damage identification in steel girder bridges using improved damage index method by modal combination and artificial neural network

The increasing desire for evaluating the health state of infrastructures has led to an interest in the vibration-based structural health monitoring techniques. as bridges are one of the most important transport infrastructure systems, therefore, their performance while hazardous events such as earthquake, is very important. by existing, some hidden defects in such infrastructures would lead them to behave improperly in their performance while earthquake happens.also, it is necessary to have efficient damage identification methods in order to identify damages which arose by the earthquake. modal strain energy-based damage index is one of the most efficient methods. however, this technique is incapable of quantifying damage severities, although it is good at detecting and locating damage. recently, theapplication of neural networks has attracted increasing attention in damage identification of structures. anns (artificial neural networks) can be used for quantifying damage severities.