Abualigah L, et al (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609. https://linkinghub.elsevier.com/retrieve/pii/S0045782520307945.
Ahmed HU, Mohammed AS, Mohammed AA, Faraj RH (2021) Systematic multiscale models to predict the compressive strength of fly ash-based geopolymer concrete at various mixture proportions and curing regimes ed Tianyu Xie. . PLOS One 16(6):e0253006. https://doi.org/10.1371/journal.pone.0253006
Alobaidi YM, Hilal NN, Faraj RH (2021) An experimental investigation on the nano-fly ash preparation and its effects on the performance of self-compacting concrete at normal and elevated temperatures. Nanotechnol Environ Eng 6(1):1–13
A’kif A-F et al (2020) Spatial mapping of groundwater springs potentiality using grid search-based and genetic algorithm-based support vector regression. Geocarto Int 24:1–20
Amin MN, et al (2021) Comparison of machine learning approaches with traditional methods for predicting the compressive strength of rice husk ash concrete. Crystals 11(7): 779. https://www.mdpi.com/2073-4352/11/7/779.
Armaghani DJ, et al (2020) On the use of neuro-swarm system to forecast the pile settlement. Appl Sci 10(6): 1904. https://www.mdpi.com/2076-3417/10/6/1904.
Ashrafian A, et al (2020) Compressive strength of foamed cellular lightweight concrete simulation: new development of hybrid artificial intelligence model. Construct Build Mater 230: 117048. https://linkinghub.elsevier.com/retrieve/pii/S0950061819324900.
Atici U, (2011) “Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network. Expert Syst Appl 38(8): 9609–18. https://linkinghub.elsevier.com/retrieve/pii/S0957417411001898.
Azimi-Pour M, Hamid E-N, Amir P (2020) Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete. Construct Build Mater 230: 117021. https://linkinghub.elsevier.com/retrieve/pii/S0950061819324638.
Behnood A, Emadaldin MG (2018) Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves. J Clean Product 202: 54–64. https://linkinghub.elsevier.com/retrieve/pii/S0959652618324016.
Behnood A, Kho PV, Mahsa MG (2015) Evaluation of the splitting tensile strength in plain and steel fiber-reinforced concrete based on the compressive strength. Construct Build Mater 98: 519–29. https://linkinghub.elsevier.com/retrieve/pii/S095006181530341X.
Cai J, Xiaopeng L, Jiawei T, Brecht V (2020) Thermal and compressive behaviors of fly ash and metakaolin-based geopolymer. J Build Eng 30: 101307. https://linkinghub.elsevier.com/retrieve/pii/S2352710219319497.
Chou J-S, Chih-Fong T, Anh-Duc P, Yu-Hsin L (2014) Machine learning in concrete strength simulations: multi-nation data analytics. Construct Build Mater 73: 771–80. https://linkinghub.elsevier.com/retrieve/pii/S0950061814010708.
Detwiler RJ, Javed IB, Battacharja S (1996) Supplementary cementing materials for use in blended cements.
Esmaeili-Choobar N, Esmaeili-Falak M, Roohi-hir M, Keshtzad S (2013) Evaluation of collapsibility potential at talesh, Iran. EJGE: 2561–73.
Esmaeili-Falak M, Hooshang K, Meysam V, Jan A (2019) Predicting triaxial compressive strength and young’s modulus of frozen sand using artificial intelligence methods. J Cold Reg Eng 33(3):04019007. https://doi.org/10.1061/%28ASCE%29CR.1943-5495.0000188
Faraj RH, Aryan Far HS, Lamyaa HJ, Dalya FI (2021) Rheological behavior and fresh properties of self-compacting high strength concrete containing recycled pp particles with fly ash and silica fume blended. J Build Eng 34:101667
Faraj RH, Azad AM et al (2021) Systematic multiscale models to predict the compressive strength of self-compacting concretes modified with nanosilica at different curing ages. Eng Comput. https://doi.org/10.1007/s00366-021-01385-9
Ganesh Babu K, Siva Nageswara Rao G (1994) Early strength behaviour of fly ash concretes. Cem Conc Res 24(2): 277–84. https://linkinghub.elsevier.com/retrieve/pii/0008884694900531.
