Extrapolating wind pressures on roof soffits of low-rise buildings using few-shot learning

Abstract

Current wind standards assume identical external wind pressure coefficients on roof soffits and adjacent wall surfaces. However, recent experiments suggest that this correlation significantly decreases with an increase in overhang width. This research introduces a few-shot learning model addressing the need for a machine learning approach that can efficiently obtain the wind pressure coefficients on roof soffits when the overhang width is large (i.e., larger than 2 ft). This research proposed a few-shot learning model to extrapolate the wind-induced pressures on roof soffits for low-rise buildings based on the wind tunnel dataset investigating the three large overhang widths (i.e., 2.4, 4.8, and 7.2 inches in 1:10 scale models). Prior knowledge relating to zonal information and wind directions shown in the standard of minimum design loads and associated criteria for buildings and other structures (i.e., ASCE 7) is incorporated into the model. The proposed few-shot learning model was trained on scale model buildings with overhang widths of 2.4 and 4.8 inches and tested on a 7.2-inch overhang width case. When predicting the minimum wind pressure coefficient for both the southside and eastside soffit surfaces, low mean-squared errors and high coefficient of determination values were observed. This study marks the first application of few-shot learning techniques to extrapolate wind pressures across different roof overhang widths and provide reliable predictions that outperform the weak correlation between the soffit and the adjacent wall surface assumed currently. This model reduces reliance on physical wind tunnel experiments and requires only a low-resolution measurement tap configuration.