Assessment of Knot-Induced Degradation in Timber Beams: Probabilistic Modeling and Data-Driven Prediction of Load Capacity Loss
Timber structural performance is significantly influenced by natural knots, which serve as critical indicators in ancient architectural heritage preservation and modern sustainable building design. However, existing studies lack a comprehensive quantitative analysis of how the randomness of timber k...
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MDPI AG
2025-06-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/12/2058 |
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| author | Peixuan Wang Guoming Liu Fanrong Li Shengcai Li Gabriele Milani Donato Abruzzese |
| author_facet | Peixuan Wang Guoming Liu Fanrong Li Shengcai Li Gabriele Milani Donato Abruzzese |
| author_sort | Peixuan Wang |
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| description | Timber structural performance is significantly influenced by natural knots, which serve as critical indicators in ancient architectural heritage preservation and modern sustainable building design. However, existing studies lack a comprehensive quantitative analysis of how the randomness of timber knot parameters relates to load-bearing capacity degradation. This study introduces a multiscale evaluation framework that integrates physical testing, probabilistic modeling, and data-driven techniques. Firstly, static tests on full-scale timber beams with artificially introduced knots reveal the failure mechanisms and load capacity reduction associated with knots in the tension zone. Subsequently, a three-dimensional Monte Carlo simulation, modeling random distributions of knot position and size, demonstrates that the midspan region is most sensitive to knot effects, with load capacity loss being more pronounced on the tension side than on the compression side. Finally, a predictive model based on a fully connected neural network is developed; feature analysis indicates that the longitudinal position of knots exerts a stronger nonlinear influence on load capacity than radial depth or diameter. The results establish a mapping between knot characteristics, stress field distortion, and ultimate load capacity, providing a theoretical basis for safety evaluation of historic timber structures and the design of defect-tolerant timber beams in modern engineering. |
| format | Article |
| id | doaj-art-a7ebb2ee7c444fd49bd986e6c0f8421c |
| institution | Matheson Library |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Buildings |
| spelling | doaj-art-a7ebb2ee7c444fd49bd986e6c0f8421c2025-06-25T13:35:56ZengMDPI AGBuildings2075-53092025-06-011512205810.3390/buildings15122058Assessment of Knot-Induced Degradation in Timber Beams: Probabilistic Modeling and Data-Driven Prediction of Load Capacity LossPeixuan Wang0Guoming Liu1Fanrong Li2Shengcai Li3Gabriele Milani4Donato Abruzzese5School of Architectural Science and Engineering, Yangzhou University, Yangzhou 225012, ChinaSchool of Architectural Science and Engineering, Yangzhou University, Yangzhou 225012, ChinaDepartment Civil Engineering & Computer Science, University of Rome “Tor Vergata”, 00133 Rome, ItalySchool of Architectural Science and Engineering, Yangzhou University, Yangzhou 225012, ChinaDepartment of Architecture, Built Environment and Construction Engineering (ABC), Politecnico di Milano, 20133 Milano, ItalyDepartment Civil Engineering & Computer Science, University of Rome “Tor Vergata”, 00133 Rome, ItalyTimber structural performance is significantly influenced by natural knots, which serve as critical indicators in ancient architectural heritage preservation and modern sustainable building design. However, existing studies lack a comprehensive quantitative analysis of how the randomness of timber knot parameters relates to load-bearing capacity degradation. This study introduces a multiscale evaluation framework that integrates physical testing, probabilistic modeling, and data-driven techniques. Firstly, static tests on full-scale timber beams with artificially introduced knots reveal the failure mechanisms and load capacity reduction associated with knots in the tension zone. Subsequently, a three-dimensional Monte Carlo simulation, modeling random distributions of knot position and size, demonstrates that the midspan region is most sensitive to knot effects, with load capacity loss being more pronounced on the tension side than on the compression side. Finally, a predictive model based on a fully connected neural network is developed; feature analysis indicates that the longitudinal position of knots exerts a stronger nonlinear influence on load capacity than radial depth or diameter. The results establish a mapping between knot characteristics, stress field distortion, and ultimate load capacity, providing a theoretical basis for safety evaluation of historic timber structures and the design of defect-tolerant timber beams in modern engineering.https://www.mdpi.com/2075-5309/15/12/2058timber knot defectsmultiscale evaluationMonte Carlo simulationneural networks |
| spellingShingle | Peixuan Wang Guoming Liu Fanrong Li Shengcai Li Gabriele Milani Donato Abruzzese Assessment of Knot-Induced Degradation in Timber Beams: Probabilistic Modeling and Data-Driven Prediction of Load Capacity Loss Buildings timber knot defects multiscale evaluation Monte Carlo simulation neural networks |
| title | Assessment of Knot-Induced Degradation in Timber Beams: Probabilistic Modeling and Data-Driven Prediction of Load Capacity Loss |
| title_full | Assessment of Knot-Induced Degradation in Timber Beams: Probabilistic Modeling and Data-Driven Prediction of Load Capacity Loss |
| title_fullStr | Assessment of Knot-Induced Degradation in Timber Beams: Probabilistic Modeling and Data-Driven Prediction of Load Capacity Loss |
| title_full_unstemmed | Assessment of Knot-Induced Degradation in Timber Beams: Probabilistic Modeling and Data-Driven Prediction of Load Capacity Loss |
| title_short | Assessment of Knot-Induced Degradation in Timber Beams: Probabilistic Modeling and Data-Driven Prediction of Load Capacity Loss |
| title_sort | assessment of knot induced degradation in timber beams probabilistic modeling and data driven prediction of load capacity loss |
| topic | timber knot defects multiscale evaluation Monte Carlo simulation neural networks |
| url | https://www.mdpi.com/2075-5309/15/12/2058 |
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