Arch bridge damage detection using vibration data and gradient-based optimizer algorithm

Bridges are one of the most pivotal civil engineering structures. Nevertheless, they are highly susceptible to localized damage, which poses a gradual threat to their long-term stability and functionality. Hence, it is of utmost importance to detect any damage in bridges to ensure their continued safe and reliable performance. In view of this, the present study proposes an optimization-based finite element model updating method to perform arch bridge damage detection by using vibration data and the gradient-based optimizer (GBO) algorithm. The damage severity of structural elements in different parts of the bridge, including those in the arch, columns, and deck, are taken as damage variables. The objective function of the optimization problem is also defined in terms of discrepancies between the vibration data of the actual damaged bridge and those computed from the finite element model. The GBO algorithm, as a new robust meta-heuristic algorithm, is applied to tackle the damage detection problem, and its performance is compared to other well-known meta-heuristic algorithms. The applicability of the proposed method is numerically assessed on an arch bridge structure under both noise-free and noisy conditions. The obtained results demonstrate the accuracy and computational efficiency of the presented method in dealing with the arch bridge damage detection problem.