Aı̈tcin P-C. Cements of yesterday and today. Cem Concr Res 2000;30:1349–59. https://doi.org/10.1016/S0008-8846(00)00365-3.
 Habib A, Houri A AL, Habib M, Elzokra A, Yildirim U. Structural Performance and Finite Element Modeling of Roller Compacted Concrete Dams: A Review. Lat Am J Solids Struct 2021;18. https://doi.org/10.1590/1679-78256467.
 Shakhmenko G, Birsh J. Concrete mix design and optimization. Proc. 2nd Int. Symp. Civ. Eng., 1998, p. 1–8.
 Ni H-G, Wang J-Z. Prediction of compressive strength of concrete by neural networks. Cem Concr Res 2000;30:1245–50. https://doi.org/10.1016/S0008-8846(00)00345-8.
 Chopra P, Sharma RK, Kumar M, Chopra T. Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete. Adv Civ Eng 2018;2018:1–9. https://doi.org/10.1155/2018/5481705.
 Young BA, Hall A, Pilon L, Gupta P, Sant G. Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods. Cem Concr Res 2019;115:379–88. https://doi.org/10.1016/j.cemconres.2018.09.006.
 Feng D-C, Liu Z-T, Wang X-D, Chen Y, Chang J-Q, Wei D-F, et al. Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach. Constr Build Mater 2020;230:117000. https://doi.org/10.1016/j.conbuildmat.2019.117000.
 Kaloop MR, Kumar D, Samui P, Hu JW, Kim D. Compressive strength prediction of high-performance concrete using gradient tree boosting machine. Constr Build Mater 2020;264:120198. https://doi.org/10.1016/j.conbuildmat.2020.120198.
 Nguyen-Sy T, Wakim J, To Q-D, Vu M-N, Nguyen T-D, Nguyen T-T. Predicting the compressive strength of concrete from its compositions and age using the extreme gradient boosting method. Constr Build Mater 2020;260:119757. https://doi.org/10.1016/j.conbuildmat.2020.119757.
 Xu J, Zhou L, He G, Ji X, Dai Y, Dang Y. Comprehensive Machine Learning-Based Model for Predicting Compressive Strength of Ready-Mix Concrete. Materials (Basel) 2021;14:1068. https://doi.org/10.3390/ma14051068.
 Topçu İB, Sarıdemir M. Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Comput Mater Sci 2008;41:305–11. https://doi.org/10.1016/j.commatsci.2007.04.009.
 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.
 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.
 Habib A, Yıldırım U. Prediction of the dynamic properties in rubberized concrete. Comput Concr 2021;27:185–97.
 Barkhordari MS, Armaghani DJ, Fakharian P. Ensemble machine learning models for prediction of flyrock due to quarry blasting. Int J Environ Sci Technol 2022. https://doi.org/10.1007/s13762-022-04096-w.
 Han Q, Gui C, Xu J, Lacidogna G. A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm. Constr Build Mater 2019;226:734–42. https://doi.org/10.1016/j.conbuildmat.2019.07.315.
 Guryanov A. Histogram-Based Algorithm for Building Gradient Boosting Ensembles of Piecewise Linear Decision Trees, 2019, p. 39–50. https://doi.org/10.1007/978-3-030-37334-4_4.
 Farooq F, Nasir Amin M, Khan K, Rehan Sadiq M, Faisal Javed MF, Aslam F, et al. A Comparative Study of Random Forest and Genetic Engineering Programming for the Prediction of Compressive Strength of High Strength Concrete (HSC). Appl Sci 2020;10:7330. https://doi.org/10.3390/app10207330.
 Jha AK, Adhikari S, Thapa S, Kumar A, Kumar A, Mishra S. Evaluation of Factors Affecting Compressive Strength of Concrete using Machine Learning. 2020 Adv. Comput. Commun. Technol. High Perform. Appl., IEEE; 2020, p. 70–4. https://doi.org/10.1109/ACCTHPA49271.2020.9213199.
