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Cited by in F6Publishing
For: Chaabene WB, Nehdi ML. Novel soft computing hybrid model for predicting shear strength and failure mode of SFRC beams with superior accuracy. Composites Part C: Open Access 2020;3:100070. [DOI: 10.1016/j.jcomc.2020.100070] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Tariq M, Khan A, Ullah A, Shayanfar J, Niaz M. Improved Shear Strength Prediction Model of Steel Fiber Reinforced Concrete Beams by Adopting Gene Expression Programming. Materials (Basel) 2022;15:3758. [PMID: 35683054 DOI: 10.3390/ma15113758] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
2 Kaloop MR, Roy B, Chaurasia K, Kim S, Jang H, Hu J, Abdelwahed BS. Shear Strength Estimation of Reinforced Concrete Deep Beams Using a Novel Hybrid Metaheuristic Optimized SVR Models. Sustainability 2022;14:5238. [DOI: 10.3390/su14095238] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Dabbaghi F, Rashidi M, Nehdi ML, Sadeghi H, Karimaei M, Rasekh H, Qaderi F. Experimental and Informational Modeling Study on Flexural Strength of Eco-Friendly Concrete Incorporating Coal Waste. Sustainability 2021;13:7506. [DOI: 10.3390/su13137506] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 7.0] [Reference Citation Analysis]
4 Ben Chaabene W, Nehdi ML. Genetic programming based symbolic regression for shear capacity prediction of SFRC beams. Construction and Building Materials 2021;280:122523. [DOI: 10.1016/j.conbuildmat.2021.122523] [Cited by in Crossref: 14] [Cited by in F6Publishing: 5] [Article Influence: 14.0] [Reference Citation Analysis]