[1] Javdanian H, Lee S. Evaluating unconfined compressive strength of cohesive soils stabilized with geopolymer: a computational intelligence approach. Eng Comput 2019;35:191–9. https://doi.org/10.1007/s00366-018-0592-8.
[2] Wubshet M, Tadesse S. Stabilization of expansive soil using bagasse ash & lime. Zede J 2014;32:21–6.
[3] Jagadesh P, Ramachandramurthy A, Murugesan R. Overview on properties of sugarcane bagasse ash (SCBA) as Pozzolan. Indian J Geo-Marine Sci 2018;47:1934–1945.
[4] Osinubi KJ, Bafyau V, Eberemu AO. Bagasse Ash Stabilization of Lateritic Soil. Appropr. Technol. Environ. Prot. Dev. World, Dordrecht: Springer Netherlands; n.d., p. 271–80. https://doi.org/10.1007/978-1-4020-9139-1_26.
[5] Nethravathi S, Ramesh HN, Udayashankar BC, Vittal M. Improvement of Strength of Expansive Black Cotton Soil Using Sugarcane Bagasse Ash-Lime as Stabilizer. GeoEdmonton 2018;1:1–6.
[6] Dang LC, Fatahi B, Khabbaz H. Behaviour of Expansive Soils Stabilized with Hydrated Lime and Bagasse Fibres. Procedia Eng 2016;143:658–65. https://doi.org/10.1016/j.proeng.2016.06.093.
[7] James J, Pandian PK. Select geotechnical properties of a lime stabilized expansive soil amended with bagasse ash and coconut shell powder. Sel Sci Pap - J Civ Eng 2018;13:45–60. https://doi.org/10.1515/sspjce-2018-0005.
[8] Srikanth Reddy S, Prasad ACS V, Vamsi Krishna N. Lime-Stabilized Black Cotton Soil and Brick Powder Mixture as Subbase Material. Adv Civ Eng 2018;2018:5834685. https://doi.org/10.1155/2018/5834685.
[9] Goutham DR, Krishnaiah AJ. Improvement in Geotechnical Properties of Expansive Soil Using Various Stabilizers: A Review. Int J Eng Manuf 2020;10:18–27. https://doi.org/10.5815/ijem.2020.05.02.
[10] Ramesh HN, Rakesh C. Influence of Lime Sludge and Sodium Salts on the Strength and Structural Behavior of Clayey Soils–Granite Stone Slurry Dust Composite with Curing. Indian Geotech J 2020;50:801–9. https://doi.org/10.1007/s40098-019-00404-3.
[11] James J, Pandian PK. Bagasse Ash as an Auxiliary Additive to Lime Stabilization of an Expansive Soil: Strength and Microstructural Investigation. Adv Civ Eng 2018;2018:9658639. https://doi.org/10.1155/2018/9658639.
[12] Hossein Alavi A, Hossein Gandomi A, Mollahassani A, Akbar Heshmati A, Rashed A. Modeling of maximum dry density and optimum moisture content of stabilized soil using artificial neural networks. J Plant Nutr Soil Sci 2010;173:368–79. https://doi.org/10.1002/jpln.200800233.
[13] Mozumder RA, Laskar AI. Prediction of unconfined compressive strength of geopolymer stabilized clayey soil using Artificial Neural Network. Comput Geotech 2015;69:291–300. https://doi.org/10.1016/j.compgeo.2015.05.021.
[14] Bunyamin SA, Ijimdiya TS, Eberemu AO, Osinubi KJ. Artificial Neural Networks Prediction of Compaction Characteristics of Black Cotton Soil Stabilized with Cement Kiln Dust. J Soft Comput Civ Eng 2018;2:50–71. https://doi.org/10.22115/scce.2018.128634.1059.
[15] Taleb Bahmed I, Harichane K, Ghrici M, Boukhatem B, Rebouh R, Gadouri H. Prediction of geotechnical properties of clayey soils stabilised with lime using artificial neural networks (ANNs). Int J Geotech Eng 2019;13:191–203. https://doi.org/10.1080/19386362.2017.1329966.
[16] Nazeer S, Dutta RK. Application of Machine Learning Techniques in Predicting the Bearing Capacity of E-shaped Footing on Layered Sand. J Soft Comput Civ Eng 2021;5:74–89. https://doi.org/10.22115/scce.2021.303113.1360.
[17] 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. https://doi.org/10.22115/scce.2018.135575.1071.
[18] Goutham DR, Krishnaiah AJ. Application of Artificial Neural Networking Technique to Predict the Geotechnical Aspects of Expansive Soil: A Review. Int J Eng Manuf 2021;11:48–53. https://doi.org/10.5815/ijem.2021.06.05.
[19] Salahudeen AB, Sadeeq JA, Badamasi A, Onyelowe KC. Prediction of unconfined compressive strength of treated expansive clay using back-propagation artificial neural networks. Niger J Eng 2020;27:45–58.
[20] IS: 2720-Part 4 Methods of Test for Soils: Grain Size Analysis. Bureau of Indian Standards New Delhi. 1985.
[21] IS:1498 Classification and identification of soils for general engineering purposes. Bureau of Indian Standards New Delhi. 2007.
[22] ASTM D 653-03 Standard Specification for Coal Fly Ash and Raw or Calcined Natural Pozzolan for Use. Annual Book of ASTM Standards. 2010:3–6.
[23] IS: 2720-Part 5 Method of testing aggregates. Bureau of Indian Standards New Delhi. 1985:1–16.
[24] IS: 2720-Part VI Determination of shrinkage factors. Bureau of Indian Standard Code New Delhi. 1972:1–12.
[25] IS: 2720-Part VII Determination of water content dry density relation using light compaction. Bureau of Indian Standard Code New Delhi. 1980:1–16.
[26] IS: 2720-Part 10 Determination of unconfined compressive strength. Bureau of Indian Standards New Delhi. 1991:1–6.
[27] Olden JD, Joy MK, Death RG. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecol Modell 2004;178:389–97. https://doi.org/10.1016/j.ecolmodel.2004.03.013.
[28] Shahin MA, Jaksa MB, Maier HR. Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications. Adv Artif Neural Syst 2009;2009:1–9. https://doi.org/10.1155/2009/308239.
[29] Hanandeh S, Ardah A, Abu-Farsakh M. Using artificial neural network and genetics algorithm to estimate the resilient modulus for stabilized subgrade and propose new empirical formula. Transp Geotech 2020;24:100358. https://doi.org/10.1016/j.trgeo.2020.100358.
[30] Valdivia S, Morales A. Determinants of the index of prices and quotations on the Mexican stock exchange: Sensitivity analysis based on artificial neural networks. Glob J Bus Res 2016;10:27–32.