Damage Detection in Building Structures Using Modified Feature Selection and Optimization Algorithm
Publication: Practice Periodical on Structural Design and Construction
Volume 29, Issue 4
Abstract
This study addresses the challenge of detecting operational and environmental changes, termed linear damages, in building structures over their lifespan. Identification of these damages is crucial for enhancing serviceability and averting sudden disasters. However, the intricate nature of uncovering concealed changes results in demanding and time-intensive computations, posing a significant computational predicament for related algorithms. Moreover, structures are often exposed to diverse environmental noise, necessitating the development of a robust algorithm capable of effectively identifying subtly hidden damages amid varying noisy conditions with high accuracy and low time consumption. This research introduces a robust and expedited signal-based algorithm, comprising three key components: processing, feature selection, and classification. Multiresolution analysis through discrete wavelet transform is employed for processing, generating diverse features alongside several statistical indices. The grey wolf optimization algorithm is utilized for feature selection, yielding optimal features. This method not only ensures commendable performance under noisy circumstances compared with optimization algorithms such as particle swan optimization and genetic algorithms, as well as common feature extraction methods such as principal component analysis, it also accelerates computation speed by over four times compared with alternative feature-selection techniques such as ReliefF. Lastly, a supervised classification algorithm is integrated to discern distinct predefined scenarios. The efficacy of the proposed algorithm was validated using a comprehensive case study encompassing nine representative scenarios of operational and environmental damages. Incorporating four levels of noise to emulate real-world variations, the algorithm achieved compelling average accuracies of approximately 96%, 93%, 95%, 91.5%, and 89% at original data and signal-to-noise ratios (SNRs) of 10, 5, 1, and 0.5 dB, respectively.
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Data Availability Statement
All data that support the findings of this study are available from the corresponding author upon reasonable request.
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Published In
Practice Periodical on Structural Design and Construction
Volume 29 • Issue 4 • November 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Aug 9, 2023
Accepted: Mar 13, 2024
Published online: Jul 17, 2024
Published in print: Nov 1, 2024
Discussion open until: Dec 17, 2024
ASCE Technical Topics:
- Algorithms
- Architectural engineering
- Building management
- Buildings
- Case studies
- Continuum mechanics
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering mechanics
- Linear functions
- Mathematical functions
- Mathematics
- Methodology (by type)
- Models (by type)
- Motion (dynamics)
- Optimization models
- Oscillations
- Research methods (by type)
- Serviceability
- Solid mechanics
- Structural engineering
- Structures (by type)
- Wavelets
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