A transfer learning-driven digital twin framework for cross-bridge response prediction in regional bridge networks

Abstract

In response to the limited monitoring resources within regional bridge networks, this study presents a transfer learning–driven digital twin framework designed for collaborative cross-bridge response prediction and efficient operation and maintenance, realizing continuous structural‐condition perception for each bridge in the regional transport network. By integrating vehicle-load statistical modeling, finite element simulations, and a Transformer architecture, we establish mappings from traffic loads to structural responses, from responses to responses between different points. Transfer learning is employed to facilitate the sharing of model parameters across different bridges. Numerical validations demonstrate that satisfactory results were achieved in scenarios such as transferring from continuous girder bridge to simply supported bridges and from simulated to measured data. Experiments conducted further validate the effectiveness of the framework, revealing that transfer learning improves predictive accuracy by around 20% while reducing training time by 12%. The proposed method delivers an efficient, high-precision solution for the coordinated, smart management of regional bridge infrastructures. These findings offer a novel technical pathway for digitalized infrastructure maintenance and structural health monitoring in resource-constrained environments.