[1] Edil TB, den Haan EJ. Settlement of peats and organic soils. Vert. Horiz. Deform. Found. Embankments, ASCE; 1994, p. 1543–72.
[2] Kaniraj SR, Havanagi VG. Compressive strength of cement stabilized fly ash-soil mixtures. Cem Concr Res 1999;29:673–7. https://doi.org/10.1016/S0008-8846(99)00018-6.
[3] Parsons RL, Kneebone E. Field performance of fly ash stabilised subgrades. Proc Inst Civ Eng - Gr Improv 2005;9:33–8. https://doi.org/10.1680/grim.2005.9.1.33.
[4] Ismeik M, Al-Rawi O. Modeling Soil Specific Surface Area with Artificial Neural Networks. Geotech Test J 2014;37:20130146. https://doi.org/10.1520/GTJ20130146.
[5] Sarmadian F, Taghizadeh Mehrjardi R. Modeling of some soil properties using artificial neural network and multivariate regression in Gorgan Province, North of Iran. Glob J Environ Res 2008;2:30–5.
[6] Park HI. Development of Neural Network Model to Estimate the Permeability Coefficient of Soils. Mar Georesources Geotechnol 2011;29:267–78. https://doi.org/10.1080/1064119X.2011.554963.
[7] Kolay E, Baser T. Estimating of the Dry Unit Weight of Compacted Soils Using General Linear Model and Multi-layer Perceptron Neural Networks. Appl Soft Comput 2014;18:223–31. https://doi.org/10.1016/j.asoc.2014.01.033.
[8] Yilmaz I, Kaynar O. Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Syst Appl 2011;38:5958–66. https://doi.org/10.1016/j.eswa.2010.11.027.
[9] Ikizler SB, Aytekin M, Vekli M, Kocabaş F. Prediction of swelling pressures of expansive soils using artificial neural networks. Adv Eng Softw 2010;41:647–55. https://doi.org/10.1016/j.advengsoft.2009.12.005.
[10] Banimahd M, Yasrobi SS, P.K.Woodward. Artificial neural network for stress–strain behavior of sandy soils: Knowledge based verification. Comput Geotech 2005;32:377–86. https://doi.org/10.1016/j.compgeo.2005.06.002.
[11] Kayadelen C. Estimation of effective stress parameter of unsaturated soils by using artificial neural networks. Int J Numer Anal Methods Geomech 2008;32:1087–106. https://doi.org/10.1002/nag.660.
[12] Dutta RK, Dutta K, Jeevanandham S. Prediction of Deviator Stress of Sand Reinforced with Waste Plastic Strips Using Neural Network. Int J Geosynth Gr Eng 2015;1:11. https://doi.org/10.1007/s40891-015-0013-7.
[13] Samui P. Support vector machine applied to settlement of shallow foundations on cohesionless soils. Comput Geotech 2008;35:419–27. https://doi.org/10.1016/j.compgeo.2007.06.014.
[14] Nazir R, Momeni E, Marsono K, Maizir H. An Artificial Neural Network Approach for Prediction of Bearing Capacity of Spread Foundations in Sand. J Teknol 2015;72. https://doi.org/10.11113/jt.v72.4004.
[15] Kalinli A, Acar MC, Gündüz Z. New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization. Eng Geol 2011;117:29–38. https://doi.org/10.1016/j.enggeo.2010.10.002.
[16] Ornek M. Estimation of ultimate loads of eccentric-inclined loaded strip footings rested on sandy soils. Neural Comput Appl 2014;25:39–54. https://doi.org/10.1007/s00521-013-1444-5.
[17] Ornek M, Laman M, Demir A, Yildiz A. Prediction of bearing capacity of circular footings on soft clay stabilized with granular soil. Soils Found 2012;52:69–80. https://doi.org/10.1016/j.sandf.2012.01.002.
[18] Gnananandarao T, Khatri VN, Dutta RK. Bearing capacity and settlement prediction of multi-edge skirted footings resting on sand. Ing e Investig 2020;40:9–21. https://doi.org/10.15446/ing.investig.v40n3.83170.
[19] Gnananandarao T, Dutta RK, Khatri VN. Application of Artificial Neural Network to Predict the Settlement of Shallow Foundations on Cohesionless Soils, 2019, p. 51–8. https://doi.org/10.1007/978-981-13-0368-5_6.
[20] Dutta RK, Rani R, Gnananandarao T. Prediction of ultimate bearing capacity of skirted footing resting on sand using artificial neural networks. J Soft Comput Civ Eng 2018;2:34–46.
[21] Das SK, Basudhar PK. Undrained lateral load capacity of piles in clay using artificial neural network. Comput Geotech 2006;33:454–9. https://doi.org/10.1016/j.compgeo.2006.08.006.
[22] Pal M, Deswal S. Modeling Pile Capacity Using Support Vector Machines and Generalized Regression Neural Network. J Geotech Geoenvironmental Eng 2008;134:1021–4. https://doi.org/10.1061/(ASCE)1090-0241(2008)134:7(1021).
