support vector regression

This paper presents a powerful two-step method for damage detection of large-span bridges with variable sections. Bridges are one of the basic infrastructures in the field of urban and suburban transportation, and timely detection of damage during its operation is important. Damage in this category of structures will cause service disruption during natural disasters. The presented method is based on the combination of spectral finite element and modal strain energy damage index, as well as the combination of genetic algorithm and support vector regression to detect and estimate the damage severity. One of the efficient methods in the field of wave propagation is the spectral finite element method, which is capable of modeling with high flexibility and detecting micro damage. Vibration-based methods are widely used to detect structural damage, while the modal strain energy damage index has a higher sensitivity in detecting damage among other vibration-based methods. The case study model is the Crowchild Bridge in Western Canada, which has special characteristics in terms of geometry and the characteristics of structural elements. In this research, the modal strain energy damage index has been modified due to the change of cross-section along the girders. Also, support vector regression has been used as a robust technique in estimation damage severity. In order to increase the accuracy and improve the damage severity estimation method, the genetic algorithm is used to optimize the effective parameters of the support vector regression. The combined method of genetic algorithm and support vector regression has been able to estimate the severity of damages in a favorable way.