- Title
- Proposed machine learning techniques for bridge structural health monitoring : a laboratory study
- Creator
- Noori Hoshyar, Azadeh; Rashidi, Maria; Yu, Yang; Samali, Bijan
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/194484
- Identifier
- vital:18361
- Identifier
-
https://doi.org/10.3390/rs15081984
- Identifier
- ISSN:2072-4292 (ISSN)
- Abstract
- Structural health monitoring for bridges is a crucial concern in engineering due to the degradation risks caused by defects, which can become worse over time. In this respect, enhancement of various models that can discriminate between healthy and non-healthy states of structures have received extensive attention. These models are concerned with implementation algorithms, which operate on the feature sets to quantify the bridge’s structural health. The functional correlation between the feature set and the health state of the bridge structure is usually difficult to define. Therefore, the models are derived from machine learning techniques. The use of machine learning approaches provides the possibility of automating the SHM procedure and intelligent damage detection. In this study, we propose four classification algorithms to SHM, which uses the concepts of support vector machine (SVM) algorithm. The laboratory experiment, which intended to validate the results, was performed at Western Sydney University (WSU). The results were compared with the basic SVM to evaluate the performance of proposed algorithms. © 2023 by the authors.
- Publisher
- MDPI
- Relation
- Remote Sensing Vol. 15, no. 8 (2023), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2023 by the authors
- Rights
- Open Access
- Subject
- 3701 Atmospheric sciences; 3709 Physical geography and environmental geoscience; 4013 Geomatic engineering; Anomaly detection; Bridge monitoring; Crack detection; Feature extraction; Feature selection; Machine learning; Structures
- Full Text
- Reviewed
- Hits: 939
- Visitors: 985
- Downloads: 59
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Published version | 3 MB | Adobe Acrobat PDF | View Details Download |