A rapid damage identification method for highway bridge double-column piers based on longitudinal dynamic stiffness

As a vital load-bearing component, double-column pier is one of the most prevalent types in highway bridges substructures. With the continuous growth of traffic volume and the occurrence of natural disasters, double-column piers are highly susceptible to damage and effective damage identification is crucial to their long-term service safety. Conventional damage identification methods usually depend on visual inspection and non-destructive testing, typically focus on the inspection of surface damage and cannot achieve quantitative assessments. To address these challenges, this paper proposes a rapid damage identification method for highway double-column piers utilizing longitudinal dynamic stiffness. Firstly, the dynamic index is proposed to achieve the preliminary judgement of the damaged pier. Secondly, the dynamic stiffness similarity coefficient and logarithmic difference of dynamic stiffness are introduced to identify the damage side and the specific damaged location of the pier. Finally, the Bayesian-optimized K-nearest neighbor classifier is used to achieve the damage quantification. The effectiveness and accuracy of the proposed method is validated through a scale model experiment of a highway bridge double-column pier. The results demonstrate that the proposed methodology can accurately locate and quantify the damage of double-column piers, and the identification results agree well with the preset damage in the scale model experiment. The proposed method can provide reference for damage identification and rapid evaluation of highway bridge double-column piers in actual engineering.