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| Prediction of buckling capacity of high-strength steel columns based on improved stacking ensemble learning |
| Received:January 12, 2022 Revised:February 28, 2022 |
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| DOI：10.7511/jslx20220112003 |
| KeyWord:buckling strength standard Stacking algorithm GSSA model Bland-Altman method SHAP model |
| Author | Institution |
| 何智成 |
湖南大学 汽车车身先进设计制造国家重点实验室, 长沙 |
| 韩茳 |
湖南大学 汽车车身先进设计制造国家重点实验室, 长沙 |
| 宋贤海 |
南昌航空大学 材料科学与工程学院, 南昌 |
| 张桂勇 |
大连理工大学 工业装备结构分析优化与CAE软件全国重点实验室 船舶工程学院, 大连 |
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| Abstract: |
| The existing knowledge of buckling strength is insufficient 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 in recent years.However,the small samples based on experimental tests easily cause 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. |
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