Assessing the condition of a π-shaped steel–concrete composite girder bridge via two-dimensional spatial load-driven influence surface identification

Structural damage continuously accumulates with the extension of bridge service life, posing significant threats to bridge safety and operational functionality. Traditional bridge inspection and assessment methods rely primarily on static load tests or localized sensor monitoring, which struggle to comprehensively reflect dynamic responses under vehicular loads and multiaxial load effects, resulting in notable limitations. In response to the aforementioned challenges, a novel bridge condition assessment methodology utilizing influence surfaces is developed in this investigation. This approach integrates two-dimensional dynamic vehicle loads, incorporates regularization techniques, and employs algorithms such as singular value decomposition (SVD) and variational mode decomposition (VMD) to identify structural influence lines and influence surfaces on bridges. The Sutai Expressway Tongfu No.3 bridge, a steel–concrete composite beam bridge, was used as a case study; a finite element model was established, and a vehicle–bridge coupled dynamic analysis was conducted. By introducing two-dimensional vehicle loads, the identification accuracy and damage detection capability of the proposed method were validated. The results demonstrate that the proposed method effectively eliminates interference from vehicle dynamic effects and multiaxial load coupling, enabling the precise identification of both damage locations and severity levels in bridges, with an identification error of less than 5%.