Author:
Gomez-Cabrera Alain,Escamilla-Ambrosio Ponciano Jorge
Abstract
This review identifies current machine-learning algorithms implemented in building structural health monitoring systems and their success in determining the level of damage in a hierarchical classification. The integration of physical models, feature extraction techniques, uncertainty management, parameter estimation, and finite element model analysis are used to implement data-driven model detection systems for SHM system design. A total of 68 articles using ANN, CNN and SVM, in combination with preprocessing techniques, were analyzed corresponding to the period 2011–2022. The application of these techniques in structural condition monitoring improves the reliability and performance of these systems.
Funder
Instituto Politécnico Nacional
Consejo Nacional de Ciencia y Tecnología
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Cited by
21 articles.
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