Machine learning-based evaluation of punching shear resistance for steel/FRP-RC slabs
With the emergence of fiber-reinforced polymer (FRP) reinforcement as a substitute for conventional steel reinforcement, different design codes have been developed to account for the different mechanical properties of the FRP, specifically the elastic modulus. The ACI 440.11-22, CSA/S806-12, and JSC...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-05-01
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| Series: | Ain Shams Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447924000431 |
| _version_ | 1827270245911363584 |
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| author | Yazan Momani Roaa Alawadi Yazeed S. Jweihan Ahmad N. Tarawneh Mazen J. Al-Kheetan Ahmad Aldiabat |
| author_facet | Yazan Momani Roaa Alawadi Yazeed S. Jweihan Ahmad N. Tarawneh Mazen J. Al-Kheetan Ahmad Aldiabat |
| author_sort | Yazan Momani |
| collection | DOAJ |
| description | With the emergence of fiber-reinforced polymer (FRP) reinforcement as a substitute for conventional steel reinforcement, different design codes have been developed to account for the different mechanical properties of the FRP, specifically the elastic modulus. The ACI 440.11-22, CSA/S806-12, and JSCE-97 are well-known standards for FRP-reinforced concrete structures. In particular, these design standards show significant variations in estimating the punching shear resistance and accounting for the elastic modulus. This study provides a statistical and machine learning-based evaluation of the punching shear models in design standards. An Artificial neural network (ANN) framework is used to develop a generalized punching resistance model of flat slabs reinforced with steel and FRP bars utilizing a large experimental dataset with 539 tests. The study presents a parametric study to examine the effect of different factors on the punching shear strength. The parametric study provided a graphical presentation and comparison between the design models and the developed ANN model. |
| first_indexed | 2024-04-24T18:48:37Z |
| format | Article |
| id | doaj.art-851a897be4dc4e4d96089c3dbc61a5a3 |
| institution | Directory Open Access Journal |
| issn | 2090-4479 |
| language | English |
| last_indexed | 2025-03-22T05:18:22Z |
| publishDate | 2024-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj.art-851a897be4dc4e4d96089c3dbc61a5a32024-04-27T04:42:26ZengElsevierAin Shams Engineering Journal2090-44792024-05-01155102668Machine learning-based evaluation of punching shear resistance for steel/FRP-RC slabsYazan Momani0Roaa Alawadi1Yazeed S. Jweihan2Ahmad N. Tarawneh3Mazen J. Al-Kheetan4Ahmad Aldiabat5Civil Engineering Department, Faculty of Engineering, University of Petra, Amman, Jordan; Corresponding author.Civil Engineering Department, Faculty of Engineering, Applied Science Private University, Amman, JordanCivil and Environmental Engineering Department, Faculty of Engineering, Mutah University, Mutah, Karak, 61710, P.O. BOX 7, JordanCivil Engineering Department, Faculty of Engineering, The Hashemite University, Zarqa, JordanCivil and Environmental Engineering Department, Faculty of Engineering, Mutah University, Mutah, Karak, 61710, P.O. BOX 7, JordanCivil Engineering Department, Faculty of Engineering, The Hashemite University, Zarqa, JordanWith the emergence of fiber-reinforced polymer (FRP) reinforcement as a substitute for conventional steel reinforcement, different design codes have been developed to account for the different mechanical properties of the FRP, specifically the elastic modulus. The ACI 440.11-22, CSA/S806-12, and JSCE-97 are well-known standards for FRP-reinforced concrete structures. In particular, these design standards show significant variations in estimating the punching shear resistance and accounting for the elastic modulus. This study provides a statistical and machine learning-based evaluation of the punching shear models in design standards. An Artificial neural network (ANN) framework is used to develop a generalized punching resistance model of flat slabs reinforced with steel and FRP bars utilizing a large experimental dataset with 539 tests. The study presents a parametric study to examine the effect of different factors on the punching shear strength. The parametric study provided a graphical presentation and comparison between the design models and the developed ANN model.http://www.sciencedirect.com/science/article/pii/S2090447924000431Punching shearANNDatabaseFRPDesign codes |
| spellingShingle | Yazan Momani Roaa Alawadi Yazeed S. Jweihan Ahmad N. Tarawneh Mazen J. Al-Kheetan Ahmad Aldiabat Machine learning-based evaluation of punching shear resistance for steel/FRP-RC slabs Ain Shams Engineering Journal Punching shear ANN Database FRP Design codes |
| title | Machine learning-based evaluation of punching shear resistance for steel/FRP-RC slabs |
| title_full | Machine learning-based evaluation of punching shear resistance for steel/FRP-RC slabs |
| title_fullStr | Machine learning-based evaluation of punching shear resistance for steel/FRP-RC slabs |
| title_full_unstemmed | Machine learning-based evaluation of punching shear resistance for steel/FRP-RC slabs |
| title_short | Machine learning-based evaluation of punching shear resistance for steel/FRP-RC slabs |
| title_sort | machine learning based evaluation of punching shear resistance for steel frp rc slabs |
| topic | Punching shear ANN Database FRP Design codes |
| url | http://www.sciencedirect.com/science/article/pii/S2090447924000431 |
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