Abstract :
[en] The appropriate bridge maintenance strategy cannot be determined unless the damage is identifed, localized, and quan tifed correctly. Damage assessment can be performed based on model updating, where material properties of a numeri cal model are modifed to represent the damaged state as accurately as possible. However, this approach may become
tedious for complex structures such as bridges due to the high number of unknown variables. This study replaces the
time-consuming Finite Element (FE) simulations with Artifcial Neural Network (ANN) as a surrogate model to reduce the
required computational time. The implementation of ANN enables automating the existing manual damage assessment
of a prestressed concrete bridge beam. In this paper, the objective is to minimize the diference between the simulated
and measured sagging, which is the irreversible downward movement of the bridge due to its weight. The minimization
is performed with the Simulated Annealing (SA) algorithm, and the optimization process is repeated with 100 diferent
starting points to ensure robustness. The results indicate that the automated approach performs similarly to the manual
approach while being faster and enabling wider exploration in the search space without compromising accuracy. The
proposed approach serves as a practical tool for real-world problems by ofering an efcient damage assessment.