 Anysz H, Brzozowski Ł, Kretowicz W, Narloch P. Feature Importance of Stabilised Rammed Earth Components Affecting the Compressive Strength Calculated with Explainable Artificial Intelligence Tools. Materials (Basel) 2020;13:2317. https://doi.org/10.3390/ma13102317.
 Ly H-B, Nguyen T-A, Thi Mai H-V, Tran VQ. Development of deep neural network model to predict the compressive strength of rubber concrete. Constr Build Mater 2021;301:124081. https://doi.org/10.1016/j.conbuildmat.2021.124081.
 Su M, Zhong Q, Peng H. Regularized multivariate polynomial regression analysis of the compressive strength of slag-metakaolin geopolymer pastes based on experimental data. Constr Build Mater 2021;303:124529. https://doi.org/10.1016/j.conbuildmat.2021.124529.
 Dao D Van, Ly H-B, Vu H-LT, Le T-T, Pham BT. Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete. Materials (Basel) 2020;13:1072. https://doi.org/10.3390/ma13051072.
 Mane KM, Kulkarni DK, Prakash KB. Prediction of flexural strength of concrete produced by using pozzolanic materials and partly replacing NFA by MS. J Soft Comput Civ Eng 2019;3:65–75.
 Nayak SC, Nayak SK, Panda SK. Assessing compressive strength of concrete with extreme learning machine. J Soft Comput Civ Eng 2021;5:68–85.
 Pandey S, Kumar V, Kumar P. Application and Analysis of Machine Learning Algorithms for Design of Concrete Mix with Plasticizer and without Plasticizer. J Soft Comput Civ Eng 2021;5:19–37.
 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.
 Amini A, Abdollahi A, Hariri-Ardebili MA, Lall U. Copula-based reliability and sensitivity analysis of aging dams: Adaptive Kriging and polynomial chaos Kriging methods. Appl Soft Comput 2021;109:107524. https://doi.org/10.1016/j.asoc.2021.107524.
 Rajan K. Materials informatics. Mater Today 2005;8:38–45. https://doi.org/10.1016/S1369-7021(05)71123-8.
 Himanen L, Geurts A, Foster AS, Rinke P. Data‐Driven Materials Science: Status, Challenges, and Perspectives. Adv Sci 2019;6:1900808. https://doi.org/10.1002/advs.201900808.
 Marsland S. Machine learning: an algorithmic perspective. Chapman and Hall/CRC; 2011.
 Kotsiantis SB, Zaharakis I, Pintelas P. Supervised machine learning: A review of classification techniques. Emerg Artif Intell Appl Comput Eng 2007;160:3–24.
 Mahesh B. Machine learning algorithms-a review. Int J Sci Res (IJSR)[Internet] 2020;9:381–6.
 Sharma D, Kumar N. A review on machine learning algorithms, tasks and applications. Int J Adv Res Comput Eng Technol 2017;6:1323–2278.
 Myles AJ, Feudale RN, Liu Y, Woody NA, Brown SD. An introduction to decision tree modeling. J Chemom 2004;18:275–85. https://doi.org/10.1002/cem.873.
 Quinlan JR. Decision trees and decision-making. IEEE Trans Syst Man Cybern 1990;20:339–46. https://doi.org/10.1109/21.52545.
 Chen Y-L, Hung LT-H. Using decision trees to summarize associative classification rules. Expert Syst Appl 2009;36:2338–51. https://doi.org/10.1016/j.eswa.2007.12.031.
 Loh W. Classification and regression trees. Wiley Interdiscip Rev Data Min Knowl Discov 2011;1:14–23.
 Charbuty B, Abdulazeez A. Classification Based on Decision Tree Algorithm for Machine Learning. J Appl Sci Technol Trends 2021;2:20–8. https://doi.org/10.38094/jastt20165.
