Advanced predictive modeling of shear strength in stainless-steel column web panels using explainable AI insights
In steel moment-resisting frames, energy dissipation occurs through yielding at the beam ends. Furthermore, the column panel zone can be designed to contribute to this energy dissipation process. The European standard (EN 1993–1–4) for stainless-steel is developed based on carbon steel procedures, w...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024017067 |
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| author | Sina Sarfarazi Rabee Shamass Federico Guarracino Ida Mascolo Mariano Modano |
| author_facet | Sina Sarfarazi Rabee Shamass Federico Guarracino Ida Mascolo Mariano Modano |
| author_sort | Sina Sarfarazi |
| collection | DOAJ |
| description | In steel moment-resisting frames, energy dissipation occurs through yielding at the beam ends. Furthermore, the column panel zone can be designed to contribute to this energy dissipation process. The European standard (EN 1993–1–4) for stainless-steel is developed based on carbon steel procedures, without taking into account stainless steel's unique strain hardening and mechanical properties. This discrepancy may result in inaccuracies in predicting panel zone behavior. However, with the recent advancements in stainless steel, it is timely to reassess these limitations. The present research investigates the behavior of stainless-steel column web panels through an explainable artifactual intelligence methodology. This approach combines twelve widely recognized machine learning algorithms with the SHAP algorithm for enhanced explainability and transparency. In addition, a user-friendly graphical user interface has been developed to simplify engineering design. The Extra Trees Regression algorithm demonstrated the highest predictive performance, achieving R² = 0.987, mean absolute error (MAE) = 3.575 kN, and root mean square error (RMSE) = 6.464 kN for the entire dataset. The SHAP analysis revealed that bolt diameter and the column second moment of inertia are the most critical input features affecting shear strength. This approach effectively captures the nonlinear characteristics of shear behavior in stainless-steel column web panels and offers clear insights into the contribution of different factors. The developed method not only improves predictive accuracy but also promotes transparency, making it a practical tool for engineers in structural component design. |
| format | Article |
| id | doaj-art-f319e22edac744e397a2a43bed4210df |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-f319e22edac744e397a2a43bed4210df2024-12-19T10:59:35ZengElsevierResults in Engineering2590-12302024-12-0124103454Advanced predictive modeling of shear strength in stainless-steel column web panels using explainable AI insightsSina Sarfarazi0Rabee Shamass1Federico Guarracino2Ida Mascolo3Mariano Modano4Department of Structures for Engineering and Architecture, University of Naples ‘‘Federico II’’, Naples, 80125, ItalyDepartment of Civil and Environmental Engineering, Brunel University London, London, UK; Corresponding author.Department of Structures for Engineering and Architecture, University of Naples ‘‘Federico II’’, Naples, 80125, ItalyDepartment of Structures for Engineering and Architecture, University of Naples ‘‘Federico II’’, Naples, 80125, ItalyDepartment of Structures for Engineering and Architecture, University of Naples ‘‘Federico II’’, Naples, 80125, ItalyIn steel moment-resisting frames, energy dissipation occurs through yielding at the beam ends. Furthermore, the column panel zone can be designed to contribute to this energy dissipation process. The European standard (EN 1993–1–4) for stainless-steel is developed based on carbon steel procedures, without taking into account stainless steel's unique strain hardening and mechanical properties. This discrepancy may result in inaccuracies in predicting panel zone behavior. However, with the recent advancements in stainless steel, it is timely to reassess these limitations. The present research investigates the behavior of stainless-steel column web panels through an explainable artifactual intelligence methodology. This approach combines twelve widely recognized machine learning algorithms with the SHAP algorithm for enhanced explainability and transparency. In addition, a user-friendly graphical user interface has been developed to simplify engineering design. The Extra Trees Regression algorithm demonstrated the highest predictive performance, achieving R² = 0.987, mean absolute error (MAE) = 3.575 kN, and root mean square error (RMSE) = 6.464 kN for the entire dataset. The SHAP analysis revealed that bolt diameter and the column second moment of inertia are the most critical input features affecting shear strength. This approach effectively captures the nonlinear characteristics of shear behavior in stainless-steel column web panels and offers clear insights into the contribution of different factors. The developed method not only improves predictive accuracy but also promotes transparency, making it a practical tool for engineers in structural component design.http://www.sciencedirect.com/science/article/pii/S2590123024017067Moment-resisting steel framesShear strength of panel zonesStainless-steel structuresData-driven modelsInterpretable machine learning |
| spellingShingle | Sina Sarfarazi Rabee Shamass Federico Guarracino Ida Mascolo Mariano Modano Advanced predictive modeling of shear strength in stainless-steel column web panels using explainable AI insights Results in Engineering Moment-resisting steel frames Shear strength of panel zones Stainless-steel structures Data-driven models Interpretable machine learning |
| title | Advanced predictive modeling of shear strength in stainless-steel column web panels using explainable AI insights |
| title_full | Advanced predictive modeling of shear strength in stainless-steel column web panels using explainable AI insights |
| title_fullStr | Advanced predictive modeling of shear strength in stainless-steel column web panels using explainable AI insights |
| title_full_unstemmed | Advanced predictive modeling of shear strength in stainless-steel column web panels using explainable AI insights |
| title_short | Advanced predictive modeling of shear strength in stainless-steel column web panels using explainable AI insights |
| title_sort | advanced predictive modeling of shear strength in stainless steel column web panels using explainable ai insights |
| topic | Moment-resisting steel frames Shear strength of panel zones Stainless-steel structures Data-driven models Interpretable machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024017067 |
| work_keys_str_mv | AT sinasarfarazi advancedpredictivemodelingofshearstrengthinstainlesssteelcolumnwebpanelsusingexplainableaiinsights AT rabeeshamass advancedpredictivemodelingofshearstrengthinstainlesssteelcolumnwebpanelsusingexplainableaiinsights AT federicoguarracino advancedpredictivemodelingofshearstrengthinstainlesssteelcolumnwebpanelsusingexplainableaiinsights AT idamascolo advancedpredictivemodelingofshearstrengthinstainlesssteelcolumnwebpanelsusingexplainableaiinsights AT marianomodano advancedpredictivemodelingofshearstrengthinstainlesssteelcolumnwebpanelsusingexplainableaiinsights |