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In this paper, a transmissibility based damage detection methodology using artificial intelligence is proposed. Structural health monitoring requires accurate damage detection in real engineering while the environmental uncertainties make this a challenge. In order to reduce this effect, artificial intelligence, such as artificial neural networks might be a possible strategy for achieving a better interpretation of the monitored data during operational condition. In this study, transmissibility is taken into account as damage sensitive feature because it accounts for the response data only. Then, auto-associative neural network is employed for detecting the structural damage and predicting its severity. In order to validate our proposed technique, a ten-floor structure is simulated and studied. The results show good performance in detecting damages. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for damage detection and localization in composite beams using vibration data, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The data was acquired by developing a program that performs dynamic analysis of unidirectional graphite-epoxy composite beams based on the Finite Element Method (FEM). The objective function makes use of natural frequencies and Modal Assurance Criterion. The proposed methodology is validated using numerically simulated data and experimental data. A comparative study between the performances of PSO and GA in detecting multiple and single damage scenarios is carried out. Then, the effect of noise is investigated by taking different noise levels in the modal data. It appears that the noise has a negligible effect on the performance of the presented approaches. [ABSTRACT FROM AUTHOR]