Monitoring and deformation of deep excavation engineering based on DFOS technology and hybrid deep learning

With the increasing urbanization in China, monitoring and predicting the deformation of deep excavations has become increasingly critical. Concurrently, as neural network models find application and development in deep excavation displacement prediction, traditional models face challenges such as insufficient accuracy and weak generalization capabilities, failing to meet the high-precision warning demands of practical engineering. Therefore, research into hybrid models is necessary. This study proposes a combined neural network model integrating a Convolutional Neural Network, Long Short-Term Memory network, and Self-Attention Mechanism (CNN-LSTM-SAM), which utilizes time-series monitoring data as input. The CNN-LSTM-SAM model merges the data feature extraction capabilities of CNN, the long-term memory function of LSTM, and the information weighting capacity of the self-attention mechanism, synthesizing the advantages of various deep excavation displacement prediction models to enhance prediction accuracy and provide more effective support for construction practice. Furthermore, given the limited application of the CNN-LSTM-SAM model in deep excavation displacement analysis, this research contributes to addressing gaps in this field. Applied to an internally braced deep excavation project in the Donggang Business District of Dalian, displacement data acquired through Distributed Fiber Optic Sensing (DFOS) technology were used as training data. The CNN-LSTM-SAM model was employed to predict the horizontal displacement at the pile top. The resulting deformation predictions were compared and analyzed against those from Back Propagation (BP) neural network, Long Short-Term Memory (LSTM) network, and a combined Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model. Results indicate that at monitoring point S5, the coefficient of determination (R²) for the CNN-LSTM-SAM model's predictions increased by 12.42%, 10.85%, and 5.63% compared to the BP, LSTM, and CNN-LSTM models, respectively, demonstrating higher accuracy than the other three models. Similar patterns were observed when training and predicting using data from other monitoring points, proving the applicability and robustness of the CNN-LSTM-SAM model. The findings of this study offer valuable references for the design and construction of similar deep excavation projects.