Deformation Analysis and Research of Building Envelope by Deep Learning Technology under the Reinforcement of the Diaphragm Wall

Abstract

The safety analysis of underground buildings is the most crucial problem in the construction industry. This work aims to optimize the safety analysis results of the underground building envelope and comprehensively improve the safety of the underground building. Long short-term memory (LSTM) can make long-term and short-term predictions, thus reducing the model’s prediction error. Applying it to the deformation analysis, data prediction of the underground building envelope can improve the accuracy of the deformation prediction of the envelope. This work deeply discusses deep learning technology and the principle of the LSTM model. Based on the safety analysis concept of the underground building envelope, LSTM underground building envelope deformation’s prediction model is established and comprehensively evaluated. The results show that in the prediction of horizontal displacement of foundation pit pile of diaphragm wall, the mean relative error (MRE) of the prediction results of the designed model range in 10%–18%, and the calculation time ranges 15–36 s. In the settlement displacement prediction, the model’s MRE is within the range of 5%–7%, and the calculation time is within the range of 17–40 s. With the increase of training times, the prediction accuracy of the model increases, and the calculation time becomes relatively stable. Compared with other models, the relative error of prediction results is about 5.4% at the highest and 1.8% at the lowest. This work provides technical support for improving the safety prediction accuracy of the underground building envelope and provides some reference value for the comprehensive development of the underground building industry.