ABSTRACT

This chapter reviews the large literature available on damage assessment based on inverse methods. It provides a description of Genetic algorithms (GA) and artificial neural networks, and their application to structural damage assessment. The chapter explains the working principle and reviewed each factor that affects its performance. The most successful applications of vibration-based damage assessment are model updating methods based on global optimization algorithms. Classical optimization algorithms used in damage assessment are sensitivity-based methods. These algorithms obtain the optimum solution through a sensitivity-based search. GAs have become very popular in the damage assessment field because of their efficiency and easy implementation in damage assessment problems. Parallel genetic algorithms are also an attractive alternative; they are particularly easy to implement and provide a superior numerical performance. GAs is robust searching algorithms with an easy implementation to structural damage assessment problems. The successful application of a neural network depends on the representation and the learning algorithms.