Study of Improved Grey BP (Back Propagation) Neural Network Combination Model for Predicting Deformation in Foundation Pits

Abstract

Deep excavation engineering is a comprehensive discipline that involves multiple fields such as engineering geology, hydrogeology, and foundation engineering. With the improvement of the utilization rate of underground space, the demand for the construction of large-scale underground structural engineering is growing, making the excavation of underground soil become increasingly frequent, which also brings about the safety problems of deep foundation pit engineering and the surrounding environment. Prediction of foundation pit deformation is an important research direction with diverse historical developments, but it is also facing a series of difficulties and challenges. In order to solve these problems, this article proposes an improvement plan, establishes a prediction model based on the combination model of grey BP (back propagation) neural network, and verifies its effectiveness through experiments. The results show that the average error of the new model’s prediction of horizontal deformation is about 0.31, which is about 32% lower than the traditional model’s prediction error. The difference between the vertical deformation prediction and actual monitoring results is also controlled. The vertical deformation predicted by wavelet transform is 7% to 9% larger than the actual monitoring results, meeting the prediction requirements. Finally, this article explores the research on the prediction of foundation pit deformation in deep excavation engineering, An improved grey BP neural network combination model was proposed and its effectiveness was verified through experiments. This article has important reference value for the study of deformation prediction in deep excavation engineering.