TY - JOUR
T1 - Review of Machine-Learning Techniques Applied to Structural Health Monitoring Systems for Building and Bridge Structures
AU - Gomez-Cabrera, Alain
AU - Escamilla-Ambrosio, Ponciano Jorge
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022/11
Y1 - 2022/11
N2 - 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.
AB - 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.
KW - building structures
KW - data-based model
KW - machine learning
KW - physics-based model
KW - structural health monitoring
UR - http://www.scopus.com/inward/record.url?scp=85141822179&partnerID=8YFLogxK
U2 - 10.3390/app122110754
DO - 10.3390/app122110754
M3 - Artículo de revisión
AN - SCOPUS:85141822179
SN - 2076-3417
VL - 12
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
IS - 21
M1 - 10754
ER -