Research on the Damage Diagnosis Model Algorithm of Cable-Stayed Bridges Based on Data Mining

To obtain an effective data mining method for cable-stayed bridge damage diagnosis, the algorithm of diagnosis model based on was studied, and a is proposed. This oriented to bridges. After comparison, support vector machine (SVM) limit gradient-boosting (XGBoost) algorithms, with advantages in location quantification, are combined optimized First, refined benchmark finite element established by Abaqus, postprocessing such as vibration frequency modal curvature used dataset. Second, feature se-lection conducted, damage-sensitive change rate index selected mining. Next, SVM XGBoost algorithms grid random search, locate quantify damage. Finally, bridges obtained. Taking example, proposed applied analyzed, results show effectiveness method.