Artificial neural networks for structural damage detection and classification

Carlos M. Ferregut; Roberto A. Osegueda; Jaime Ortiz

doi:10.1117/12.207718

20 April 1995 Artificial neural networks for structural damage detection and classification

Carlos M. Ferregut, Roberto A. Osegueda, Jaime Ortiz

Proceedings Volume 2446, Smart Structures and Materials 1995: Smart Systems for Bridges, Structures, and Highways; (1995) https://doi.org/10.1117/12.207718
Event: Smart Structures and Materials '95, 1995, San Diego, CA, United States

Abstract

An analysis of artificial neural networks on damage assessment of an aluminum cantilever beam was conducted. The neural networks were trained and tested with deterministic data of resonant frequency information to test their ability in determining the magnitude, location and type of damage on the beam. Being a preliminary study, no experimental data has been included, since no information was found in the literature where neural networks were used in determining the type of damage on a structure. This paper includes a discussion on the theory of neural network and the process involved in developing the architecture for three layer backpropagation neural networks for damage assessment. The neural networks were tested for three types of damage using four damage magnitudes.

Citation Download Citation

Carlos M. Ferregut, Roberto A. Osegueda, and Jaime Ortiz "Artificial neural networks for structural damage detection and classification", Proc. SPIE 2446, Smart Structures and Materials 1995: Smart Systems for Bridges, Structures, and Highways, (20 April 1995); https://doi.org/10.1117/12.207718

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