土木与环境工程学报（中英文）2024，Vol.46Issue(1)：182-193,12.DOI:10.11835/j.issn.2096-6717.2022.131
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
Machine learning method for overall stability of welded constant section box columns made of high strength steel
摘要
Abstract
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.关键词
机器学习/高强钢/整体稳定性/预测模型/纤维模型Key words
machine learning/high-strength steel/overall stability/prediction model/fiber model分类
建筑与水利引用本文复制引用
张营营,徐浩,陈培见,马俊,周祎..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[J].土木与环境工程学报（中英文）,2024,46(1):182-193,12.基金项目
国家自然科学基金(52278229)National Natural Science Foundation of China(No.52278229) （52278229）
