V.T. Babar, P.K, Joshi, D.N. Shinde, Shear strength of steel fiber reinforced concrete beam without stirrups, International Journal of Advanced Engineering Technology, 5(2), (2015), 15-18.
 B.M. Adolfo, K.H. Wong, Design of simply supported deep beams using strut-and-tie models, ACI Structural Journal, 100(6), (2003), 704-712.
 I.M. Boyan, C.B. Evan, P.C. Michael, Two-parameter kinematic theory for shear behavior of beep beams, ACI Structural Journal, 110(3), (2013), 447-456.
 F. Danglade, J.P. Pernot, P. Ve´ron, L. Fine, A priori evaluation of simulation models preparation processes using artificial intelligence techniques, Computers in Industry, 91, (2017), 45-61.
 H.G. Ni, J.Z. Wang, Prediction of compressive strength of concrete by neural networks, Cement and Concrete Research, 30(8), (2000), 1245-1250.
 M.Y. Mansour, M. Dicleli, J.Y. Lee, J. Zhang, Predicting the shear strength of reinforced concrete beams using artificial neural networks, Engineering Structures, 26(6), (2004), 781-799.
 A. Toghroli, M. Mohammadhassani, M. Suhatril, M. Shariati, Z. Ibrahim, Prediction of shear capacity of channel shear connectors using the ANFIS model, Steel and Composite Structures, 17(5), (2014), 623-639.
 Mansouri, M. Shariati, M. Safa, Z. Ibrahim, M.M. Tahir, D. Petković, Analysis of influential factors for predicting the shear strength of a V-shaped angle shear connector in composite beams using an adaptive neuro-fuzzy technique, Journal of Intelligent Manufacturing, 30(3), (2019), 1247-1257.
 M. Safa, M. Shariati, Z. Ibrahim, A. Toghroli, S.B. Baharom, N.M. Nor, D. Petkovic, Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam’s shear strength, Steel and Composite Structures, 21(3), (2016), 679-688.
 Y. Sedghi, Y. Zandi, A. Toghroli, M. Safa, E.T. Mohamad, M. Khorami, K. Wakil, Application of ANFIS technique on performance of C and L shaped angle shear connectors, Smart Structures and Systems, 22(3), (2018), 335-340.
 D.V. Dao
, H.B. Ly
, S.H. Trinh
, T.T. Le
, B.T. Pham
, Artificial intelligence approaches for prediction of compressive strength of geopolymer concrete. Materials
, 12(6), (2019), 983-990.
 M.A Mashrei, M.M. Alaa, An Adaptive Neuro-Fuzzy Inference Model to Predict Punching Shear Strength of Flat Concrete Slabs, Journal of Applied Sciences, 9(4), (2019), 809-829.
 H. Naderpour, M. Mirrashid, Shear Strength Prediction of RC Beams Using Adaptive Neuro-Fuzzy Inference System, Sharif University of technology, (2018).
 K.P.N Suguna, J.K. Raghunath, R.U. Maheswari, ANN based modeling for high strength concrete beams with surface mounted FRP laminates. International Jornal of Optimization in Civil Engineering, (2019).
 Z. Keshavarz, H. Torkian, Application of ANN and ANFIS models in determining compressive strength of concrete, Journal of Soft Computing in Civil Engineering, 2(1), (2018), 62-70.
 V.R.A Saathappan, P.N. Raghunath, K. Suguna, Adaptive neuro-fuzzy model for performance evaluation of RC T-beams with externally bonded GFRP reinforcement, Journal of Reinforced Plastics and Composites, 30(24), (2011), 2015-2023.
 E.M. Golafshani, A. Rahai, M.H. Sebt, H. Akbarpour, Prediction of bond strength of spliced steel bars in concrete using artificial neural network and fuzzy logic, Construction and Building Materials, 36, (2012),411-418.
 M.M. Alshihri, A.M. Azmy, M.S. El-Bisy, Neural networks for predicting compressive strength of structural light weight concrete, Construction and Building Materials, 23(6), (2009), 2214-2219.
 F. Ozcan, C.D. Atis, O. Karahan, E. Uncuoglu, H. Tanyildizi, Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete, Advances in Engineering Software, 40(9), (2009), 856-863
 L. Bal, F. Buyle-Bodin, Artificial neural network for predicting drying shrinkage of concrete, Construction and Building Materials, 38, (2013), 246-254.
 J. Sobhani, M. Najimi, A.R. Pourkhorshidi, T. Parhizkar, Prediction of the compressive strength of no-slump concrete: a comparative study of regression, neural network and ANFIS models, Construction and Building Materials, 24(5), (2010), 709-718.
 N.H. Guang, W.J. Zong, Prediction of compressive strength of concrete by neural network, Cement and Concrete Research, 30(8), (2000), 1245-1250.
