Postprint version. Published in IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings: Tianjin, China, July 2, 2012, pages 7-11.
The definitive version is available at https://doi.org/10.1109/CIMSA.2012.6269593.
With the ever-increasing demand for the safety and functionality of civil infrastructures, structure health monitoring (SHM) has now become more and more important.
Recent developments in computational intelligence and digital signal processing offer great potentials to develop a more efficient, reliable, and robust structure damage identification system. In this paper, the application of artificial neural networks and wavelet analysis is investigated to develop an intelligent and adaptive structural damage detection system. The proposed approach is tested on an IASC (International Association for Structural Control)-ASCE (American Society of Civil Engineers) SHM benchmark problem. Satisfactory computer simulation results are obtained.
Electrical and Computer Engineering
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