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

• Published in: Sustainability 2020-04, Vol.12 (7), p.2709
• Main Authors: Ly, Hai-Bang, Le, Tien-Thinh, Vu, Huong-Lan Thi, Tran, Van Quan, Le, Lu Minh, Pham, Binh Thai
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Civil engineering; Computer applications; Concrete; Construction; Datasets; Design factors; Empirical equations; Experiments; Genetic algorithms; Geometry; Laboratories; Learning algorithms; Machine learning; Mathematical models; Mechanical properties; Neural networks; Optimization; Optimization techniques; Performance evaluation; Prestressed concrete; Reinforced concrete; Sensitivity analysis; Shear; Shear strength; Shear tests; Software; Steel; Steel fiber reinforced concretes; Structural design; Structural engineering; Studies; Support vector machines; Sustainability; Tensile strength; Variables
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su12072709

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.