A dynamic stiffness-based two-step method for damage identification of joints in hinged slab bridges using support vector machine

As critical components of hinged slab bridges, joints are of great importance to the bearing capacity and serviceability of bridges. The existing model-based joint damage detection methods are based on precise finite element models, and their identification accuracy is significantly affected by construction uncertainty. In this paper, dynamic stiffness is introduced into joint damage detection and a two-step framework is proposed for the damage localization and quantification of joints in hinged slab bridges. The dynamic stiffness-based data-driven method is suitable for the evaluation of bridges with incomplete information or bridges with serious damage. Firstly, the second-order difference of dynamic stiffness (SDDS) calculated by slabs' responses is used an indicator to locate the damage. Secondly, Bayesian optimization is utilized to optimize the model hyperparameters of the support vector machine (SVM), where the dynamic stiffness serves as the characteristic vector for training the optimized SVM network. The applicability of this method was numerically validated using a grillage model of a hinged slab bridge, taking into consideration factors such as noise interference, impact location, sensor placement, and impact force characteristics. Additionally, on-site experiments were conducted on an in-service bridge to verify the effectiveness of the proposed method. Analysis results demonstrate that the proposed two-step method can properly evaluate joint damage with less dependence on the impact locations, force amplitude and environmental factors, and thus can be used for rapid damage identification of in-service hinged slab bridges and quantitative evaluation of repair effect.