[23] Shishegaran A, Varaee H, Rabczuk T, Shishegaran G. High correlated variables creator machine: Prediction of the compressive strength of concrete. Comput Struct 2021;247:106479. https://doi.org/10.1016/j.compstruc.2021.106479.
[24] Shishegaran A, Karami B, Safari Danalou E, Varaee H, Rabczuk T. Computational predictions for predicting the performance of steel 1 panel shear wall under explosive loads. Eng Comput 2021;38:3564–89. https://doi.org/10.1108/EC-09-2020-0492.
[25] Shishegaran A, Taghavizade H, Bigdeli A, Shishegaran A. Predicting the Earthquake Magnitude along Zagros Fault Using Time Series and Ensemble Model. J Soft Comput Civ Eng 2019;3:67–77.
[26] Noori F, Varaee H. Nonlinear Seismic Response Approximation Of Steel Moment Frames Using Artificial Neural Networks. Jordan J Civ Eng 2022;16.
[27] Safaeian Hamzehkolaei N, Alizamir M. Performance evaluation of machine learning algorithms for seismic retrofit cost estimation using structural parameters. J Soft Comput Civ Eng 2021;5:32–57.
[28] Ma C, Xie Y, Long G, Chen B, Chen L. Effects of fly ash on mechanical and physical properties of earth-based construction. Constr Build Mater 2017;157:1074–83. https://doi.org/10.1016/j.conbuildmat.2017.09.122.
[29] Jongpradist P, Jumlongrach N, Youwai S, Chucheepsakul S. Influence of Fly Ash on Unconfined Compressive Strength of Cement-Admixed Clay at High Water Content. J Mater Civ Eng 2010;22:49–58. https://doi.org/10.1061/(ASCE)0899-1561(2010)22:1(49).
[30] Tastan EO, Edil TB, Benson CH, Aydilek AH. Stabilization of Organic Soils with Fly Ash. J Geotech Geoenvironmental Eng 2011;137:819–33. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000502.
[31] Dutta RK, Gnananandarao T, Ladol S. Soft computing based prediction of friction angle of clay. Arch Mater Sci Eng 2020;2:58–68. https://doi.org/10.5604/01.3001.0014.4895.
[32] Gnananandarao T, Khatri VN, Dutta RK. Prediction of bearing capacity of H shaped skirted footings on sand using soft computing techniques. Arch Mater Sci Eng 2020;2:62–74. https://doi.org/10.5604/01.3001.0014.3356.
[33] Dutta RK, Gnananandarao T, Sharma A. Application of random forest regression in the Prediction of ultimate bearing capacity of strip footing resting on dense sand overlying loose sand deposit. J Soft Comput Civ Eng 2019;3:28–40.
[34] Dutta RK, Gnananandarao T, Khatri VN. Application of Soft Computing Techniques in Predicting the Ultimate Bearing Capacity of Strip Footing Subjected to Eccentric Inclined Load and Resting on Sand. J Soft Comput Civ Eng 2019;3:30–42.
[35] Dutta RK, Singh A, Gnananandarao T. Prediction of free swell index for the expansive soil using artificial neural networks. J Soft Comput Civ Eng 2019;3:47–62.
[36] Bishop CM. Neural networks for pattern recognition. Oxford university press; 1995.
[37] L. B. Random forests—random features. Technical Report 567, Statistics Department. University of California, Berkeley 1999.
[38] Breiman L. Bagging predictors. Mach Learn 1996;24:123–40. https://doi.org/10.1007/BF00058655.
[39] Quinlan JR. Learning with continuous classes In: Adams A, Sterling L, editors n.d.
[40] L B, JH F, RA O, CJ S. Classifcationand regression trees. Wadsworth, Monterey 1984.
[41] Pal M, Mather PM. An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens Environ 2003;86:554–65. https://doi.org/10.1016/S0034-4257(03)00132-9.
[42] Feller W. An introduction to probability theory and its applications, vol 2. John Wiley & Sons; 2008.
[43] Vapnik V. The nature of statistical learning theory. Springer science & business media; 1999.
[44] Puri N, Prasad HD, Jain A. Prediction of Geotechnical Parameters Using Machine Learning Techniques. Procedia Comput Sci 2018;125:509–17. https://doi.org/10.1016/j.procs.2017.12.066.
[45] Kitazume M. State of practice report–Field and laboratory investigations, properties of binders and stabilized soil. Proc. Int. Conf. Deep Mix. Best Pract. Recent Adv., vol. 2, 2005, p. 660–84.
[46] Onyelowe KC, Gnananandarao T, Ebid AM. Estimation of the erodibility of treated unsaturated lateritic soil using support vector machine-polynomial and -radial basis function and random forest regression techniques. Clean Mater 2022;3:100039. https://doi.org/10.1016/j.clema.2021.100039.
[47] Onyelowe KC, Gnananandarao T, Nwa-David C. Sensitivity analysis and prediction of erodibility of treated unsaturated soil modified with nanostructured fines of quarry dust using novel artificial neural network. Nanotechnol Environ Eng 2021;6:37. https://doi.org/10.1007/s41204-021-00131-2.