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.

The structure of a prestressed steel structure is complex, which can result in insufficient control accuracy and the low efficiency of the structural safety. The traditional analysis method only obtains the mechanical parameters of the structure and it cannot obtain the key factors that affect the structural safety. In order to improve the intelligence level of the structural safety performance analysis, this study proposes an intelligent analysis for the safety-influencing factors of prestressed steel structures that is based on digital twins (DTs) and random forest (RF). Firstly, the high-precision twin modeling is carried out by the weighted average method. The design parameters and the mechanical parameters of the structure are extracted in real time in the twin model, and the parameters are classified by the RF. The fusion mechanism of the DTs and RF is formed, and the intelligent analysis model of the structural safety factors is established. Driven by the analysis model, the correlation mechanism between the design parameters and the mechanical parameters is formed. The safety state of the structure is judged by the mechanical parameters, and the key design parameters that affect the various mechanical parameters are analyzed. Through the integration of the design parameters and mechanical parameters, the intelligent analysis process of the safety-influencing factors of prestressed steel structures is formed. Finally, an intelligent analysis of the importance of the safety-influencing factors is carried out with the string-supported beam structure as the test object. Driven by the integration of DTs and RF, the key design parameters that affect the various mechanical parameters are accurately obtained, which provides a basis for the intelligent control of the structural safety.

In the construction process of beam string structures, the environmental effect and corresponding mechanical properties of the structure are complex. The problem of the misjudgment of structural safety performance caused by the uncertainty of a structural mechanical parameter analysis under various factors needs to be solved. In this study, a method for capturing key components and an intelligent safety analysis of beam string structures based on digital twins (DTs) was proposed. Combined with the characteristics of DTs mapping feedback, a component capture and security analysis framework was formed. Driven by twin framework, multi-source data for structural safety analysis were obtained and the parameter association mechanism established. Considering the space-time evolution and the interaction between the virtual and real elements of the construction process, a multidimensional model was established. Driven by the Dempster–Shafer (D–S) evidence theory, the fusion of structural mechanics parameters was carried out. The safety of the structure was analyzed intelligently by capturing key structural components, thereby providing a basis for the safety maintenance of the structure. The integration of DTs modeling and multi-source data improves the accuracy and intelligence of structural construction safety analysis. In the analysis process, capturing the key components of the structure is the core step. Taking the construction process of a string supported beam roof (symmetrical structure) in a convention and exhibition center as an example, the outlined research method was applied. Based on DTs and D–S evidence theory, the variation degree of mechanical parameters of various components under temperature was determined. By comprehensively investigating the changes of various mechanical parameters, the key components of the structure were captured. Thus, the intelligent analysis of structural safety was realized. The comparison of data verified that the intelligent method can effectively analyze the safety performance of the structure.

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