This study investigates the performance and robustness of regression machine-learning models in the presence of variability in the experimental database. The main objective of this work is to predict the ultimate load of circular concrete-filled steel tubes. The simulations were designed by combining size of the learning dataset, random realizations and prediction models. The variability (i.e. probability density function of each variable) is propagated to the output response through the regression machine-learning models. Results show that such variability must be considered when training and testing regression machine-learning models. The performance and robustness of the prediction models are presented and discussed. Based on the most robust and efficient model, a prediction equation is proposed for practical use. After conducting a comparison investigation, the performance of the proposed equation is found superior to one of current models. Finally, the proposed equation is implemented in Excel and appended to this paper.

这项研究调查了在实验数据库中存在可变性的情况下回归机器学习模型的性能和鲁棒性。这项工作的主要目的是预测圆形混凝土钢管的极限载荷。通过结合学习数据集的大小，随机实现和预测模型来设计仿真。变异性（即每个变量的概率密度函数）通过​​回归机器学习模型传播到输出响应。结果表明，在训练和测试回归机器学习模型时必须考虑这种可变性。提出并讨论了预测模型的性能和鲁棒性。基于最鲁棒和最有效的模型，提出了一个实用的预测方程。经过比较研究，发现所提出方程的性能优于当前模型之一。最后，所提出的方程在Excel中实现并附加到本文中。