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A Hybrid Semi-supervised Regression Based Machine Learning Method for Predicting Peak Wind Loads on a Group of Buildings by Kun Du; Bo Chen is a Engineering article available to read on EtoBox.

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This study proposes a new hybrid machine learning method (named as SEC) to predict peak wind loads on a group of buildings. The SEC method integrates the semi-supervised regression, extreme learning machine, and computational fluid dynamics (CFD). Wind loads on a group of buildings are sensitive to many influencing factors, and there are not sufficient enough wind tunnel test samples for machine learning prediction. SEC uses semi-supervised regression to construct pseudo-labeled samples of untested buildings and mixes them with existing samples to expand training samples to improve the prediction precision. Wind tunnel tests demonstrate that peak wind loads sometimes have a strong relationship with the building spacing or alter rapidly with the variation of building spacings. CFD can calculate mean wind loads much more accurately than peak wind loads at low costs. SEC adds mean wind loads of untested buildings simulated by CFD as the input variable in the prediction model to weaken the nonlinear relationship between the input variables and peak wind loads and further improve the prediction accuracy. The results of the case study of a group of three flat-roof buildings show that the

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Author

Kun Du; Bo Chen

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)