Fire Resistance of Steel Beams with Intumescent Coating Exposed to Fire Using ANSYS and Machine Learning
The thermal conductivity of steel is high compared to other materials such as concrete or timber. Therefore, fire protection measures are applied to prolong the duration between the onset of fire exposure and the final loss of load-bearing function of a steel structure. The most common passive fire...
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MDPI AG
2025-07-01
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| author | Igor Džolev Sofija Kekez-Baran Andrija Rašeta |
| author_facet | Igor Džolev Sofija Kekez-Baran Andrija Rašeta |
| author_sort | Igor Džolev |
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| description | The thermal conductivity of steel is high compared to other materials such as concrete or timber. Therefore, fire protection measures are applied to prolong the duration between the onset of fire exposure and the final loss of load-bearing function of a steel structure. The most common passive fire protection measure is the application of intumescent coating (IC), a thin film that expands at elevated temperatures and forms an insulating char layer of lower thermal conductivity. This paper focuses on structural steel beams with IPE open-section profiles protected by a water-based IC and subjected to static and standard fire loading. ANSYS 16.0 is used to simulate heat transfer, with thermal conductivity function described by standard multivariate linear regression analysis, followed by mechanical analysis considering degradation of material mechanical properties at elevated temperatures. Simulations are conducted for all IPE profile sizes, with varying initial degrees of utilisation, beam lengths, and coating thicknesses. Results indicated fire resistance times ranging from 24 to 53.5 min, demonstrating a relatively good level of fire resistance even with the minimal IC thickness. Furthermore, artificial neural networks were developed to predict the fire resistance time of steel members with IC using varying numbers of hidden neurons and subset ratios. The model achieved a predictability level of 99.9% upon evaluation. |
| format | Article |
| id | doaj-art-ebf979e4e03241899a531f301b848e79 |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Buildings |
| spelling | doaj-art-ebf979e4e03241899a531f301b848e792025-08-20T03:50:21ZengMDPI AGBuildings2075-53092025-07-011513233410.3390/buildings15132334Fire Resistance of Steel Beams with Intumescent Coating Exposed to Fire Using ANSYS and Machine LearningIgor Džolev0Sofija Kekez-Baran1Andrija Rašeta2Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, SerbiaDepartment of Building, Energy and Material Technology, UiT The Arctic University of Norway, NO-8514 Narvik, NorwayFaculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, SerbiaThe thermal conductivity of steel is high compared to other materials such as concrete or timber. Therefore, fire protection measures are applied to prolong the duration between the onset of fire exposure and the final loss of load-bearing function of a steel structure. The most common passive fire protection measure is the application of intumescent coating (IC), a thin film that expands at elevated temperatures and forms an insulating char layer of lower thermal conductivity. This paper focuses on structural steel beams with IPE open-section profiles protected by a water-based IC and subjected to static and standard fire loading. ANSYS 16.0 is used to simulate heat transfer, with thermal conductivity function described by standard multivariate linear regression analysis, followed by mechanical analysis considering degradation of material mechanical properties at elevated temperatures. Simulations are conducted for all IPE profile sizes, with varying initial degrees of utilisation, beam lengths, and coating thicknesses. Results indicated fire resistance times ranging from 24 to 53.5 min, demonstrating a relatively good level of fire resistance even with the minimal IC thickness. Furthermore, artificial neural networks were developed to predict the fire resistance time of steel members with IC using varying numbers of hidden neurons and subset ratios. The model achieved a predictability level of 99.9% upon evaluation.https://www.mdpi.com/2075-5309/15/13/2334fire loadingsteel beamsfire resistanceparametric analysisartificial neural networks |
| spellingShingle | Igor Džolev Sofija Kekez-Baran Andrija Rašeta Fire Resistance of Steel Beams with Intumescent Coating Exposed to Fire Using ANSYS and Machine Learning Buildings fire loading steel beams fire resistance parametric analysis artificial neural networks |
| title | Fire Resistance of Steel Beams with Intumescent Coating Exposed to Fire Using ANSYS and Machine Learning |
| title_full | Fire Resistance of Steel Beams with Intumescent Coating Exposed to Fire Using ANSYS and Machine Learning |
| title_fullStr | Fire Resistance of Steel Beams with Intumescent Coating Exposed to Fire Using ANSYS and Machine Learning |
| title_full_unstemmed | Fire Resistance of Steel Beams with Intumescent Coating Exposed to Fire Using ANSYS and Machine Learning |
| title_short | Fire Resistance of Steel Beams with Intumescent Coating Exposed to Fire Using ANSYS and Machine Learning |
| title_sort | fire resistance of steel beams with intumescent coating exposed to fire using ansys and machine learning |
| topic | fire loading steel beams fire resistance parametric analysis artificial neural networks |
| url | https://www.mdpi.com/2075-5309/15/13/2334 |
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