Construction pit deformation measurement technology based on neural network algorithm

Abstract

Abstract The current technology of foundation pit deformation measurement is inefficient, and its accuracy is not ideal. Therefore, an intelligent prediction model of foundation pit deformation based on back propagation neural network (BPNN) is proposed to predict the foundation pit deformation intelligently, with high accuracy and efficiency, so as to improve the safety of the project. Firstly, to address the shortcomings of BPNNs, which rely on the initial parameter settings and tend to fall into local optimum and unstable performance, this study adopts the modified particle swarm optimization (MPSO) to optimise the parameters of BPNNs and constructs a pit deformation prediction model based on the MPSO–BP algorithm to achieve predictive measurements of pit deformation. After training and testing the data samples, the results show that the prediction accuracy of the MPSO–BP pit deformation prediction model is 99.76%, which is 2.25% higher than that of the particle swarm optimization–back propagation (PSO–BP) pit deformation prediction model and 3.01% higher than that of the BP pit deformation prediction model. The aforementioned results show that the MPSO–BP pit deformation prediction model proposed in this study can effectively predict the pit deformation variables of construction projects and provide data support for the protective measures of the staff, which is helpful for the cause of construction projects in China.