Automated damage assessment of prestressed concrete bridge beam based on sagging

Abstract

The appropriate bridge maintenance strategy cannot be determined unless the damage is identified, localized, and quantified correctly. Damage assessment can be performed based on model updating, where material properties of a numerical model are modified 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 Artificial 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 difference 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 different 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 offering an efficient damage assessment.