 Janikow CZ. Fuzzy decision trees: issues and methods. IEEE Trans Syst Man Cybern Part B 1998;28:1–14. https://doi.org/10.1109/3477.658573.
 Breiman L. Random forests. Mach Learn 2001;45:5–32.
 Micheletti N, Foresti L, Robert S, Leuenberger M, Pedrazzini A, Jaboyedoff M, et al. Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping. Math Geosci 2014;46:33–57. https://doi.org/10.1007/s11004-013-9511-0.
 Williams G. Data mining with Rattle and R: The art of excavating data for knowledge discovery. Springer Science & Business Media; 2011.
 Rodriguez-Galiano V, Sanchez-Castillo M, Chica-Olmo M, Chica-Rivas M. Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geol Rev 2015;71:804–18. https://doi.org/10.1016/j.oregeorev.2015.01.001.
 Zhang J, Ma G, Huang Y, Sun J, Aslani F, Nener B. Modelling uniaxial compressive strength of lightweight self-compacting concrete using random forest regression. Constr Build Mater 2019;210:713–9. https://doi.org/10.1016/j.conbuildmat.2019.03.189.
 Geurts P, Ernst D, Wehenkel L. Extremely randomized trees. Mach Learn 2006;63:3–42. https://doi.org/10.1007/s10994-006-6226-1.
 Freund Y, Schapire RE. A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. J Comput Syst Sci 1997;55:119–39. https://doi.org/10.1006/jcss.1997.1504.
 Drucker H. Improving regressors using boosting techniques. ICML, vol. 97, Citeseer; 1997, p. 107–15.
 Chen T, Guestrin C. Xgboost: A scalable tree boosting system. Proc. 22nd acm sigkdd Int. Conf. Knowl. Discov. data Min., 2016, p. 785–94.
 Feng D-C, Wang W-J, Mangalathu S, Hu G, Wu T. Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements. Eng Struct 2021;235:111979. https://doi.org/10.1016/j.engstruct.2021.111979.
 Fisher A, Rudin C, Dominici F. All Models are Wrong, but Many are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously. J Mach Learn Res 2019;20:1–81.
 Archer KJ, Kimes R V. Empirical characterization of random forest variable importance measures. Comput Stat Data Anal 2008;52:2249–60. https://doi.org/10.1016/j.csda.2007.08.015.
 Strobl C, Boulesteix A-L, Zeileis A, Hothorn T. Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics 2007;8:25. https://doi.org/10.1186/1471-2105-8-25.
 Biecek P. DALEX: explainers for complex predictive models in R. J Mach Learn Res 2018;19:3245–9.
 Yeh I-C. Modeling of strength of high-performance concrete using artificial neural networks. Cem Concr Res 1998;28:1797–808. https://doi.org/10.1016/S0008-8846(98)00165-3.
 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).
 Asteris PG, Skentou AD, Bardhan A, Samui P, Pilakoutas K. Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models. Cem Concr Res 2021;145:106449. https://doi.org/10.1016/j.cemconres.2021.106449.
 Ke X, Duan Y. A Bayesian machine learning approach for inverse prediction of high-performance concrete ingredients with targeted performance. Constr Build Mater 2021;270:121424. https://doi.org/10.1016/j.conbuildmat.2020.121424.
 Ramezanianpour AA, Malhotra VM. Effect of curing on the compressive strength, resistance to chloride-ion penetration and porosity of concretes incorporating slag, fly ash or silica fume. Cem Concr Compos 1995;17:125–33. https://doi.org/10.1016/0958-9465(95)00005-W.
 Sengul O, Tasdemir MA. Compressive Strength and Rapid Chloride Permeability of Concretes with Ground Fly Ash and Slag. J Mater Civ Eng 2009;21:494–501. https://doi.org/10.1061/(ASCE)0899-1561(2009)21:9(494).
 Alsadey S. Influence of superplasticizer on strength of concrete. Int J Res Eng Technol 2012;1:164–6.