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Prediction of Mechanical Properties of the Stirrup-Confined Rectangular CFST Stub Columns Using FEM and Machine Learning
In this study, a machine learning method using gradient boost regression tree (GBRT) model was presented to predict the ultimate bearing capacity of stirrup-confined rectangular CFST stub columns (SCFST) by using a comprehensive data set and by adjusting the selected parameters indicated in the previous research (B, D, t, ρsa, fcu, fs). The advantage of GBRT is its strong predictive ability, which can naturally handle different types of data and very robust processing of outliers out of space.doi:10.3390/math9141643 fatcat:xujrovof4jeg5esfyzldupl66u