Review of Machine-Learning Techniques Applied to Structural Health Monitoring Systems for Building and Bridge Structures

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

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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