Structural damage detection using measured dynamic data for pattern recognition is a promising approach. These pattern recognition techniques utilize artificial neural networks and genetic algorithm to match pattern features. In this study, an artificial neural network-based damage detection method using frequency response functions is presented, which can effectively detect nonlinear damages for a given level of excitation. The main objective of this article is to present a feasible method for structural vibration-based health monitoring, which reduces the dimension of the initial frequency response function data and transforms it into new damage indices and employs artificial neural network method for detecting different levels of nonlinearity using recognized damage patterns from the proposed algorithm. Experimental data of the three-story bookshelf structure at Los Alamos National Laboratory are used to validate the proposed method. Results showed that the levels of nonlinear damages can be identified precisely by the developed artificial neural networks. Moreover, it is identified that artificial neural networks trained with summation frequency response functions give higher precise damage detection results compared to the accuracy of artificial neural networks trained with individual frequency response functions. The proposed method is therefore a promising tool for structural assessment in a real structure because it shows reliable results with experimental data for nonlinear damage detection which renders the frequency response function-based method convenient for structural health monitoring.

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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, artifi cial 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 identifi ed. Damage indices corresponding to different damage locations and severities are introduced to ANNs. The effectiveness of the proposed method is validated using the fi nite 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.