A Multi-Gene Genetic Programming Model for Predicting Shear Strength of Steel Fiber Concrete Beams

The superior performance of steel fibers (SFs) in enhancing the shear capacity of reinforced concrete (RC) beams supports their use in regions that would otherwise require minimum shear reinforcement (stirrups). Subsequently, several researchers proposed different predictive models to capture the co...

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Vydané v:ACI structural journal Ročník 119; číslo 2; s. 317 - 328
Hlavní autori: Ismail, Mohamed K, Yosri, Ahmed, El-Dakhakhni, Wael
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Farmington Hills American Concrete Institute 01.03.2022
Predmet:
Artificial intelligence
Behavior
Complex systems
Concrete
Cracks
Datasets
Fibers
Genetic algorithms
Mechanics
Mechanics (physics)
Parameter identification
Prediction models
Regression analysis
Reinforced concrete
Reinforcing steels
Shear (Mechanics)
Shear strength
Steel fiber reinforced concretes
Steel fibers
Stirrups
Tensile strength
Canada
ISSN:0889-3241, 0889-3241, 1944-7361
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Shrnutí:The superior performance of steel fibers (SFs) in enhancing the shear capacity of reinforced concrete (RC) beams supports their use in regions that would otherwise require minimum shear reinforcement (stirrups). Subsequently, several researchers proposed different predictive models to capture the contribution of SFs to the shear capacity of RC beams without stirrups. However, most of these models were empirically developed using respective limited data sets, restricting their generalizability. Artifcial intelligencebased models have shown high efcacy in imitating the behavior of complex systems based on large data sets; however, such models are usually criticized for being impractical for design and too complex to use. To address this issue, a new methodology was adopted in this paper using multi-gene genetic programming (MGGP)--a class of artifcial intelligence techniques--to develop an elegant shear strength prediction model for steel fiberreinforced concrete (SFRC) beams. Guided by mechanics and previous research findings, the governing parameters and their key combinations were first identified and selected for the model development. Subsequently, the model was trained, validated, and tested using observations from 752 experimental tests, and different measures were applied to assess its performance and generalizability. The MGGP-based model developed in this study outperformed 15 shear strength prediction models developed in previous studies and is also presented in a standards-ready format to facilitate adoption. The outcomes from this study demonstrate the promising capability of a mechanics-guided artifcial intelligence approach to develop interpretable models that can efficiently predict complex structural behaviors of other components and systems. Keywords: data-driven empirical models; genetic programming; sensitivity analysis; shear strength prediction; steel fiber-reinforced concrete (SFRC) beams.
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ISSN:0889-3241
0889-3241
1944-7361
DOI:10.14359/51734345