“…In addition, these models can also be used to double-check the quality of the laboratory tests, serving as an additional quality control check of the accuracy of the tests conducted in the laboratory. Thus, due to the urgent need for tools to predict these parameters, there have been many attempts in the literature to propose models to aid the prediction using linear regression analysis (LRA) (NCHRP, 2001;Gurtug and Sridharan, 2002;Gurtug et al, 2004;Ali et al, 2019;Katte et al, 2019;Gül and Çayir, 2020), multiple linear regression analysis (MLRA) (Reddy and Pavani, 2006;Vinod and Reena, 2008;Breytenbach et al, 2010;Patel and Desai, 2010;Yildirim and Gunaydin, 2011;Ferede, 2012;Alawi and Rajab, 2013;Mujtaba et al, 2013;Patel and Patel, 2013;Ramasubbarao and Siva Sankar, 2013;Talukdar, 2014;Erzin and Turkoz, 2016a, b;Rehman et al, 2017;Saikia et al, 2017;Al-Hamdani, 2018;Farias et al, 2018;Hohn et al, 2022), and soft computing techniques (Yildirim and Gunaydin, 2011;Venkatasubramanian and Dhinakaran, 2011;Kumar et al, 2013;Erzin and Turkoz, 2016a;Kurnaz and Kaya, 2019;Alam et al, 2020). The information collected from previous studies regarding the type of soil employed in the analysis, number of data points, soil parameters employed in the prediction, technique employed in the prediction, and the proposed models (if applicable) are presented in Table 1.…”