Simultaneous identification of structural damage and nonlinear hysteresis parameters by an evolutionary algorithm-based artificial neural network
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ANN approximates the nonlinear function between inputs and outputs with high accuracy using historical data.
“Using historical data, it approximates the nonlinear function between inputs and outputs with high accuracy [245-247].”
From Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review (2023) · cites this paper as [245]
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- Authors
- Ding, Z., Li, J., & Hao, H.
- Affiliations
- Hong Kong Polytechnic University (HK)
- Curtin University (AU)
- Corresponding author
- Li, Jun
- Venue
- International Journal of Non-Linear Mechanics
- Publisher
- Elsevier BV
- Published
- 2022-06
- Volume
- 142
- Pages
- 103970
- ISSN
- 0020-7462 (ISSN-L)
- OpenAlex
- W4212926153
- Cited by
- 22
- References
- 42
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 9 · 2023: 4 · 2022: 4
- FWCI
- 2.36
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Non-Linear Mechanics (publishedVersion)
- Funders
- China Scholarship Council (CSC 201606380106)
- Curtin University of Technology
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Keywords
- structural damage detection, evolutionary algorithms, artificial neural networks, principal component analysis, dimensionality reduction, accelerometer data, vanishing and exploding gradients, local minima, structural health monitoring
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