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Machine Learning Prediction on Progressive Collapse Resistance of Purely Welded Steel Frames Considering Weld Defects

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Udgivet i:Buildings vol. 15, no. 22 (2025), p. 4174-4209
Hovedforfatter: Guo Zikang
Andre forfattere: Yu, Peng, Huang Xinheng, Yao Yingkang, Zhang, Chunwei
Udgivet:
MDPI AG
Fag:
Load
Finite element method
Height
Diameters
Thickness ratio
Machine learning
Hypercubes
Influence
Learning algorithms
Strain gauges
Welding
Bayesian analysis
Artificial intelligence
Frames (data processing)
Collapse
Steel
Weld defects
Ductility
Optimization
Yield stress
Steel frames
Welded joints
Mathematical models
Catastrophic collapse
Latin hypercube sampling
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Resumen: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.
ISSN:2075-5309
DOI:10.3390/buildings15224174
Fuente:Engineering Database