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| Autori principali: | , , , |
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| Natura: | Artículo Open Access |
| Pubblicazione: |
Wiley
2025
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| Soggetti: | |
| Accesso online: | https://onlinelibrary.wiley.com/doi/10.1002/msd2.70049 |
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Sommario:
- Experimental Modal Analysis‐Driven Gaussian Process Regression Modeling of Rectangular Steel Beams With Various Hole Geometries Amir Hossein Rabiee Majid Rajaei Mohammad Hossein Mahdavi Nasab Parsa Shafiei International Journal of Mechanical System Dynamics ABSTRACTUnderstanding the dynamic behavior of structural components is crucial for optimizing performance and ensuring structural integrity. This study presents a new method that combines a systematic experimental investigation of four distinct hole geometries (circular, square, compact rectangular, and long rectangular) with varying hole counts, all designed to maintain equal material removal, and the application of Gaussian Process Regression to model and predict natural frequencies from experimental modal analysis. Experimental modal testing was conducted on 36 beam specimens to evaluate changes in vibrational properties. The results demonstrate a clear relationship between hole geometry and the attenuation of natural frequencies: circular holes resulted in minimal reductions due to uniform stress distribution, whereas square and rectangular holes caused greater stiffness loss, with the most substantial attenuation observed in beams with long rectangular holes where the first natural frequency decreased by up to 44%. The proposed Gaussian Process Regression model achieved high predictive accuracy on the test set, with coefficients of determination (R2) of approximately 0.98 for the first natural frequency, 0.93 for the second, and 0.92 for the third. Sensitivity analysis identified the number of holes as the most influential parameter, contributing 67.8% to variations in the first natural frequency. These findings provide practical guidelines for structural design by identifying optimal hole geometries and counts to achieve weight reduction while maintaining stiffness, and demonstrate the utility of Gaussian Process Regression for reliable modeling of vibrational behavior in perforated beams. 10.1002/msd2.70049 http://creativecommons.org/licenses/by/4.0/