Evaluation of Shear Capacity of Steel Fiber Reinforced Concrete Beams without Stirrups Using Artificial Intelligence Models
The shear transfer mechanism of steel fiber reinforced concrete (SFRC) beams without stirrups is still not well understood. This is demonstrated herein by examining the accuracy of typical empirical formulas for 488 SFRC beam test records compiled from the literature. To steer clear of these cogniti...
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MDPI AG
2022-03-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/15/7/2407 |
| _version_ | 1797438686912053248 |
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| author | Yong Yu Xin-Yu Zhao Jin-Jun Xu Shao-Chun Wang Tian-Yu Xie |
| author_facet | Yong Yu Xin-Yu Zhao Jin-Jun Xu Shao-Chun Wang Tian-Yu Xie |
| author_sort | Yong Yu |
| collection | DOAJ |
| description | The shear transfer mechanism of steel fiber reinforced concrete (SFRC) beams without stirrups is still not well understood. This is demonstrated herein by examining the accuracy of typical empirical formulas for 488 SFRC beam test records compiled from the literature. To steer clear of these cognitive limitations, this study turned to artificial intelligence (AI) models. A gray relational analysis (GRA) was first conducted to evaluate the importance of different parameters for the problem at hand. The outcomes indicate that the shear capacity depends heavily on the material properties of concrete, the amount of longitudinal reinforcement, the attributes of steel fibers, and the geometrical and loading characteristics of SFRC beams. After this, AI models, including back-propagation artificial neural network, random forest and multi-gene genetic programming, were developed to capture the shear strength of SFRC beams without stirrups. The findings unequivocally show that the AI models predict the shear strength more accurately than do the empirical formulas. A parametric analysis was performed using the established AI model to investigate the effects of the main influential factors (determined by GRA) on the shear capacity. Overall, this paper provides an accurate, instantaneous and meaningful approach for evaluating the shear capacity of SFRC beams containing no stirrups. |
| first_indexed | 2024-03-09T11:41:51Z |
| format | Article |
| id | doaj.art-cd33c3eb58b148cb8b826e931cd1cb48 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-09T11:41:51Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-cd33c3eb58b148cb8b826e931cd1cb482023-11-30T23:31:44ZengMDPI AGMaterials1996-19442022-03-01157240710.3390/ma15072407Evaluation of Shear Capacity of Steel Fiber Reinforced Concrete Beams without Stirrups Using Artificial Intelligence ModelsYong Yu0Xin-Yu Zhao1Jin-Jun Xu2Shao-Chun Wang3Tian-Yu Xie4School of Environment and Civil Engineering, Dongguan University of Technology, Dongguan 523808, ChinaState Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou 510640, ChinaCollege of Civil Engineering, Nanjing Tech University, Nanjing 211816, ChinaShanghai Construction No.1 (Group) Co., Ltd., Shanghai 200120, ChinaSchool of Engineering, RMIT University, Melbourne, VIC 3000, AustraliaThe shear transfer mechanism of steel fiber reinforced concrete (SFRC) beams without stirrups is still not well understood. This is demonstrated herein by examining the accuracy of typical empirical formulas for 488 SFRC beam test records compiled from the literature. To steer clear of these cognitive limitations, this study turned to artificial intelligence (AI) models. A gray relational analysis (GRA) was first conducted to evaluate the importance of different parameters for the problem at hand. The outcomes indicate that the shear capacity depends heavily on the material properties of concrete, the amount of longitudinal reinforcement, the attributes of steel fibers, and the geometrical and loading characteristics of SFRC beams. After this, AI models, including back-propagation artificial neural network, random forest and multi-gene genetic programming, were developed to capture the shear strength of SFRC beams without stirrups. The findings unequivocally show that the AI models predict the shear strength more accurately than do the empirical formulas. A parametric analysis was performed using the established AI model to investigate the effects of the main influential factors (determined by GRA) on the shear capacity. Overall, this paper provides an accurate, instantaneous and meaningful approach for evaluating the shear capacity of SFRC beams containing no stirrups.https://www.mdpi.com/1996-1944/15/7/2407steel fiber reinforced concrete beamshear capacityback-propagation artificial neural workrandom forestmulti-gene genetic programmingparameter sensitivity |
| spellingShingle | Yong Yu Xin-Yu Zhao Jin-Jun Xu Shao-Chun Wang Tian-Yu Xie Evaluation of Shear Capacity of Steel Fiber Reinforced Concrete Beams without Stirrups Using Artificial Intelligence Models Materials steel fiber reinforced concrete beam shear capacity back-propagation artificial neural work random forest multi-gene genetic programming parameter sensitivity |
| title | Evaluation of Shear Capacity of Steel Fiber Reinforced Concrete Beams without Stirrups Using Artificial Intelligence Models |
| title_full | Evaluation of Shear Capacity of Steel Fiber Reinforced Concrete Beams without Stirrups Using Artificial Intelligence Models |
| title_fullStr | Evaluation of Shear Capacity of Steel Fiber Reinforced Concrete Beams without Stirrups Using Artificial Intelligence Models |
| title_full_unstemmed | Evaluation of Shear Capacity of Steel Fiber Reinforced Concrete Beams without Stirrups Using Artificial Intelligence Models |
| title_short | Evaluation of Shear Capacity of Steel Fiber Reinforced Concrete Beams without Stirrups Using Artificial Intelligence Models |
| title_sort | evaluation of shear capacity of steel fiber reinforced concrete beams without stirrups using artificial intelligence models |
| topic | steel fiber reinforced concrete beam shear capacity back-propagation artificial neural work random forest multi-gene genetic programming parameter sensitivity |
| url | https://www.mdpi.com/1996-1944/15/7/2407 |
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