Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

Hai-Bang Ly, Tien-Thinh Le, Huong-Lan Thi Vu, Tran Van Quan, Lu Minh Le and Binh Thai Pham
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Hai-Bang Ly: University of Transport Technology, Hanoi 100000, Vietnam
Tien-Thinh Le: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Huong-Lan Thi Vu: University of Transport Technology, Hanoi 100000, Vietnam
Tran Van Quan: University of Transport Technology, Hanoi 100000, Vietnam
Lu Minh Le: Faculty of Engineering, Vietnam National University of Agriculture, Gia Lam, Hanoi 100000, Vietnam
Binh Thai Pham: University of Transport Technology, Hanoi 100000, Vietnam

Sustainability, 2020, vol. 12, issue 7, 1-34

Abstract: Understanding shear behavior is crucial for the design of reinforced concrete beams and sustainability in construction and civil engineering. Although numerous studies have been proposed, predicting such behavior still needs further improvement. This study proposes a soft-computing tool to predict the ultimate shear capacities (USCs) of concrete beams reinforced with steel fiber, one of the most important factors in structural design. Two hybrid machine learning (ML) algorithms were created that combine neural networks (NNs) with two distinct optimization techniques (i.e., the Real-Coded Genetic Algorithm (RCGA) and the Firefly Algorithm (FFA)): the NN-RCGA and the NN-FFA. A database of 463 experimental data was gathered from reliable literature for the development of the models. After the construction, validation, and selection of the best model based on common statistical criteria, a comparison with the empirical equations available in the literature was carried out. Further, a sensitivity analysis was conducted to evaluate the importance of 16 inputs and reveal the dependency of structural parameters on the USC. The results showed that the NN-RCGA (R = 0.9771) was better than the NN-FFA and other analytical models (R = 0.5274–0.9075). The sensitivity analysis results showed that web width, effective depth, and a clear depth ratio were the most important parameters in modeling the shear capacity of steel fiber-reinforced concrete beams.

Keywords: civil engineering; structural engineering; shear capacity; fiber reinforced concrete beams; machine learning; optimization techniques (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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