BiLSTM deep foundation pit deformation prediction method integrating attention mechanism

Predicting foundation pit deformation is a significant challenge for foundation pit engineering. The inaccuracy of deformation prediction is increased by the intricacy of subterranean space and the variety of construction conditions. Thus, this study develops a deformation prediction model that combines the attention mechanism and bidirectional long short term memory network (BiLSTM) in order to increase the accuracy of deep foundation pit deformation prediction. Meanwhile, to enhance the generalization ability of the model, this study introduces combined regularization in the loss function and adds a Dropout mechanism in the network structure. This study takes a deep foundation pit excavation project in Guangzhou as an example. Experiments shows that the model proposed in the study can complete convergence in about 30 training rounds, and the training loss is maintained at the 0.03 level. Meanwhile, the maximum absolute error of the model in the prediction of verification data is 1.44 mm, and the minimum error is 0.001 mm. The mean absolute error of the model is 0.311 mm, the root mean square error is 0.433 mm, and the R² is 0.906, which is better than the comparison model. The attention mechanism and BiLSTM model suggested in this study provides good generalization performance and high prediction accuracy in deep foundation pit deformation prediction, according to experimental results. Its potential for use in engineering safety management is promising.