Mesbah HA, Lachemi M, Aitcin P-C. Determination of elastic properties of high-performance concrete at early ages. Mater J 2002;99:37–41.
 363 ACIC. Report on High-Strength Concrete (ACI 363R-10). ACI; 2010.
 Felix EF, Possan E, Carrazedo R. A New Formulation to Estimate the Elastic Modulus of Recycled Concrete Based on Regression and ANN. Sustainability 2021;13:8561.
 Nguyen T-T, Thai H-T, Ngo T. Optimised mix design and elastic modulus prediction of ultra-high strength concrete. Constr Build Mater 2021;302:124150.
 Mindess S, Young JF, Darwin D. Concrete, 2nd EditionPrentice Hall. Englewood Cliffs, NJ 2002.
 ASTM-C469. Standard Test Method for Static Modulus of Elasticity and Poisson’s Ratio of Concrete in Compression. ASTM Stand 2010.
 318 ACIC. Building Code Requirements for Structural Concrete (ACI 318-14) and Commentary, American Concrete Institute; 2014.
 Standard B. Structural Use of Concrete: Code of Practice for Design and Construction, Part 1, BS 8110 1997.
 Association CS. Design of concrete structures. Mississauga, Ont.: Canadian Standards Association; 2004.
 Institution BS. Eurocode 2: Design of concrete structures: Part 1-1: General rules and rules for buildings. British Standards Institution; 2004.
 Institute TS. Requirements for design and construction of reinforced concrete structures. TS500-2000 2000.
 Gardner NJ, Zhao JW. Mechanical properties of concrete for calculation of long term deformations. Proc. Second Can. Symp. Cem. Concr., University of British Columbia Press, Vancouver, British Columbia, Canada; 1991, p. 150–9.
 CEB-FIP C. model code 1990. Com Euro-International Du Beton, Paris 1991:87–109.
 Standard N. Norwegian Council for building standardization. NS3473, Norw 1998.
 Graybeal BA. Material property characterization of ultra-high performance concrete. United States. Federal Highway Administration. Office of Infrastructure …; 2006.
 Wee TH, Chin MS, Mansur MA. Stress-strain relationship of high-strength concrete in compression. J Mater Civ Eng 1996;8:70–6.
 Rashid MA, Mansur MA, Paramasivam P. Correlations between mechanical properties of high-strength concrete. J Mater Civ Eng 2002;14:230–8.
 Demir F. A new way of prediction elastic modulus of normal and high strength concrete—fuzzy logic. Cem Concr Res 2005;35:1531–8.
 Demir F. Prediction of elastic modulus of normal and high strength concrete by artificial neural networks. Constr Build Mater 2008;22:1428–35. https://doi.org/10.1016/j.conbuildmat.2007.04.004.
 Yan K, Shi C. Prediction of elastic modulus of normal and high strength concrete by support vector machine. Constr Build Mater 2010;24:1479–85.
 Gandomi AH, Faramarzifar A, Rezaee PG, Asghari A, Talatahari S. New design equations for elastic modulus of concrete using multi expression programming. J Civ Eng Manag 2015;21:761–74.
 Ahmadi-Nedushan B. Prediction of elastic modulus of normal and high strength concrete using ANFIS and optimal nonlinear regression models. Construction and Building Materials 2012;36:665–73.
 Ozturan T. An investigation of concrete abrasion as two phase material. Fac Civ Eng Istanbul Tech Univ 1984.
 Turan M, Iren M. Strain stress relationship of concrete. J Eng Archit 1997;12:76–81.
 Gesoǧlu M, Güneyisi E, Özturan T. Effects of end conditions on compressive strength and static elastic modulus of very high strength concrete. Cem Concr Res 2002;32:1545–50.
 Shannag MJ. High strength concrete containing natural pozzolan and silica fume. Cem Concr Compos 2000;22:399–406.
 Naderpour H, Rafiean AH, Fakharian P. Compressive strength prediction of environmentally friendly concrete using artificial neural networks. Journal of Building Engineering 2018;16:213–9.
 Naderpour H, Nagai K, Fakharian P, Haji M. Innovative models for prediction of compressive strength of FRP-confined circular reinforced concrete columns using soft computing methods. Compos Struct 2019;215:69–84. https://doi.org/10.1016/j.compstruct.2019.02.048.
 Naderpour H, Rezazadeh Eidgahee D, Fakharian P, Rafiean AH, Kalantari SM. A new proposed approach for moment capacity estimation of ferrocement members using Group Method of Data Handling. Eng Sci Technol an Int J 2020;23:382–91. https://doi.org/10.1016/j.jestch.2019.05.013.
 Shahmansouri AA, Akbarzadeh Bengar H, Jafari A. Modeling the lateral behavior of concrete rocking walls using multi-objective neural network. J Concr Struct Mater 2020;5:110–28.
 Shahmansouri AA, Akbarzadeh Bengar H, Ghanbari S. Experimental investigation and predictive modeling of compressive strength of pozzolanic geopolymer concrete using gene expression programming. J Concr Struct Mater 2020;5:92–117.
 Ghanizadeh AR, Ziaee A, Khatami SMH, Fakharian P. Predicting Resilient Modulus of Clayey Subgrade Soils by Means of Cone Penetration Test Results and Back-Propagation Artificial Neural Network. J Rehabil Civ Eng 2022;10:146–62. https://doi.org/10.22075/jrce.2022.25013.1568.
 Naderpour H, Sharei M, Fakharian P, Heravi MA. Shear Strength Prediction of Reinforced Concrete Shear Wall Using ANN, GMDH-NN and GEP. Journal of Soft Computing in Civil Engineering 2022;6:66–87.
 Mehrabian AR, Lucas C. A novel numerical optimization algorithm inspired from weed colonization. Ecol Inform 2006;1:355–66.
 Mohammed Abdelkader E, Moselhi O, Marzouk M, Zayed T. Hybrid Elman Neural Network and an Invasive Weed Optimization Method for Bridge Defect Recognition. Transp Res Rec 2021;2675:167–99.
 Asteris PG, Cavaleri L, Ly H-B, Pham BT. Surrogate models for the compressive strength mapping of cement mortar materials. Soft Comput 2021;25:6347–72.
 Huang L, Asteris PG, Koopialipoor M, Armaghani DJ, Tahir MM. Invasive weed optimization technique-based ANN to the prediction of rock tensile strength. Appl Sci 2019;9:5372.
 Smith GN. Probability and statistics in civil engineering. Collins London; 1986.