Intelligent Prediction of Prestressed Steel Structure Construction Safety Based on BP Neural Network

Abstract

In the construction process of a prestressed steel structure, it is a point of research interest to obtain the safety state of the structure according to the design parameters and working conditions of the structure. The intelligent prediction of structural construction safety provides the basis for safety control. This study proposes an intelligent prediction method of structural construction safety based on a back propagation (BP) neural network. Firstly, the correlation mechanism of structural construction safety performance parameters is established, which involves structural design parameters and mechanical parameters. According to the basic principle of a BP neural network, the relationship between design parameters and mechanical parameters is captured. The virtual model of a structure construction process is established based on digital twins (DTs). The DTs and BP neural network are combined to form a structural safety intelligent prediction framework and theoretical method, setting working conditions in a twin model to obtain mechanical parameters. Mechanical parameters are intelligently predicted by design parameters in neural networks. The safety performance of structure construction is evaluated according to mechanical parameters. Finally, the intelligent prediction method is applied to the construction process of string beam. Based on DTs and BP neural network, the intelligent analysis of structural construction safety is carried out. This provides a reliable basis for safety control. The feasibility of this research method is verified by comparing the predicted results of the theoretical method with the measured data on site.