Abstract

In this paper, a surrogate Machine-Learning (ML) model based on Gaussian Process Regression (GPR) was developed to predict the axial load of square concrete-filled steel tubular (CFST) columns under compression. For this purpose, an experimental database was extracted from the available literature and used for the development and training of the GPR model. The GPR model’s performance is superior to that of existing models in relation to the axial load of square CFST columns. For practical application, a Graphical User Interface (GUI) was developed for researchers, engineers to support the teaching and interpretation of the axial behavior of CFST columns.

摘要

在本文中，开发了一种基于高斯过程回归 (GPR) 的替代机器学习 (ML) 模型来预测方形钢管混凝土 (CFST) 柱在受压下的轴向载荷。为此，从现有文献中提取了一个实验数据库，用于 GPR 模型的开发和训练。GPR模型在方形CFST柱的轴向载荷方面的性能优于现有模型。对于实际应用，为研究人员、工程师开发了图形用户界面 (GUI)，以支持对 CFST 柱的轴向行为的教学和解释。