Dynamic-based damage identification using neural network ensembles and damage index method
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This paper presents a vibration-based damage identification method that utilises a “damage fingerprint” of a structure in combination with Principal Component Analysis (PCA) and neural network techniques to identify defects. The Damage Index (DI) method is used to extract unique damage patterns from a damaged beam structure with the undamaged structure as baseline. PCA is applied to reduce the effect of measurement noise and optimise neural network training. PCA-compressed DI values are, then, used as inputs for a hierarchy of neural network ensembles to estimate locations and severities of various damage cases. The developed method is verified by a laboratory structure and numerical simulations in which measurement noise is taken into account with different levels of white Gaussian noise added. The damage identification results obtained from the neural network ensembles show that the presented method is capable of overcoming problems inherent in the conventional DI method. Issues associated with field testing conditions are successfully dealt with for numerical and the experimental simulations. Moreover, it is shown that the neural network ensemble produces results that are more accurate than any of the outcomes of the individual neural networks.
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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 [247]
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Self-citation
- Authors
- Dackermann, U., Li, J., & Samali, B.
- Affiliations
- University of Technology Sydney (AU)
- Corresponding author
- Dackermann, Ulrike
- Venue
- Advances in Structural Engineering
- Publisher
- SAGE Publications
- Published
- 2010-12
- Volume
- 13
- Issue
- 6
- Pages
- 1001-1016
- ISSN
- 1369-4332 (ISSN-L)
- 2048-4011
- OpenAlex
- W210515905
- Cited by
- 66
- References
- 44
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 5 · 2023: 9 · 2022: 8 · 2021: 9 · 2020: 10 · 2019: 6 · 2018: 1 · 2017: 3 · 2016: 3 · 2015: 2 · 2014: 1 · 2013: 2
- FWCI
- 2.03
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Advances in Structural Engineering (publishedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Keywords
- damage detection, vibration-based structural health monitoring, neural network ensembles, principal component analysis, damage localization, damage severity estimation, measurement noise, beams
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