Soft Computing Applied to Rotation Capacity of Wide Flange Beams
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The rotation capacity (Ʀ) of steel beams is a physical factor that indicates the ductility of a structural member. This information is most useful in severe conditions like earthquakes, for example. In fact, Ʀ is a deciding factor in the plastic design of wide flange beams. To simplify the calculation of Ʀ, soft computing techniques could be applied. In this paper, the various attributes that govern Ʀ have been obtained from a wide experimental database, gathered from previously conducted experiments. To develop a model that accurately predicts Ʀ, four models—support vector machine, relevance vector machine, Gaussian process regression and generalized regression neural network—have been considered. These models have been tested and trained with the data collected. The models have then been validated and compared to arrive at the best one. Such efforts could go a long way in helping determine the Ʀ of wide flange steel beams and contribute to better design of structural members.
KeywordsRotation capacity Support vector machine Relevance vector machine Gaussian process regression Generalized regression neural network
This work was supported by Natural Hazard Minimizing Research Group funded by Ministry of Public Safety and Security (MPSS-자연-2015-78).
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