Research on Geotechnical Deformation Prediction Method Based on LSTM Algorithm

The field of geotechnical engineering is an important research direction in civil engineering, which has important theoretical and practical value for deformation prediction of rock and soil mass. This study aims to propose an efficient and accurate geotechnical deformation prediction method by introducing Long and short time memory network (LSTM) algorithm, so as to improve the traditional prediction model and meet the practical engineering requirements. Firstly, through a detailed literature review, the existing methods and problems of deformation prediction in geotechnical engineering are deeply understood. In traditional methods, because of the complexity and nonlinear characteristics of rock and soil mass, it is often difficult to accurately capture its deformation law, which leads to the instability of the prediction results. In order to solve this problem, the LSTM algorithm in deep learning is introduced in this study, which can effectively process time series data and is suitable for problems with time series characteristics such as rock and soil mass deformation. Secondly, by collecting a large number of geotechnical experimental data, a dataset containing various working conditions and deformation conditions is established. In order to train the LSTM model, data preprocessing and feature engineering are performed to ensure that the data input to the model is representative and interpretable. Through the training and verification of the model, the feasibility and superiority of LSTM algorithm in geotechnical deformation prediction are verified. In the research process, the parameters of LSTM algorithm are optimized to improve the accuracy and generalization ability of the model. By comparing the experimental results of LSTM model and traditional prediction model, the excellent performance of LSTM algorithm in geotechnical deformation prediction is verified. At the same time, through the analysis of different working conditions and rock mass characteristics, the superiority of LSTM algorithm for dealing with different complex situations is revealed. At last, this paper presents a geotechnical deformation prediction method based on LSTM algorithm, and its application in practical engineering is forecasted. This method not only improves the accuracy, but also has better generalization performance, which is suitable for different types of rock and soil mass and engineering conditions. Through popularization and application, the method in this study is expected to provide a new idea and method for the further development of geotechnical deformation prediction, and provide a scientific and feasible reference for engineering practice.