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.

Analysis of ductility of hybrid fiber ultra-high performance concrete based on improved GA-BP neural network

“…Therefore, it can be used for modeling prediction. The typical BP neural network structure is shown in Figure 1 [15].…”

“…Qin Hu, Yuanzhi Gao. Applied Mathematics and Nonlinear Sciences, 9(1) (2024)[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] …”

Construction Safety Risk Assessment of High-Pile Wharf: A Case Study in China

“…Based on the BIM platform, a safety risk identification system and early warning system for China's metro construction was established, by which the safety risk control for China's metro construction including pre-construction risk identification and risk warning in construction were visualized and quantitatively developed [31]. In recent years, some newly developed data analysis methods, such as the fuzzy hierarchical comprehensive evaluation method, the BP neural network and analytic network process-fuzzy comprehensive evaluation model have been introduced to the BIM-based platform to develop construction safety risk assessments and safety management [32][33][34]. During the construction of a high-pile wharf, tiny problems may cause safety accidents.…”

A Study on the Elastoplastic Stable Bearing Capacity of Double-Steering Prestressed Plate Columns

“…Liu et al [20] devised an intelligent evaluation technique for prestressed steel columns using the digital twin (DT) technology, encompassing performance analysis and maintenance. Zhu et al [21] proposed an Various scholars have explored the parameter optimization of beam-brace prestressed columns [15]. Ma et al [16] proposed a novel approach for optimizing prestressed column parameters that are capable of addressing multi-objective optimization challenges.…”

“…Liu et al [20] devised an intelligent evaluation technique for prestressed steel columns using the digital twin (DT) technology, encompassing performance analysis and maintenance. Zhu et al [21] proposed an intelligent prediction method for the construction and safety of prestressed columns based on the DT technology, laying a methodological foundation for safety forecasting. Liu et al [22] conducted intelligent evaluations of prestressed cable safety through DT and artificial intelligence integration, offering a methodology for the intelligent evaluation and application of prestressed cables.…”