critical role in the construction of rectangular jacked tunnels. Conventional prediction techniques often rely on empirical models and statistical analysis, posing challenges in accurately forecasting the jacking force for intricate tunnel structures. To overcome this obstacle, a method for predicting tunnel jacking force is proposed, which integrates a convolutional neural network (CNN) and long short-term memory network (LSTM). By utilizing geometric and operational parameters as inputs, the CNN extracts data features, which are subsequently inputted into the LSTM network for time-series modeling. This model effectively processes continuous jacking force data by comprehending the complex correlations within the data set, resulting in more precise predictions of future jacking force values. Comparative analysis with traditional methods such as the artificial neural network, single CNN model, and LSTM network demonstrates that the CNN-LSTM model significantly reduces prediction errors in tunnel jacking force estimation, thereby enhancing model accuracy. Consequently, the efficacy of the CNN-LSTM model has been validated, showcasing the benefits of employing deep-learning techniques for predicting jacking force in pipe jacking tunnel construction.