Intelligent Discrimination Method Based on Digital Twins for Analyzing Sensitivity of Mechanical Parameters of Prestressed Cables

Abstract

The information collected on large-span prestressed cables by field sensors is susceptible to interference, which leads to inaccurate collection of structural and mechanical parameters of large-span prestressed cables, resulting in misjudgment of structural safety performance. This paper proposes an intelligent judgment method for improving the sensitivity of analyzing mechanical parameters of prestressed cables based on digital twins (DTs). The safety performance of the structure was evaluated by analyzing the mechanical parameters. First, the information during prestressed cable tensioning is dynamically sensed, thereby establishing a multidimensional model of structural analysis. The virtual model is processed by the model modification rule to improve the robustness of the simulation; thus, a DT framework for the sensitivity judgment of the mechanical parameters of the cable is built. In the twin model, the simulation data of the real structure were extracted. Probabilistic analysis was performed using the Dempster–Shafer(D–S) evidence theory to discriminate the sensitivity of mechanical parameters of each cable node under the action of external forces with high accuracy and intelligence. Sensitivity analysis provides a reliable basis for the safety performance assessment of structures. Taking the wheel–spoke-type cable truss as an example, the application of DTs and D–S evidence theory to the sensitivity determination of cable mechanical parameters under temperature fully verified that the proposed intelligent method can effectively evaluate the safety performance of the actual structure.