Tougwa FN. Some Major Challenges Faced by Civil Engineering Professionals in the Execution of their Profession and the impact of the challenges to the Environment, Society and Economy of Developing Countries. Curr Trends Civ Struct Eng 2020;5. https://doi.org/10.33552/CTCSE.2020.05.000622.
 Shah KW, Huseien GF. Biomimetic Self-Healing Cementitious Construction Materials for Smart Buildings. Biomimetics 2020;5:47. https://doi.org/10.3390/biomimetics5040047.
 Roig-Flores M, Formagini S, Serna P. Self-healing concrete-what is it good for? Mater Construcción 2021;71:e237–e237. https://doi.org/10.3989/mc.
 Naderpour H, Rafiean AH, Fakharian P. Compressive strength prediction of environmentally friendly concrete using artificial neural networks. J Build Eng 2018;16:213–9. https://doi.org/10.1016/j.jobe.2018.01.007.
 Ghosh SK. Self-healing materials: fundamentals, design strategies, and applications. vol. 18. Wiley Online Library; 2009.
 Chernykh TN, Bondarenko SA, Zimich V V. Study of natural self-healing of materials based on inorganic binders. IOP Conf Ser Mater Sci Eng 2020;962:022040. https://doi.org/10.1088/1757-899X/962/2/022040.
 Azarsa P, Gupta R, Biparva A. Assessment of self-healing and durability parameters of concretes incorporating crystalline admixtures and Portland Limestone Cement. Cem Concr Compos 2019;99:17–31. https://doi.org/10.1016/j.cemconcomp.2019.02.017.
 Chandra Sekhara Reddy T, Ravitheja A. Macro mechanical properties of self healing concrete with crystalline admixture under different environments. Ain Shams Eng J 2019;10:23–32. https://doi.org/10.1016/j.asej.2018.01.005.
 Iqbal MF, Liu Q, Azim I, Zhu X, Yang J, Javed MF, et al. Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming. J Hazard Mater 2020;384:121322. https://doi.org/10.1016/j.jhazmat.2019.121322.
 Abdulla NA. Using the artificial neural network to predict the axial strength and strain of concrete-filled plastic tube. J Soft Comput Civ Eng 2020;4:63–84. https://doi.org/10.22115/SCCE.2020.225161.1198.
 Kekez S, Kubica J. Application of Artificial Neural Networks for Prediction of Mechanical Properties of CNT/CNF Reinforced Concrete. Materials (Basel) 2021;14:5637. https://doi.org/10.3390/ma14195637.
 Mukherjee A, Schemauder S, Ruhle M. Artificial neural network for the prediction of the mechanical behaviour of metal matrix composite. Acta Met Mater 1995;43:4083–4091.
 Chopra P, Sharma RK, Kumar M. Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming. Adv Mater Sci Eng 2016;2016:1–10. https://doi.org/10.1155/2016/7648467.
 Sharifi Y, Hosseinpour M. A predictive model based ANN for compressive strength assessment of the mortars containing metakaolin. J Soft Comput Civ Eng 2020;4:1–12. https://doi.org/10.22115/SCCE.2020.214444.1157.
 Hamdia KM, Lahmer T, Nguyen-Thoi T, Rabczuk T. Predicting the fracture toughness of PNCs: A stochastic approach based on ANN and ANFIS. Comput Mater Sci 2015;102:304–13. https://doi.org/10.1016/j.commatsci.2015.02.045.
 Bal L, Buyle-Bodin F. Artificial neural network for predicting drying shrinkage of concrete. Constr Build Mater 2013;38:248–54. https://doi.org/10.1016/j.conbuildmat.2012.08.043.
 Başyigit C, Akkurt I, Kilincarslan S, Beycioglu A. Prediction of compressive strength of heavyweight concrete by ANN and FL models. Neural Comput Appl 2010;19:507–13. https://doi.org/10.1007/s00521-009-0292-9.
 Dias WPS, Pooliyadda SP. Neural networks for predicting properties of concretes with admixtures. Constr Build Mater 2001;15:371–9. https://doi.org/10.1016/S0950-0618(01)00006-X.
 Adeli H. Neural Networks in Civil Engineering: 1989–2000. Comput Civ Infrastruct Eng 2001;16:126–42. https://doi.org/10.1111/0885-9507.00219.
 Ashraf M, Iqbal MF, Rauf M, Ashraf MU, Ulhaq A, Muhammad H, et al. Developing a sustainable concrete incorporating bentonite clay and silica fume: Mechanical and durability performance. J Clean Prod 2022;337:130315. https://doi.org/10.1016/j.jclepro.2021.130315.
 Iqbal MF, Javed MF, Rauf M, Azim I, Ashraf M, Yang J, et al. Sustainable utilization of foundry waste: Forecasting mechanical properties of foundry sand based concrete using multi-expression programming. Sci Total Environ 2021;780:146524. https://doi.org/10.1016/j.scitotenv.2021.146524.
