Machine Learning Prediction on Progressive Collapse Resistance of Purely Welded Steel Frames Considering Weld Defects
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| Xuất bản năm: | Buildings vol. 15, no. 22 (2025), p. 4174-4209 |
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| Tác giả chính: | |
| Tác giả khác: | , , , |
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MDPI AG
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| Truy cập trực tuyến: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2075-5309 | ||
| 024 | 7 | |a 10.3390/buildings15224174 |2 doi | |
| 035 | |a 3275507947 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231437 |2 nlm | ||
| 100 | 1 | |a Guo Zikang |u State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, China; dipinory@tju.edu.cn (Z.G.); | |
| 245 | 1 | |a Machine Learning Prediction on Progressive Collapse Resistance of Purely Welded Steel Frames Considering Weld Defects | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This study proposes a machine learning (ML) framework to predict the progressive collapse resistance of purely welded steel frames considering weld defects. A finite element model (FEM) incorporating weld weakening degree at joints was developed and validated against push-down tests. A parametric modelling program, combined with Latin Hypercube Sampling (LHS), was used to generate 700 samples from 27 design features across 8 categories, establishing a progressive collapse database containing full-process resistance curves. Five ML algorithms—DNN, SVR, RF, XGBoost, and LightGBM—were trained and evaluated. SVR was identified as the optimal model through Bayesian hyperparameter optimization and K-fold cross-validation, achieving an R2 = 0.988 and sMAPE = 5.096% in predicting the full-process resistance response. SHAP analysis was employed to examine feature interpretations both locally and globally, revealing that the failure scenario, beam span-to-height ratio, and weld quality are the three most significant factors affecting structural resistance, accounting for 22.6%, 22.5%, and 16% of the overall influence, respectively. For practical design, a steel frame with a beam span-to-height ratio of approximately 15, a weld joint relative position ratio between 0.15 and 0.18, a circular stub diameter-to-beam width ratio around 1.8, and a stub diameter-to-thickness ratio near 13 can achieve superior progressive collapse robustness, provided that weld quality is ensured. | |
| 653 | |a Load | ||
| 653 | |a Finite element method | ||
| 653 | |a Height | ||
| 653 | |a Diameters | ||
| 653 | |a Thickness ratio | ||
| 653 | |a Machine learning | ||
| 653 | |a Hypercubes | ||
| 653 | |a Influence | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Strain gauges | ||
| 653 | |a Welding | ||
| 653 | |a Bayesian analysis | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Frames (data processing) | ||
| 653 | |a Collapse | ||
| 653 | |a Steel | ||
| 653 | |a Weld defects | ||
| 653 | |a Ductility | ||
| 653 | |a Optimization | ||
| 653 | |a Yield stress | ||
| 653 | |a Steel frames | ||
| 653 | |a Welded joints | ||
| 653 | |a Mathematical models | ||
| 653 | |a Catastrophic collapse | ||
| 653 | |a Latin hypercube sampling | ||
| 700 | 1 | |a Yu, Peng |u State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, China; dipinory@tju.edu.cn (Z.G.); | |
| 700 | 1 | |a Huang Xinheng |u School of Civil Engineering, Tianjin University, Tianjin 300350, China | |
| 700 | 1 | |a Yao Yingkang |u State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, China; dipinory@tju.edu.cn (Z.G.); | |
| 700 | 1 | |a Zhang, Chunwei |u State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, China; dipinory@tju.edu.cn (Z.G.); | |
| 773 | 0 | |t Buildings |g vol. 15, no. 22 (2025), p. 4174-4209 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3275507947/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3275507947/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3275507947/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |