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论文类型：期刊论文

发表时间：2019-05-01

发表刊物：JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS

收录刊物：SCIE、EI

卷号：188

页面范围：30-42

ISSN号：0167-6105

关键字：Wind pressure interpolation; Machine learning; Gaussian process regression; Low-rise building

摘要：This study focuses on using limited wind tunnel test records to interpolate surface wind pressures where no measuring taps are arranged or under wind directions that are not measured. Gaussian process regression (GPR), a machine learning method, is first introduced to investigate the issue. For the interpolations of lower-order moments, time histories, higher-order moments, and wind pressure cumulative distribution functions (CDF), several novel GPR-based approaches are proposed. The newly proposed time history interpolation approach can capture the slight variation of wind pressure field with time, owing to the invocation of the time-varying GPR models. Interpolations of skewness, kurtosis, and wind pressure CDFs are attempted for the first time. Taking the surface pressures of a rectangular flat-roof low-rise building as an example, all the proposed approaches are comprehensively discussed, and their interpolation accuracy and superiority are demonstrated by comparing the interpolation results with the measured data and with the outputs from current state-of-the-art approaches.