Detection of the micro and small scale damages in steel beams using spectral finite element methods, modal strain energy-based damage index, and support vector regression

Bridges are exposed to various micro and small damages during their service life. Due to the importance and role of these structures in transportation on roads, timely detection of damages in them has a special place. This research presents an innovative and efficient method for diagnosing and estimating micro and small damages to bridges. The proposed method is based on the combination of spectral finite element and modal strain energy-based damage index to determine the location of damages in the first step. Then the support vector regression technique is used to estimate the severity of damages in the second step. A new eight-node element with spectral finite element characteristics is defined in OpenSees software. Then, micro and small damages are modeled in a single-span beam and a double-span steel beam with spring supports. In the first step, after modal analysis of the structures, the modal strain energy-based damage index is calculated to determine the location of the damages. In the second step, using the modal strain energy-based damage indices calculated in the previous step, the support vector regression networks are trained to estimate the severity of the damages. The results show very high accuracy and optimal performance of the proposed method.