基于机器学习的高强钢焊接等截面箱型柱整体稳定性预测方法

目前，针对高强钢构件整体稳定性的研究多采用有限元建模或实验室试验方法，而基于机器学习的预测方法能够显著提升预测的准确性和便捷性。为了准确预测高强钢焊接等截面箱型柱的整体稳定性，提出使用纤维模型构建数据库并利用机器学习建立预测模型的方法。首先确定模型的输入输出参数，并通过纤维模型方法建立数据库；接着，选用常见的3种不同类型的机器学习模型和现有规范中的经验模型进行预测，并依据评价指标进行性能对比；最后，根据可解释算法分析机器学习模型的合理性。结果表明：大部分机器学习模型预测结果与试验结果吻合度略高于现有规范中的经验模型，其中，高斯过程回归模型对高强钢构件整体稳定性的预测表现最优；机器学习预测模型中各类参数对构件整体稳定性的影响趋势符合预期，验证了机器学习模型的合理性和可靠性；构件的正则化长细比对预测结果影响最大，而构件初始缺陷的影响相对最小。

Machine learning method for overall stability of welded constant section box columns made of high strength steel

At present, finite element modeling or laboratory testing methods are generally used in the research of the overall stability of high-strength steel members. However, the prediction method based on machine learning (ML) has greatly improved the accuracy and convenience of component performance prediction. To accurately predict the overall stability of welded constant section box columns made of high strength steel, ML method together with a database based on the fiber model is proposed in this paper. Firstly, the input and output parameters of the model are determined, and the database is provided. Then, three different ML models and empirical models in the existing specifications are selected for prediction, and the performance is compared according to the evaluation index. Finally, the rationality of ML models is analyzed according to interpretable algorithms. The results show that the prediction results of most ML models are in good agreement with the experimental results, which are slightly higher than the empirical models, and the Gaussian process regression model has the best prediction performance for the overall stability of high-strength steel members; the influential trend of various parameters on the overall stability of components meets the expectation, which verifies the rationality and reliability of the ML model; the regularized slenderness ratio has the greatest influence on the prediction results, while the initial defects have the least.