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Computational predictions for predicting the performance of steel 1 panel shear wall under explosive loads

机译:用于预测爆炸载荷下钢1面板剪力墙性能的计算预测

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PurposeThe resistance of steel plate shear walls (SPSW) under explosive loads is evaluated using nonlinear FE analysis and surrogate methods. This study uses the conventional weapons effect program (CONWEP) model for the explosive load and the Johnson-Cook model for the steel plate. Based on the Taguchi method, 25 samples out of 100 samples are selected for a parametric study where we predict the damaged zones and the maximum deflection of SPSWs under explosive loads. Then, this study uses a multiple linear regression (MLR), multiple Ln equation regression (MLnER), gene expression programming (GEP), adaptive network-based fuzzy inference (ANFIS) and an ensemble model to predict the maximum detection of SPSWs. Several statistical parameters and error terms are used to evaluate the accuracy of the different surrogate models. The results show that the cross-section in the y-direction and the plate thickness have the most significant effects on the maximum deflection of SPSWs. The results also show that the maximum deflection is related to the scaled distance, i.e. for a value of 0.383. The ensemble model performs better than all other models for predicting the maximum deflection of SPSWs under explosive loads.Design/methodology/approachThe SPSW under explosive loads is evaluated using nonlinear FE analysis and surrogate methods. This study uses the CONWEP model for the explosive load and the Johnson-Cook model for the steel plate. Based on the Taguchi method, 25 samples out of 100 samples are selected for a parametric study where we predict the damaged zones and the maximum deflection of SPSWs under explosive loads. Then, this study uses a MLR, MLnER, GEP, ANFIS and an ensemble model to predict the maximum detection of SPSWs. Several statistical parameters and error terms are used to evaluate the accuracy of the different surrogate models. The results show that the cross-section in the y-direction and the plate thickness have the most significant effects on the maximum deflection of SPSWs. The results also show that the maximum deflection is related to the scaled distance, i.e. for a value of 0.383. The ensemble model performs better than all other models for predicting the maximum deflection of SPSWs under explosive loads.FindingsThe resistance of SPSW under explosive loads is evaluated using nonlinear FE analysis and surrogate methods. This study uses the CONWEP model for the explosive load and the Johnson-Cook model for the steel plate. Based on the Taguchi method, 25 samples out of 100 samples are selected for a parametric study where we predict the damaged zones and the maximum deflection of SPSWs under explosive loads. Then, this study uses a MLR, MLnER, GEP, ANFIS and an ensemble model to predict the maximum detection of SPSWs. Several statistical parameters and error terms are used to evaluate the accuracy of the different surrogate models. The results show that the cross-section in the y-direction and the plate thickness have the most significant effects on the maximum deflection of SPSWs. The results also show that the maximum deflection is related to the scaled distance, i.e. for a value of 0.383. The ensemble model performs better than all other models for predicting the maximum deflection of SPSWs under explosive loads.Originality/valueThe resistance of SPSW under explosive loads is evaluated using nonlinear FE analysis and surrogate methods. This study uses the CONWEP model for the explosive load and the Johnson-Cook model for the steel plate.Based on the Taguchi method, 25 samples out of 100 samples are selected for a parametric study where we predict the damaged zones and the maximum deflection of SPSWs under explosive loads. Then, this study uses a MLR, MLnER, GEP, ANFIS and an ensemble model to predict the maximum detection of SPSWs. Several statistical parameters and error terms are used to evaluate the accuracy of the different surrogate models. The results show that the cross-section in the y-direction and the plate thickness have the most significant effects on the maximum deflection of SPSWs. The results also show that the maximum deflection is related to the scaled distance, i.e. for a value of 0.383. The ensemble model performs better than all other models for predicting the maximum deflection of SPSWs under explosive loads.
机译:使用非线性Fe分析和替代方法评估爆炸载量下钢板剪切壁(SPSW)的用途抗性。本研究采用钢板爆炸负荷和约翰逊烹饪模型的常规武器效应计划(Conwep)模型。基于Taguchi方法,选择了来自100个样品的25个样品用于参数研究,在那里我们预测受损区域和爆炸性负载下SPSW的最大偏转。然后,本研究使用多元线性回归(MLR),多个LN等式回归(MLNER),基因表达编程(GEP),基于自适应网络的模糊推理(ANFIS)和集合模型,以预测SPSW的最大检测。若干统计参数和错误术语用于评估不同代理模型的准确性。结果表明,Y方向和板厚的横截面对SPSW的最大偏转具有最显着的影响。结果还表明,最大偏转与缩放距离有关,即值为0.383。该集合模型比所有其他模型更好地预测爆炸负载下SPSW的最大偏转。使用非线性FE分析和替代方法评估爆炸性载荷下的SPSW。本研究采用了钢板爆炸载荷和约翰逊烹饪模型的ConWep模型。基于Taguchi方法,选择了来自100个样品的25个样品用于参数研究,在那里我们预测受损区域和爆炸性负载下SPSW的最大偏转。然后,本研究使用MLR,MLNER,GEP,ANFI和集合模型来预测SPSW的最大检测。若干统计参数和错误术语用于评估不同代理模型的准确性。结果表明,Y方向和板厚的横截面对SPSW的最大偏转具有最显着的影响。结果还表明,最大偏转与缩放距离有关,即值为0.383。该集合模型比所有其他模型更好地进行了预测爆炸性载荷下SPSW的最大偏转。使用非线性FE分析和替代方法评估SPSW下的SPSW的电阻。本研究采用了钢板爆炸载荷和约翰逊烹饪模型的ConWep模型。基于Taguchi方法,选择了来自100个样品的25个样品用于参数研究,在那里我们预测受损区域和爆炸性负载下SPSW的最大偏转。然后,本研究使用MLR,MLNER,GEP,ANFI和集合模型来预测SPSW的最大检测。若干统计参数和错误术语用于评估不同代理模型的准确性。结果表明,Y方向和板厚的横截面对SPSW的最大偏转具有最显着的影响。结果还表明,最大偏转与缩放距离有关,即值为0.383。该集合模型比所有其他模型更好地执行用于预测爆炸性载荷下SPSW的最大偏转。使用非线性FE分析和替代方法评估SPSW下的SPSW的耐菌株。本研究采用了爆炸载荷的ConWep模型和钢板的Johnson-Cook模型。基于Taguchi方法,选择了100个样品的25个样品,用于参数研究,我们预测受损区域和最大偏转爆炸性负荷下的spsws。然后,本研究使用MLR,MLNER,GEP,ANFI和集合模型来预测SPSW的最大检测。若干统计参数和错误术语用于评估不同代理模型的准确性。结果表明,Y方向和板厚的横截面对SPSW的最大偏转具有最显着的影响。结果还表明,最大偏转与缩放距离有关,即值为0.383。该集合模型比所有其他模型更好地执行,以预测爆炸性负载下SPSW的最大偏转。

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