Journal Article

Prediction of Mechanical Properties of the Stirrup-Confined Rectangular CFST Stub Columns Using FEM and Machine Learning

Lu, Deren, Chen, Zhidong, Sun, Peng, Chen, Zhenming, Ding, Faxing

2021

Overview

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. The comprehensive data set obtained from the FEM method which has been verified the accuracy and rationality by the existing literature. In order to make the data group closer to the engineering example, a large amount of experimental data collected in the literature was added to the data group to enhance the accuracy of the model. We compare a few regression models simply and the results show that the GBRT model has a good predictive effect on the mechanical properties of CFST columns. In summary, it can help pre-investigations for the CFST columns.

• Publisher: MDPI AG
• Subject: Algorithms / Artificial intelligence / Bearing capacity / Civil engineering / Concrete / Datasets / Earthquakes / Experiments / finite element analyses / Food science / gradient boost regression tree (GBRT) model / High rise buildings / Machine learning / machine learning method / Mechanical properties / Model accuracy / Neural networks / Outliers (statistics) / prediction / Regression analysis / Regression models / Reinforced concrete / Seismic engineering / Software / stirrup-confined rectangular CFST stub columns / Support vector machines