Vu, V.T. and Thom, D.V. and Tran, T.D. (2024) Identification of damage in steel beam by natural frequency using machine learning algorithms. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. ISSN 09544062
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In recent times, the efficacy of machine learning (ML) algorithms as tools for forecasting structural damage has become increasingly evident. However, input data in structural health monitoring predominantly comprises normal operational states or states with minor deviations from the initial condition, lacking potentially hazardous states. Consequently, creating a realistic dataset for machine learning models to identify structural damage poses a challenge. If such data were obtainable, it might involve parameters like stress intensity factor range and stress ratio, which are often difficult to measure within real structures. In this paper, ML models, including Artificial Neural Network (ANN), Extreme Gradient Boosting (XGB), and Random Forest (RF), were constructed to predict the locations, widths, and depths of saw-cuts in steel beams. The prognostications were based on fluctuations in natural frequencies. The natural frequencies under various damage scenarios were identified using the Finite Element Method (FEM). The natural frequencies in the absence of saw-cuts, obtained from the two methods, Finite Element Method (FEM) and Frequency Domain Decomposition (FDD), were compared to validate their agreement. Conclusions regarding the selection of appropriate machine learning models, as well as the combination of FEM, FDD, and machine learning methods, will be drawn upon completion. © IMechE 2024.
Item Type: | Article |
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Divisions: | Offices > Office of International Cooperation |
Identification Number: | 10.1177/09544062241255570 |
Uncontrolled Keywords: | Adaptive boosting; Damage detection; Domain decomposition methods; Forecasting; Forestry; Frequency domain analysis; Machine learning; Natural frequencies; Neural networks; Steel beams and girders; Structural health monitoring, Dynamics analysis; Extreme gradient boosting; Finite element method dynamic analyse; Frequency domain decomposition; Gradient boosting; Machine learning algorithms; Machine learning models; Random forests; Saw-cut prediction; Steel beams, Finite element method |
URI: | http://eprints.lqdtu.edu.vn/id/eprint/11276 |