Data-Driven Method for Predicting Long-Term Underground Pipeline Settlement Induced by Rectangular Pipe Jacking Tunnel Construction
Yongsuo Li, Xiaoxuan Weng, Da Hu, Ze Tan, Jing Liu
Abstract
The long-term settlement of deep pipelines is a crucial factor in the construction of rectangular shield tunnels. Conventional prediction techniques primarily depend on empirical models and statistical analyses, which often fail to accurately forecast pipeline deformation and settlement in intricate environments. To tackle this challenge, this article introduces a long-term settlement prediction model for pipelines based on long short-term memory (LSTM) networks, incorporating time decay and multiscale improved self-attention mechanisms (ISA), referred to as the LSTM-ISA model. Initially, settlement data that matched theoretical expectations were selected from 69 sets of monitoring data to create a data set for predicting pipeline settlement during shield tunnel construction. The LSTM-ISA model was then developed, using the LSTM network to capture temporal dependencies in time-series data. The time-decay mechanism gives greater importance to more recent data, while the multiscale self-attention mechanism identifies features across various time scales. The model’s effectiveness and reliability were tested using actual measurement data from the Changsha Metro Line 6 project and compared with predictions from traditional LSTM and LSTM-SA networks. The findings indicate that the LSTM-ISA model surpasses both the LSTM and LSTM-SA models, achieving a 12.9% and 30.7% decrease in mean squared error (MSE) and a 6.4% and 21.8% decrease in mean absolute error (MAE), respectively. These results imply that the LSTM-ISA model can serve as an effective tool for providing early warnings regarding long-term pipeline settlement caused by the construction of rectangular shield tunnels.