A transfer-learning framework to alleviate data scarcity in cross-slope wind pressure modeling

• Four high-performance ensemble learning models are employed to predict the wind pressure distribution on low‑rise building roofs. • Model uncertainty is quantified through the coefficient of variation (CV) and bootstrap method. • The proposed transfer learning framework, built upon ensemble methods, accurately forecasts the wind pressure distribution for target structures with limited data. • Advanced SHAP analysis is integrated with the developed models to provide comprehensive interpretability of their predictions. In the context of rapid urbanization and growing incidence of extreme wind events, wind pressure prediction on low-rise building roofs has gained considerable research attention. However, there…

• NS
  Natural Science Foundation of Shandong Province
• NS
  Natural Science Foundation of Qingdao