Data-driven prediction for curved pipe jacking performance during underwater excavation of ancient shipwreck using an attention-based graph convolutional network approach

Evaluation of Jacking Forces in Weathered Phyllite Based on In Situ Pressuremeter Testing and Deep Learning

“…Deep neural networks have often been characterised as a black box model [23,46,47]. Despite their success in matching input data to output prediction, limited work has been conducted to explore the underlying features influencing predictions related to pipe jacking forces.…”

“…In geotechnical engineering, variable and uncertain geological environments can cause challenges during the operation of the tunnelling process [52][53][54]. Although deep learning models used in tunnelling such as RNN, LSTM, GRU, and Conv1D [36,37,47,[55][56][57] show the ability to handle such complex data, they do not provide insights into which features of the data have the most influence on the predictions. By applying the attention mechanism in the deep neural networks, it can effectively learn to focus on specific features that are significant to the prediction, addressing the challenges caused by heterogeneous geological conditions during the prediction such as jacking forces [20,39,58], controlling the alignment of the machine [55], and ground settlement [30].…”