A.M.J.J.O.C.P. Rashad, An exploratory study on high-volume fly ash concrete incorporating silica fume subjected to thermal loads, 87 (2015) 735-744.
 R. Feiz, J. Ammenberg, L. Baas, M. Eklund, A. Helgstrand, R.J.J.O.C.P. Marshall, Improving the CO2 performance of cement, part I: utilizing life-cycle assessment and key performance indicators to assess development within the cement industry, 98 (2015) 272-281.
 S. Ghavami, B. Farahani, H. Jahanbakhsh, F.J.A.J.O.C.E. Moghadas Nejad, Effects of Silica Fume and Nano-silica on the Engineering Properties of Kaolinite Clay, 2(2) (2018) 135-142.
 F.M. Nejad, M. Habibi, P. Hosseini, H.J.J.o.c.p. Jahanbakhsh, Investigating the mechanical and fatigue properties of sustainable cement emulsified asphalt mortar, 156 (2017) 717-728.
 B. Lothenbach, K. Scrivener, R.J.C. Hooton, c. research, Supplementary cementitious materials, 41(12) (2011) 1244-1256.
 M.H. Shehata, M.D.J.C. Thomas, C. Research, The effect of fly ash composition on the expansion of concrete due to alkali-silica reaction, 30(7) (2000) 1063-1072.
 R.J.C. Siddique, C. Research, Performance characteristics of high-volume Class F fly ash concrete, 34(3) (2004) 487-493.
 A. Behnood, V. Behnood, M.M. Gharehveran, K.E.J.C. Alyamac, B. Materials, Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm, 142 (2017) 199-207.
 F. Deng, Y. He, S. Zhou, Y. Yu, H. Cheng, X.J.C. Wu, B. Materials, Compressive strength prediction of recycled concrete based on deep learning, 175 (2018) 562-569.
 A. Behnood, E.M.J.J.o.c.p. Golafshani, Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves, 202 (2018) 54-64.
 B. Vakhshouri, S.J.N. Nejadi, Prediction of compressive strength of self-compacting concrete by ANFIS models, 280 (2018) 13-22.
 Z.M. Yaseen, R.C. Deo, A. Hilal, A.M. Abd, L.C. Bueno, S. Salcedo-Sanz, M.L.J.A.i.E.S. Nehdi, Predicting compressive strength of lightweight foamed concrete using extreme learning machine model, 115 (2018) 112-125.
 H.J.I.J.O.I. Naseri, Management, Technology, Cost Optimization of No-Slump Concrete Using Genetic Algorithm and Particle Swarm Optimization, 10(1) (2019).
 J.-S. Chou, C.-F.J.A.i.C. Tsai, Concrete compressive strength analysis using a combined classification and regression technique, 24 (2012) 52-60.
 D.-K. Bui, T. Nguyen, J.-S. Chou, H. Nguyen-Xuan, T.D.J.C. Ngo, B. Materials, A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete, 180 (2018) 320-333.
 H. Naderpour, A.H. Rafiean, P.J.J.O.B.E. Fakharian, Compressive strength prediction of environmentally friendly concrete using artificial neural networks, 16 (2018) 213-219.
 T. Kim, J.M. Davis, M.T. Ley, S. Kang, P.J.C. Amrollahi, B. Materials, Fly ash particle characterization for predicting concrete compressive strength, 165 (2018) 560-571.
 N. Rajamane, J.A. Peter, P.J.C. Ambily, c. composites, Prediction of compressive strength of concrete with fly ash as sand replacement material, 29(3) (2007) 218-223.
 I.B. Topcu, M.J.C.M.S. Sarıdemir, Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic, 41(3) (2008) 305-311.
 M.M. Khotbehsara, B.M. Miyandehi, F. Naseri, T. Ozbakkaloglu, F. Jafari, E.J.C. Mohseni, B. Materials, Effect of SnO2, ZrO2, and CaCO3 nanoparticles on water transport and durability properties of self-compacting mortar containing fly ash: Experimental observations and ANFIS predictions, 158 (2018) 823-834.
 I.-C.J.C. Yeh, C. research, Modeling of strength of high-performance concrete using artificial neural networks, 28(12) (1998) 1797-1808.
 I.-C.J.J.C.I.C.H.E. Yeh, Prediction of strength of fly ash and slag concrete by the use of artificial neural networks, 15(4) (2003) 659-663.
 J. Sobhani, M. Najimi, A.R. Pourkhorshidi, T.J.C. Parhizkar, B. Materials, Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models, 24(5) (2010) 709-718.
 J.R. Sampson, Adaptation in natural and artificial systems (John H. Holland), in, Society for Industrial and Applied Mathematics, 1976.
 H. Naseri, M.A.E.J.I.J.o.I. Ghasbeh, Management, Technology, Time-Cost Trade off to Compensate Delay of Project Using Genetic Algorithm and Linear Programming, 9(6) (2018).
 D. Neeraja, T. Kamireddy, P. Santosh Kumar, V. Simha Reddy, Weight optimization of plane truss using genetic algorithm, in: Materials Science and Engineering Conference Series, 2017, pp. 032015.
 Z. Aydın, Y.J.K.J.O.C.E. Ayvaz, Overall cost optimization of prestressed concrete bridge using genetic algorithm, 17(4) (2013) 769-776.
 H. Eskandar, A. Sadollah, A. Bahreininejad, M.J.C. Hamdi, Structures, Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems, 110 (2012) 151-166.
 A. Sadollah, H. Eskandar, A. Bahreininejad, J.H.J.S.C. Kim, Water cycle algorithm for solving multi-objective optimization problems, 19(9) (2015) 2587-2603.
 A. Sadollah, H. Eskandar, J.H.J.A.S.C. Kim, Water cycle algorithm for solving constrained multi-objective optimization problems, 27 (2015) 279-298.
 E.M. Golafshani, A.J.C. Behnood, C. Composites, Estimating the optimal mix design of silica fume concrete using biogeography-based programming, 96 (2019) 95-105.
 E.A. Ramalho, J.J. Ramalho, P.D.J.J.o.P.A. Henriques, Fractional regression models for second stage DEA efficiency analyses, 34(3) (2010) 239-255.
 E.A. Ramalho, J.J. Ramalho, J.M.J.J.o.E.S. Murteira, Alternative estimating and testing empirical strategies for fractional regression models, 25(1) (2011) 19-68.
 M. Mokhtari, s. Abedian, S.A. Almasi, Rockfall Susceptibility Mapping Using Artificial Neural Network, Frequency Ratio, and Logistic Regression: A Case Study in Central Iran, Taft County %J AUT Journal of Civil Engineering, (2019) -.
 A. Committee, I.O.f. Standardization, Building code requirements for structural concrete (ACI 318-08) and commentary, in, American Concrete Institute, 2008.
 M. Mirzahosseini, P. Jiao, K. Barri, K.A. Riding, A.H.J.E.C. Alavi, New machine learning prediction models for compressive strength of concrete modified with glass cullet, 36(3) (2019) 876-898.