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A Review of Vibration Based Inverse Methods for Damage Detection and Identification in Mechanical Structures Using Optimization Algorithms and ANN

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Abstract

The Structural Health Monitoring (SHM) technique is today the principle approach to manage the discovery and recognizable proof of damage in the most various designing areas. The need to monitor structural behavior is increasing every day but due to the development of new materials and increasingly complex structures. This leads to the development of increasingly robust and sensitive SHM methodologies and techniques. Damage Identification by means of intelligent signal processing and optimization algorithms based in vibration metrics are particularly emphasized in this paper. The methods discussed here are mainly elaborated by the evaluation of vibrational and modal data due to the great potential (and relatively easy to apply) of application. This article discusses the use of optimization algorithms and Artificial Neural Networks (ANN) for structural monitoring in the form of a brief review. This paper can be seen as a starting point of developing SHM systems and data analysis. The content of this paper aims to help engineers and researchers find a better alternative to their specific structural monitoring problems.

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Acknowledgements

The authors would like to acknowledge the financial support from the Brazilian agency CNPq—Conselho Nacional de Desenvolvimento Científico e Tecnológico and CAPES—Coordenação de Aperfeiçoamento de Pessoal de Nível Superior.

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  1. Mechanical Engineering Institute, Federal University of Itajubá, Itajubá, Brazil

    Guilherme Ferreira Gomes, Yohan Ali Diaz Mendez, Patrícia da Silva Lopes Alexandrino, Sebastiao Simões da Cunha Jr. & Antonio Carlos Ancelotti Jr.

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  1. Guilherme Ferreira Gomes
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Gomes, G.F., Mendez, Y.A.D., da Silva Lopes Alexandrino, P. et al. A Review of Vibration Based Inverse Methods for Damage Detection and Identification in Mechanical Structures Using Optimization Algorithms and ANN. Arch Computat Methods Eng 26, 883–897 (2019). https://doi.org/10.1007/s11831-018-9273-4

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