Performance-Based Prediction of Shear and Flexural Strengths in Fiber-Reinforced Concrete Beams via Machine Learning

References

• Farooq U, Bedi KS. Study of shear behavior of RC beams: Non-linear analysis. Conference: Structural Engineering Convention. 2014: 3477–3488.
   Google Scholar
• Adebar P, Mindess S, St.-Pierre D, Olund B. Shear test of fiber concrete beams without stirrups. Struct J. 1997; 94: 68–76.
   Google Scholar
• Olalusi OB, Awoyera PO. Shear capacity prediction of slender reinforced concrete structures with steel fibers using machine learning. Eng Struct. 2021; 227: 111470.
   Web of Science ®Google Scholar
• Lin AL. Prediction of shear strength of fiber reinforced concrete beam of intermediate shear span [Master's Thesis]. Taipei, Taiwan: College of Engineering—Department of Civil Engineering, National Taiwan University; 2013. (In Chinese)
   Google Scholar
• Alotaibi E, Mostafa O, Nassif N, Omar M, Arab MG. Prediction of punching shear capacity for fiber-reinforced concrete slabs using neuro-nomographs constructed by machine learning. J Struct Eng. 2021; 147(6): 04021075.
   Web of Science ®Google Scholar
• Yaseen ZM, Tran MT, Kim S, Bakhshpoori T, Deo RC. Shear strength prediction of steel fiber reinforced concrete beam using hybrid intelligence models: a new approach. Eng Struct. 2018; 177: 244–255.
   Web of Science ®Google Scholar
• Ly HB, Le TT, Vu HLT, Tran VQ, Le LM, Pham BT. Computational hybrid machine learning based prediction of shear capacity for steel fiber reinforced concrete beams. Sustainability. 2020; 12(7): 2709.
   Web of Science ®Google Scholar
• Rahman J, Ahmed KS, Khan NI, Islam K, Mangalathu S. Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach. Eng Struct. 2021; 233: 111743.
   Web of Science ®Google Scholar
• Solhmirzaei R, Salehi H, Kodur V, Naser MZ. Machine learning framework for predicting failure mode and shear capacity of ultra high performance concrete beams. Eng Struct. 2020; 224: 111221.
   Web of Science ®Google Scholar
• Nassif N, Al-Sadoon ZA, Hamad K, Altoubat S. Cost-based optimization of shear capacity in fiber reinforced concrete beams using machine learning. Struct Eng Mech. 2022; 83(5): 671–680.
   Web of Science ®Google Scholar
• Minelli F, Plizzari GA (2013) On the effectiveness of steel fibers as shear reinforcement. ACI Struct J 110(3):379–389 29.
   Web of Science ®Google Scholar
• Bertozzi A, Reggia A. Fibre di acciaio per l’armatura minima a taglio delle travi (in Italian) [MS thesis]. Brescia: University of Brescia; 2006.
   Google Scholar
• Minelli F, Plizzari GA, Vecchio FJ. Influence of steel fibers on full-scale RC beams under shear loading. Proceedings of the International Conference FraMCoS–High Performance Concrete, Brick-Masonry and Environmental Aspects. Catania, Italy; 2007.
   Google Scholar
• Conforti A. Il traliccio ad inclinazione variabile per il progetto a taglio di travi fibrorinforzate: studio sperimentale e analitico (in Italian) [MS thesis]. Brescia: University of Brescia; 2008.
   Google Scholar
• Minelli F, Conforti A, Cuenca E, Plizzari G. Are steel fibres able to mitigate or eliminate size effect in shear?. Mater Struct. 2014; 47(3): 459–473.
   Web of Science ®Google Scholar
• Conforti A, Minelli F, Tinini A, Plizzari GA, Moro S. Structural applicability of polypropylene fibres: deep and wide-shallow beams subjected to shear. ACI Spec Publ. 2014; 310: 171–180.
   Google Scholar
• Conforti A, Minelli F, Plizzari GA. Influence of width-to-effective depth ratio on shear strength of reinforced concrete elements without Web reinforcement. ACI Struct J. 2017; 114(4): 995–1006. doi:10.14359/51689681.
   Web of Science ®Google Scholar
• Cuenca E. On shear behavior of structural elements made of steel fiber reinforced concrete [Ph.D. dissertation]. Switzerland: Springer Thesis. Springer International Publishing; 2015.
   Google Scholar
• EN 14651. Test method for metallic fibre concrete. Measuring the flexural tensile strength (limit of proportionality (LOP), residual). London: British Standards Institution; 2005.
   Google Scholar
• Hamad K, Khalil MA, Shanableh A. Modeling roadway traffic noise in a hot climate using artificial neural networks. Transp Res D Transp Environ. 2017; 53: 161–177.
   Web of Science ®Google Scholar
• The MathWorks Inc. Statistics and machine learning toolbox (R2022a). Natick, Massachusetts, United State; 2022.
   Google Scholar
• Européen C. Eurocode 2: Design of concrete structures—part 1-1: General rules and rules for buildings. London: British Standard Institution; 2004.
   Google Scholar
• Federation Internationale du Beton (fib). Model Code 2010-final draft, Vol. 1, Bulletin 65 and Vol. 2, Bulletin 66. Lausanne; 2012.
   Google Scholar