Prediction of deformation and damage of foundation support system based on BP neural network

Abstract

The pit in the ground that needs to be excavated during the construction of a building is known as a foundation pit. In this paper, a MATLAB neural network toolbox is proposed to establish a three-layer BP neural network to predict building settlement. The relative error of the predicted values obtained after training is between -2% and 1%, and the maximum absolute error is 0.049 mm. this error is acceptable for rock-on-rock engineering problems. It shows that the building settlement prediction using artificial neural network method is feasible and the prediction results are credible. It automatically identifies the complex nonlinear relationships between the dependent and independent variables through self-learning of the optimal operation strategy, and considers the interactions among the factors in a comprehensive and integrated manner, which has good application prospects for solving nonlinear problems in the field of geotechnical engineering.