Horizontal deformation prediction of deep foundation pit support piles based on decomposition methods model

In order to predict the long-term development pattern of horizontal deformation of deep foundation pit support piles, an AM-CNN-LSTM model capable of predicting the deformation of support piles was constructed based on spatial feature extraction of convolutional neural network (CNN) data combined with long and short term memory neural network (LSTM) to analyze the temporal nature of the data and the divided feature weights of attention mechanism (AM). In the context of a deep foundation pit project in Beijing, the factors affecting the maximum deformation of the supporting piles are clarified based on the gray correlation method. The constructed model was used to analyze the single-point deformation pattern of the supporting pile and to compare and analyze the results obtained from the predictions of back propagation neural network (BPNN), CNN and traditional CNN-LSTM models. The results show that the maximum deformation value of the supporting piles is highly correlated with the excavation depth of the deep foundation pit, the number of days of proximity, the internal force of the support, the nature of the soil, the size of the piles, and the embedment depth. The AM mechanism significantly improves the initial data information mining depth and deformation prediction accuracy, which is continuously updated by the gradient descent method until the error requirements are satisfied. Compared with BPNN, CNN and CNN-LSTM models, the application of AM-CNN-LSTM model is more stable for long-term deformation prediction of supporting piles. By comparing with the measured data, the prediction accuracy of the AM-CNN-LSTM model is within 5% to 10% error.