“…25 kg emulsion explosive (17.5 kg TNT equivalence) placed 1.5 m above the center of the column to generate the blast loading. After filed the blast test, the damaged columns were transferred to the laboratory to conduct a quasi-static axial compression test to obtain the RALBC.Figure 3.The schematic view of the test setup (Modified from reference (Li et al, 2021)).…”
Section: Finite Element Model and Validationmentioning
confidence: 99%
“…Ritchie et al (2018) tested four CFST columns under farfield explosion with scaled distances of 2.70 m/kg 1/3 and 1.89 m/kg 1/3 . Li et al (2021Li et al ( , 2019Li et al ( , 2018 conducted a series of field blast tests and laboratory axial compression tests to study the dynamic response of CFDST columns under close-in and contact explosions and residual loadbearing capacity after the explosion. Through the above tests, the effect of explosive mass, scaled distance, concrete strength, axial load ratio, and steel ratio was investigated.…”
In the present study, the post-blast axial load-bearing capacity of the CFST column under the close-in explosion is investigated. Dynamic response and residual axial load capacity (RALBC) of 1599 circular CFST columns are numerically studied using a validated finite element model. The effects of column dimensions, material properties, blast loading, and axial load ratio are discussed. Based on the huge database, prediction models of RALBC are developed using three commonly used machine learning techniques including Extreme Gradient Boosting (known as XGBoost), Artificial Neutral Network (ANN), and Support Vector Regression (SVR). Comparison results show that the prediction model generated by the XGBoost performs the best among the three methods followed by the ANN and SVR methods. The RALBC of the CFST columns can be estimated rapidly and accurately with the prediction model developed in the present study. Finally, the feature importance of the input variables is analyzed using the additive feature attribution method SHAP (Shapley Additive ExPlanation). The results show that geometric parameters are the main factors impacting the RALBC of the CFST columns under close-in explosion.
“…25 kg emulsion explosive (17.5 kg TNT equivalence) placed 1.5 m above the center of the column to generate the blast loading. After filed the blast test, the damaged columns were transferred to the laboratory to conduct a quasi-static axial compression test to obtain the RALBC.Figure 3.The schematic view of the test setup (Modified from reference (Li et al, 2021)).…”
Section: Finite Element Model and Validationmentioning
confidence: 99%
“…Ritchie et al (2018) tested four CFST columns under farfield explosion with scaled distances of 2.70 m/kg 1/3 and 1.89 m/kg 1/3 . Li et al (2021Li et al ( , 2019Li et al ( , 2018 conducted a series of field blast tests and laboratory axial compression tests to study the dynamic response of CFDST columns under close-in and contact explosions and residual loadbearing capacity after the explosion. Through the above tests, the effect of explosive mass, scaled distance, concrete strength, axial load ratio, and steel ratio was investigated.…”
In the present study, the post-blast axial load-bearing capacity of the CFST column under the close-in explosion is investigated. Dynamic response and residual axial load capacity (RALBC) of 1599 circular CFST columns are numerically studied using a validated finite element model. The effects of column dimensions, material properties, blast loading, and axial load ratio are discussed. Based on the huge database, prediction models of RALBC are developed using three commonly used machine learning techniques including Extreme Gradient Boosting (known as XGBoost), Artificial Neutral Network (ANN), and Support Vector Regression (SVR). Comparison results show that the prediction model generated by the XGBoost performs the best among the three methods followed by the ANN and SVR methods. The RALBC of the CFST columns can be estimated rapidly and accurately with the prediction model developed in the present study. Finally, the feature importance of the input variables is analyzed using the additive feature attribution method SHAP (Shapley Additive ExPlanation). The results show that geometric parameters are the main factors impacting the RALBC of the CFST columns under close-in explosion.
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