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Machine Learning Techniques and Population-Based Metaheuristics for Damage Detection and Localization Through Frequency and Modal-Based Structural Health Monitoring: A Review

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Abstract

Vibration-based damage detection techniques, and particularly frequency and modal-based methods, address the problem of localization and quantification of damage in a structure by using observed changes in its dynamic properties. Most of these procedures are based on an optimal criterion in which the stiffness distribution of an element into a structural system is iteratively updated in order to match the computed natural frequencies with the measured ones at a certain level of deterioration. This paper provides a comprehensive review of the optimization methodologies based on random search procedures that have been employed to find practical solutions in the forward and inverse frequency and modal-based Structural Health Monitoring (SHM) problem. Over the last three decades, the application of machine learning approaches and population-based metaheuristics to solve real-engineering optimization problems has grown massively, especially in the SHM field. Therefore, the literature review provided in this paper is organized into three main sections: (1) Machine learning techniques; (2) Population-based metaheuristics; and (3) coupled methodologies. Finally, the purpose of this paper is to provide the reader with a wider understanding and a critical point of view of frequency and modal-based techniques for structural damage detection, and the various computational methods that have been employed to solve the problem.

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Fig. 1

source: Bektas et al. [115])

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Source: Kim et al. [166])

Fig. 4

Source: Zenzen et al. [190])

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  1. Departamento de Ingeniería Civil, Facultad de Minas, Centro de Proyectos e Investigaciones Sísmicas (CPIS), Universidad Nacional de Colombia, Sede Medellín, 75267, Medellín, Colombia

    Daniel Caicedo, Luis Lara-Valencia & Yamile Valencia

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DC: Conceptualization, methodology, formal analysis, investigation, resources, visualization, writing—original draft, supervision, writing—reviewing and editing. LLV: Conceptualization, methodology, formal analysis, investigation, resources, visualization, writing—original draft, supervision, writing—reviewing and editing. YVG: Formal analysis, investigation, resources, visualization, supervision, writing—reviewing and editing.

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Caicedo, D., Lara-Valencia, L. & Valencia, Y. Machine Learning Techniques and Population-Based Metaheuristics for Damage Detection and Localization Through Frequency and Modal-Based Structural Health Monitoring: A Review. Arch Computat Methods Eng 29, 3541–3565 (2022). https://doi.org/10.1007/s11831-021-09692-6

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