Research and Application of Deformation Prediction Model for Deep Foundation Pit Based on LSTM
Hailin Li, zhizhou zhao, Xue Du
Abstract
Deep foundation pit is a door with a long history, but it has new disciplines; in this paper, firstly, the modeling method and process of LSTM (long short‐term memory) network are discussed in detail, then the optimization algorithm used in the model is described in detail, and the parameter selection methods such as initial learning rate, activation function, and iteration number related to LSTM network training are introduced in detail. LSTM network is used to process the deformation data of deep foundation pit, and random gradient descent, momentum, Nesterov, RMSProp, AdaGmd, and Adam algorithms are selected in the same example for modeling prediction and comparison. Two examples of horizontal displacement prediction of pile and vertical displacement prediction of column in deep foundation pit show that the LSTM network model established by different optimization algorithms has different prediction accuracy, and the LSTM network model established by Adam optimization algorithm has the highest accuracy, which proves that the selection of optimization algorithm plays an important role in LSTM and also verifies the feasibility of LSTM network in the data processing and prediction of deep foundation pit deformation.