欢迎光临《计算力学学报》官方网站!
 
基于改进Stacking集成学习的高强度钢柱屈曲能力预测
Prediction of Buckling Capacity of High-Strength Steel Columns Based on Improved Stacking Ensemble Learning
投稿时间:2022-01-12  修订日期:2022-02-26
DOI:
中文关键词:  屈曲强度  Stacking算法  GSSA模型  Bland-Altman法  SHAP模型
英文关键词:buckling strength  standard Stacking algorithm  GSSA model  Bland-Altman method  SHAP model
基金项目:国家自然科学基金联合基金(U20A20285);湖南省杰出青年科学基金(2021JJ10016)资助项目.
作者单位邮编
何智成 湖南大学汽车车身先进设计制造国家重点实验室 410082
韩茳 湖南大学汽车车身先进设计制造国家重点实验室 
宋贤海 南昌航空大学材料科学与工程学院 330036
张桂勇 大连理工大学船舶工程学院 
摘要点击次数: 159
全文下载次数: 0
中文摘要:
      由于屈曲强度的形成机制复杂,影响屈曲强度的因素较多,目前对屈曲强度的认识还不全面。近年来,机器学习已初步应用于预测结构屈曲强度等力学性能,并用于指导其性能设计优化,然而基于实验测试的样本数据量小容易造成过拟合,导致其预测精度低。本文提出一种基于改进Stacking算法的Grid Search-Stacking Algorithm (GSSA)模型,并对某型号高强度钢柱屈曲强度进行预测,提升了屈曲强度的预测精度。首先,基于标准Stacking算法通过使用网格搜索算法选择最优基模型组合,并采用留一交叉验证(LOOCV)法训练基模型,实现了GSSA模型的构建,有效解决了小样本集训练带来的预测精度低问题;然后,为了进一步验证GSSA模型的可靠性,本文采用Bland-Altman法对GSSA模型进行了一致性评价,结果表明,GSSA模型具有很好的可靠性;最后,本文采用SHAP模型对GSSA模型预测的屈曲强度进行了可解释性分析,实现了其影响因素评价,为新型钢结构的设计提供了指导方向。
英文摘要:
      The existing knowledge of buckling strength is incomplete due to the complicated formation mechanism of buckling strength, which is influenced by various factors. Machine learning has been initially applied to predict mechanical properties of structures such as buckling strength and to guide their performance design optimization in recent years. However, the small amount of sample data based on experimental tests easily causes overfitting, resulting in low prediction accuracy. In this paper, a Grid Search-Stacking Algorithm (GSSA) model based on the improved Stacking Algorithm was proposed to predict the buckling strength of high-strength steel columns, and the prediction accuracy of buckling strength is improved. First, based on the standard Stacking algorithm, the GSSA model was constructed by employing the grid search algorithm to select the optimal combination of base models and the leave-one-out cross-validation (LOOCV) method to train the base models, which effectively solves the problem of low prediction accuracy caused by the training of small samples; then, in order to further verify the reliability of the GSSA model, this paper adopted the Bland-Altman method to evaluate the consistency of the GSSA model, and the results show that the GSSA model has high reliability; finally, the SHAP model was introduced to perform an interpretability analysis of buckling strength predicted by the GSSA model and realizes the evaluation of its influence factors, which provides a guiding direction for the design of new steel structures.
  查看/发表评论  下载PDF阅读器
您是第10087967位访问者
版权所有:《计算力学学报》编辑部
本系统由 北京勤云科技发展有限公司设计