Modal characteristics based computational approaches for structural damage identification

Abstract

The main objective of health monitoring is to detect, localize and quantify the level of structural damage to the civil infrastructure. By using structural damage identification and parameter estimation as a means of determining the actual state properties, performance, and limit states of a structure, it is possible to gain an improved understanding of a structure’s capacity and typical performance during its service. Damage identification methods based on modal analysis have several advantages over alternative techniques due to the fact that the modal parameters depend only on the mechanical characteristics of the structure and not on the excitation applied, and furthermore that theoretically the structure can be represented by measurements taken at a single location. Recently, computational intelligence methods have been applied to the problem of structural damage detection and identification. Computational intelligence methods, such as artificial neural networks (ANN) and genetic algorithm (GA), are highly adaptive methods originated from the laws of nature and biology. In the present paper, application of computational methodologies such as ANN and GA has been discussed for identification of structural damage utilizing vibration data. It was observed that the proposed approaches are capable of predicting the location and magnitude of the damage with better accuracy. A hybrid methodology combining the modal strain energy criteria with genetic algorithm showed the better damage identification results especially for large scale structures.