Explainable Machine-Learning Model for Rapid Damage Assessment of CFST Columns after Close-In Explosion
Publication: Journal of Performance of Constructed Facilities
Volume 38, Issue 3
Abstract
In the present study, the dynamic response and damage of concrete-filled steel tubular (CFST) columns under close-in explosion were numerically studied. An extensive parametric study was carried out to investigate the effects of column height, diameter, wall thickness, yield strength of steel, compressive strength of concrete, and axial load ratio on the residual midheight displacement (RMHD) and residual axial load-bearing capacity (RALBC). It was found that the RALBC is strongly correlated with the RMHD under different explosion scenarios. Three models were developed using Extreme Gradient Boosting (XGBoost) based on a database comprising 1,708 circular CFST column samples. These models aimed to predict the relationship between RMHD and RALBC, utilizing different combinations of input variables. Accurate prediction results can be obtained from all the models, and the selection of a model can be based on the availability of known input variables. The third prediction model, which does not require knowledge of the blast loading parameters and axial load ratio, which are usually difficult to obtain, can yield accurate results. Therefore, it can be used to quickly evaluate the RALBC of CFST columns. Finally, the prediction model was further interpreted locally and globally using the additive feature attribution method Shapley Additive Explanation (SHAP). Through the SHAP interpretation, the contribution of each input variable to the RALBC of CFST columns was analyzed. This provided valuable insights into the impact of individual variables on the prediction results.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors would acknowledge the support from the National Natural Science Foundation of China (Grant Nos. 52208498 and 52178295), the China Postdoctoral Science Foundation (Grant Nos. 2021M702446 and 2023M732608), and Tianjin Nature Science Foundation (Grant No. 23JCQNJC00900).
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Information
Published In
Journal of Performance of Constructed Facilities
Volume 38 • Issue 3 • June 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jun 7, 2023
Accepted: Jan 4, 2024
Published online: Mar 22, 2024
Published in print: Jun 1, 2024
Discussion open until: Aug 22, 2024
ASCE Technical Topics:
- Axial loads
- Columns
- Composite materials
- Compressive strength
- Disaster risk management
- Disasters and hazards
- Engineering fundamentals
- Engineering materials (by type)
- Engineering mechanics
- Explosions
- Man-made disasters
- Material mechanics
- Material properties
- Materials engineering
- Model accuracy
- Models (by type)
- Static loads
- Statics (mechanics)
- Steel columns
- Strength of materials
- Structural engineering
- Structural members
- Structural systems
- Tubes (structure)
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