Review of Machine-Learning Techniques Applied to Structural Health Monitoring Systems for Building and Bridge Structures
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Structural Health Monitoring Techniques · Infrastructure Maintenance and Monitoring · Concrete Corrosion and Durability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City 07738, Mexico
Funding
Consejo Nacional de Ciencia y Tecnología (CONACYT) · Instituto Politécnico Nacional (IPN)
References (100+)
Show source-provided references
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