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Damage detection on a three-storey steel frame using artificial neural networks and genetic algorithms

  • Advances in Dynamics, Stability and Control of Mechanical Systems
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Abstract

The paper, through the discussion of an experimental investigation, considers a combined approach based on artificial neural networks and genetic algorithms for structural damage identification. A reduced scale three-storey steel spatial frame was instrumented by a series of 12 accelerometers and progressively damaged by cutting one of its columns just above the first storey. Accelerations induced by ambient vibrations were recorded as the frame was progressively damaged, and the deepness of the cut was taken as the entity of the damage. At every damage level the modal properties (natural frequencies and modal shapes) of the steel frame were evaluated through a neural network based approach. Subsequently, two error functions that measure the differences between the experimental results and those calculated from a finite element model of the steel frame were defined and a genetic algorithm was employed for damage detection. Results of the experimentation (where damage was known as both location and extent) were compared with the results of the optimization algorithm in order to verify its ability to match the actual damage.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering (DICeA), University of Florence, Via di Santa Marta, 3, 50139, Florence, Italy

    Michele Betti, Luca Facchini & Paolo Biagini

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  1. Michele Betti
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  2. Luca Facchini
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  3. Paolo Biagini
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Correspondence to Michele Betti.

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Betti, M., Facchini, L. & Biagini, P. Damage detection on a three-storey steel frame using artificial neural networks and genetic algorithms. Meccanica 50, 875–886 (2015). https://doi.org/10.1007/s11012-014-0085-9

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  • DOI: https://doi.org/10.1007/s11012-014-0085-9

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