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Computational predictions for predicting the performance of steel 1 panel shear wall under explosive loads
Engineering Computations ( IF 1.6 ) Pub Date : 2021-06-15 , DOI: 10.1108/ec-09-2020-0492
Aydin Shishegaran , Behnam Karami , Elham Safari Danalou , Hesam Varaee , Timon Rabczuk

Purpose

The 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/approach

The 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.

Findings

The 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/value

The 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.



中文翻译:

用于预测爆炸荷载作用下钢 1 面板剪力墙性能的计算预测

目的

钢板剪力墙 (SPSW) 在爆炸载荷下的抗力使用非线性有限元分析和替代方法进行评估。本研究对爆炸载荷采用常规武器效应程序 (CONWEP) 模型,对钢板采用 Johnson-Cook 模型。基于田口方法,从 100 个样本中选择 25 个样本进行参数研究,我们预测爆炸载荷下 SPSW 的损坏区域和最大挠度。然后,本研究使用多元线性回归 (MLR)、多元 Ln 方程回归 (MLnER)、基因表达编程 (GEP)、自适应网络模糊推理 (ANFIS) 和集成模型来预测 SPSW 的最大检测。几个统计参数和误差项用于评估不同代理模型的准确性。结果表明,y 方向的横截面和板厚对 SPSW 的最大挠度影响最显着。结果还表明,最大偏转与缩放距离有关,即值 0.383。在预测爆炸载荷下 SPSW 的最大挠度方面,集成模型的性能优于所有其他模型。

设计/方法/方法

爆炸载荷下的 SPSW 使用非线性有限元分析和代理方法进行评估。本研究对爆炸载荷使用 CONWEP 模型,对钢板使用 Johnson-Cook 模型。基于田口方法,从 100 个样本中选择 25 个样本进行参数研究,我们预测爆炸载荷下 SPSW 的损坏区域和最大挠度。然后,本研究使用 MLR、MLnER、GEP、ANFIS 和集成模型来预测 SPSW 的最大检测量。几个统计参数和误差项用于评估不同代理模型的准确性。结果表明,y 方向的横截面和板厚对 SPSW 的最大挠度影响最显着。结果还表明,最大挠度与缩放距离有关,即 值为 0.383。在预测爆炸载荷下 SPSW 的最大挠度方面,集成模型的性能优于所有其他模型。

发现

使用非线性有限元分析和代理方法评估爆炸载荷下 SPSW 的抵抗力。本研究对爆炸载荷使用 CONWEP 模型,对钢板使用 Johnson-Cook 模型。基于田口方法,从 100 个样本中选择 25 个样本进行参数研究,我们预测爆炸载荷下 SPSW 的损坏区域和最大挠度。然后,本研究使用 MLR、MLnER、GEP、ANFIS 和集成模型来预测 SPSW 的最大检测量。几个统计参数和误差项用于评估不同代理模型的准确性。结果表明,y 方向的横截面和板厚对 SPSW 的最大挠度影响最显着。结果还表明,最大偏转与缩放距离有关,即值 0.383。在预测爆炸载荷下 SPSW 的最大挠度方面,集成模型的性能优于所有其他模型。

原创性/价值

使用非线性有限元分析和代理方法评估爆炸载荷下 SPSW 的抵抗力。本研究对爆炸载荷使用 CONWEP 模型,对钢板使用 Johnson-Cook 模型。基于田口方法,从 100 个样本中选择 25 个样本进行参数研究,我们预测爆炸载荷下 SPSW 的损坏区域和最大挠度。然后,本研究使用 MLR、MLnER、GEP、ANFIS 和集成模型来预测 SPSW 的最大检测量。几个统计参数和误差项用于评估不同代理模型的准确性。结果表明,y 方向的横截面和板厚对 SPSW 的最大挠度影响最显着。结果还表明,最大偏转与缩放距离有关,即值 0.383。在预测爆炸载荷下 SPSW 的最大挠度方面,集成模型的性能优于所有其他模型。

更新日期:2021-06-15
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