 S. Wild, J. Bai, J.A. Ware, B.B. Sabir, Using neural networks to predict workability of concrete incorporating metakaolin and fly ash, Advances in Engineering Software, 34(11), (2003), 663-669.
 M. Khatibinia, M.R. Mohammadizadeh, Intelligent fuzzy inference system approach for modeling of debonding strength in FRP retrofitted masonry elements, Structural Engineering and Mechanics, 56(5), (2017) ,787-796.
 J.S Chou, A.D. Pham, Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength, Construction and Building Materials, 49, (2013), 554-563.
 Z. MundherYaseen, M.T. Tran, S. Kim, T. Bakhshpoori, R.C. Deo, Shear strength prediction of steel reinforced concrete beam using hybrid intelligence models: A new approach, Engineering Structures, 177, (2013), 244-255.
 B. Keshtegar, M. Bagheri, Z. Mundher Yaseen, Shear strength of steel fiber-unconfined reinforced concrete beam simulation: Application of novel intelligent model, Composite Structures, 212, (2019), 230-242.
 F. Khademi, S.M. Jamal, N. Deshpande, S. Londhe, Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression, International Journal of Sustainable Built Environment, 5(2), (2016), 355-369.
 M. Pal, S. Deswal, Support vector regression based shear strength modelling of deep beams, Computers and Structures, 89(13), (2011), 1430-1439.
 J.S. Chou, N.T. Ngo, A.D. Pham, Shear strength prediction in reinforced concrete deep beams using nature-inspired metaheuristic support vector regression, Journal of Computing in Civil Engineering, 30(1), (2015), 107-115.
 N.D. Hoang, X.L. Tran, H. Nguyen, Predicting ultimate bond strength of corroded reinforcement and surrounding concrete using a metaheuristic optimized least squares support vector regression model, Neural Computing and Applications, (2019), 1-21.
 L. Li, W. Zheng, Y. Wang, Prediction of Moment Redistribution in Statically Indeterminate Reinforced Concrete Structures Using Artificial Neural Network and Support Vector Regression”, Applied Sciences, 9(1), (2019), 28-51.
 A. Gholampour, I. Mansouri, O. Kisi, T. Ozbakkaloglu, Evaluation of mechanical properties of concretes containing coarse recycled concrete aggregates using multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), and least squares support vector regression (LSSVR) models, Neural Computing and Applications, (2018).
 B.T. Pham, T.A. Hoang, D.M. Nguyen, D.T. Bui, Prediction of shear strength of soft soil using machine learning methods, Catena, 166, (2018), 181-191.
 S.F. Liu, Y.J. Yang, J. Forrest, Grey Data Analysis: Methods, Models and Applications, Springer Singapore, (2017).
 J.J. Xu, Z.P. Chen, T. Ozbakkaloglu, X.Y. Zhao, C. Demartino, A critical assessment of the compressive behavior of reinforced recycled aggregate concrete columns”, Engineering Structures, 161, (2018), 161-175.
 S. Liu, H. Zhang, Y. Yang, Explanation of terms of grey incidence analysis models”, Grey Systems: Theory and Application, 7(1), (2017), 136-142.
 H. Yu, S. Kim, SVM tutorial: classification, regression, and ranking”, Handbook of Natural Computing, Springer Berlin Heidelberg, (2012), 479-506.
 H. Drucker, C.J. Burges, L. Kaufman, A.J. Smola, V. Vapnik, Support vector regression machines”, In Advances in Neural Information Processing Systems, 28(7), (1997), 779-784.
 J. Guan, J. Zurada, A. Levitan, An Adaptive Neuro fuzzy inference system based approach to real estate property assessment”, Journal of Real Estate Research, 30(4), (2008), 395-422.
 J. Kennedy, R.C. Eberhart, Y. Shiny, Swarm intelligence, Morgan Kaufmann Publishers, (2011).
 A.N. Hanoon, M.S. Jaafar, F. Hejazi, F.N.A. Abdul Aziz, Energy absorption evaluation of reinforced concrete beams under various loading rates based on particle swarm optimization technique, Engineering Optimization, 49(9), (2016), 1483-1501.
 J.H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, MIT press, (1992).
 H. Garg, A hybrid GSA-GA algorithm for constrained optimization problems, Information Sciences, 478, (2018), 499-523.
 T. Chai, R.R. Draxler, Root mean square error (RMSE) or mean absolute error (MAE)–arguments against avoiding RMSE in the literature, Geoscientific model development,7(3), (2014), 1247-1250.
 American Concrete Institute (ACI), Committee 318-11: Building Code Requirements for Structural Concrete and Commentary, American Concrete Institute, (2011).
 Canadian Standards Association (CSA), Design of concrete structures: Structures (design), A national standard of Canada. CAN-A23.3-94, Clause11.1.2, Toronto, (1994).