 Alexandridis A, Triantis D, Stavrakas I, Stergiopoulos C. A neural network approach for compressive strength prediction in cement-based materials through the study of pressure-stimulated electrical signals. Constr Build Mater 2012;30:294–300. https://doi.org/10.1016/j.conbuildmat.2011.11.036.
 Muhammad K, Mohammad N, Rehman F. Modeling shotcrete mix design using artificial neural network. Comput Concr 2015;15:167–81. https://doi.org/10.12989/cac.2015.15.2.167.
 Bilim C, Atiş CD, Tanyildizi H, Karahan O. Predicting the compressive strength of ground granulated blast furnace slag concrete using artificial neural network. Adv Eng Softw 2009;40:334–40. https://doi.org/10.1016/j.advengsoft.2008.05.005.
 Erdem H. Prediction of the moment capacity of reinforced concrete slabs in fire using artificial neural networks. Adv Eng Softw 2010;41:270–6. https://doi.org/10.1016/j.advengsoft.2009.07.006.
 Erdal HI, Karakurt O, Namli E. High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform. Eng Appl Artif Intell 2013;26:1246–54. https://doi.org/10.1016/j.engappai.2012.10.014.
 Cheng M-Y, Firdausi PM, Prayogo D. High-performance concrete compressive strength prediction using Genetic Weighted Pyramid Operation Tree (GWPOT). Eng Appl Artif Intell 2014;29:104–13. https://doi.org/10.1016/j.engappai.2013.11.014.
 Ghafari E, Bandarabadi M, Costa H, Júlio E. Prediction of Fresh and Hardened State Properties of UHPC: Comparative Study of Statistical Mixture Design and an Artificial Neural Network Model. J Mater Civ Eng 2015;27. https://doi.org/10.1061/(ASCE)MT.1943-5533.0001270.
 Gupta S. Using Artificial Neural Network to Predict the Compressive Strength of Concrete containing Nano-silica. Civ Eng Archit 2013;1:96–102. https://doi.org/10.13189/cea.2013.010306.
 Mush D, Horne B. Progress in supervised neural networks: what’s new since Lippman. IEEE Signal Process Mag 1993:8–39.
 Najigivi A, Khaloo A, Iraji zad A, Abdul Rashid S. An Artificial Neural Networks Model for Predicting Permeability Properties of Nano Silica–Rice Husk Ash Ternary Blended Concrete. Int J Concr Struct Mater 2013;7:225–38. https://doi.org/10.1007/s40069-013-0038-z.
 Naderpour H, Sharei M, Fakharian P, Heravi MA. Shear Strength Prediction of Reinforced Concrete Shear Wall Using ANN, GMDH-NN and GEP. J Soft Comput Civ Eng 2022;6:66–87. https://doi.org/10.22115/SCCE.2022.283486.1308.
 Sincero AP. Predicting Mixing Power Using Artificial Neural Network. World Water & Environ. Resour. Congr. 2003, Reston, VA: American Society of Civil Engineers; 2003, p. 1–9. https://doi.org/10.1061/40685(2003)262.
 Słoński M. A comparison of model selection methods for compressive strength prediction of high-performance concrete using neural networks. Comput Struct 2010;88:1248–53. https://doi.org/10.1016/j.compstruc.2010.07.003.
 Sarıdemir M. Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic. Adv Eng Softw 2009;40:920–7. https://doi.org/10.1016/j.advengsoft.2008.12.008.
 Yeh I-C. Analysis of Strength of Concrete Using Design of Experiments and Neural Networks. J Mater Civ Eng 2006;18:597–604. https://doi.org/10.1061/(ASCE)0899-1561(2006)18:4(597).
 Yeh I-C. Design of High-Performance Concrete Mixture Using Neural Networks and Nonlinear Programming. J Comput Civ Eng 1999;13:36–42. https://doi.org/10.1061/(ASCE)0887-3801(1999)13:1(36).
 (Tony) Hou T-H, Su C-H, Chang H-Z. Using neural networks and immune algorithms to find the optimal parameters for an IC wire bonding process. Expert Syst Appl 2008;34:427–36. https://doi.org/10.1016/j.eswa.2006.09.024.
 Kostić S, Vasović D. Prediction model for compressive strength of basic concrete mixture using artificial neural networks. Neural Comput Appl 2015;26:1005–24. https://doi.org/10.1007/s00521-014-1763-1.
 Lee S-C. Prediction of concrete strength using artificial neural networks. Eng Struct 2003;25:849–57. https://doi.org/10.1016/S0141-0296(03)00004-X.
 Öztaş A, Pala M, Özbay E, Kanca E, Çagˇlar N, Bhatti MA. Predicting the compressive strength and slump of high strength concrete using neural network. Constr Build Mater 2006;20:769–75. https://doi.org/10.1016/j.conbuildmat.2005.01.054.
 Sheela KG, Deepa SN. Review on Methods to Fix Number of Hidden Neurons in Neural Networks. Math Probl Eng 2013;2013:1–11. https://doi.org/10.1155/2013/425740.