The Three-Stage Artificial Neural Network Method for Damage Assessment of Building Structures

Abstract

Building structures are often huge and composed of a number of elements. It may not be possible to make modal measurements along the large number of degrees of freedom. Structural damage detection therefore becomes much more challenging both in terms of measurement and subsequent analyses. Accordingly, a problem in structural damage detection is requirement of a systematic and effective method. Among the developed damage detection techniques, artificial neural networks (ANNs) have become promising tools recently. The main drawback of using ANNs in structural condition monitoring is the requirement of enormous computational effort. To address this issue, a novel technique is proposed using “damage index” derived from frequency response functions (FRFs) with the three-stage ANN method to detect damage. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points. Then using these features, damage indices of damage cases of the structure are identified. Damage indices corresponding to different damage locations and severities are introduced to ANNs. The effectiveness of the proposed method is validated using the finite element model of a 10-storey framed structure. The results show that the principal component analysis based damage index is suitable for structural damage detection.