Digital Integration of Temperature Field of Cable-Stayed Bridge Based on Finite Element Model Updating and Health Monitoring

Abstract

A health monitoring system typically collects and processes data to observe the health status of a bridge. The cost limitations imply that only the measurement point data of a few key points can be obtained; however, the entire bridge monitoring information cannot be established, which significantly interferes with the data integrity of the structural monitoring system. In this study, a solution is proposed for reconstructing the monitoring data of the entire bridge. By updating the finite element (FE) model based on structural thermal analysis, numerical simulation technology, and other methods, the temperature field integration model of a cable-stayed bridge is realized. The temperature spatial expansion method of deep learning is established by using the complete simulated temperature field of the entire bridge based on limited measured temperature data; this data is verified and applied to the main beam and bridge tower, thereby establishing the complete measured temperature field of the whole bridge. This is of great significance in supplementing health monitoring information, allowing for the accurate monitoring and evaluation of the structural safety and service performance of long bridges.

Evolution of Digital Twin Frameworks in Bridge Management: Review and Future Directions

“…Furthermore, expert knowledge is utilized in combining bridge visual inspection results and finite element modeling to create DTs of bridges, which can be used for bridge design, management, and operation [152]. As the results of Table 3 show, most of the bridge DTs in the literature focus on bridge monitoring using FEM model updating to simulate the current condition and predict the future status of the bridge [160]. These methods combine FEM, data-driven virtual models, and bridge health monitoring results to create a DT.…”