Hemavathi S, Sumil Kumaran A, Sindhu R (2020) An experimental investigation on properties of concrete by using silica fume and glass fibre as admixture. Mater Today Proc 21: 456–59. https://linkinghub.elsevier.com/retrieve/pii/S2214785319319303.
Hilal, NN, Noor AR, Rabar HF (2020) Fresh behavior and hardened properties of self-compacting concrete containing coal ash and fly ash as partial replacement of cement. In: IOP Conference Series: Materials Science and Engineering, IOP Publishing, pp 12005.
Hubertova M, Hela R (2007) The effect of metakaolin and silica fume on the properties of lightweight self consolidating concrete. Spec Publ 243:35–48
Lam L, Wong YL, Poon CS (1998) Effect of fly ash and silica fume on compressive and fracture behaviors of concrete. Cem Conc Res 28(2): 271–83. https://linkinghub.elsevier.com/retrieve/pii/S000888469700269X.
Kjellsen KO, Wallevik OH, Hallgren M (1999) On the compressive strength development of high-performance concrete and paste—effect of silica fume. Mater Struct 32(1):63. https://doi.org/10.1007/BF02480414
Masoumi F, Najjar-Ghabel S, Safarzadeh A, Sadaghat B (2020) Automatic calibration of the groundwater simulation model with high parameter dimensionality using sequential uncertainty fitting approach. Water Supply 20(8):3487–3501
Nochaiya T, Watcharapong W, Arnon C (2010) Utilization of fly ash with silica fume and properties of portland cement–fly ash–silica fume concrete. Fuel 89(3): 768–74. https://linkinghub.elsevier.com/retrieve/pii/S0016236109004657.
Pala M, Erdoğan Ö, Ahmet Ö, Ishak Yuce M (2007) Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks. Construct Build Mater 21(2): 384–94. https://linkinghub.elsevier.com/retrieve/pii/S0950061805002539.
Parande AK, et al. (2008) Study on strength and corrosion performance for steel embedded in metakaolin blended concrete/mortar. Construct Build Mater 22(3): 127–34. https://linkinghub.elsevier.com/retrieve/pii/S0950061806002844.
Pazouki G, Emadaldin MG, Ali B (2021) Predicting the compressive strength of self‐compacting concrete containing class F fly ash using metaheuristic radial basis function neural network. Struct Conc. https://onlinelibrary.wiley.com/doi/10.1002/suco.202000047.
Shariati M et al (2020) A novel hybrid extreme learning machine-grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement. Eng Comput. https://doi.org/10.1007/s00366-020-01081-0
Siddique R (2004) Performance characteristics of high-volume class F fly ash concrete. Cement Conc Res 34(3): 487–93. https://linkinghub.elsevier.com/retrieve/pii/S0008884603003107.
Taffese, WZ, Esko S (2017) Machine learning for durability and service-life assessment of reinforced concrete structures: recent advances and future directions. Automat Construct 77: 1–14. https://linkinghub.elsevier.com/retrieve/pii/S0926580517300559.
Topçu İB, Mustafa S (2008) Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Computat Mater Science 41(3): 305–11. https://linkinghub.elsevier.com/retrieve/pii/S0927025607001085.
Toutanji, H. et al. (2004) Effect of supplementary cementitious materials on the compressive strength and durability of short-term cured concrete. Cem Conc Res 34(2): 311–19. https://linkinghub.elsevier.com/retrieve/pii/S0008884603002953.
Turk K, Turgut P, Karatas M, Benli A (2010) Mechanical properties of selfcompacting concrete with silica fume/fly ash. In: 9th International Congress on Advances in Civil Engineering, pp 27–30.
Vapnik V (2013) The nature of statistical learning theory neural networks.
Wang L (2005) 177 Support vector machines: theory and applications. Springer Science and Business Media
Yaprak H, Abdülkadir K, İlhami D (2013) Prediction of the effect of varying cure conditions and w/c ratio on the compressive strength of concrete using artificial neural networks. Neural Comput Appl 22(1): 133–41. http://link.springer.com/10.1007/s00521-011-0671-x.
Yeh I-C (1998) Modeling of strength of high-performance concrete using artificial neural networks. Cem Conc Res 28(12): 1797–1808. https://linkinghub.elsevier.com/retrieve/pii/S0008884698001653