Estimating the Strength of Stabilized Dispersive Soil with Cement Clinker and Fly Ash

  • Samaptika Mohanty
  • Nagendra Roy
  • Suresh Prasad Singh
  • Parveen SihagEmail author
Original Paper


In this study, the potential of four popular artificial intelligence techniques random forest (RF), Gaussian process (GP), M5P tree and artificial neural network (ANN) are assessed for estimating the strength of stabilized dispersive soil with cement clinker and fly ash. GP, M5P and ANN models were providing a good estimate of performance, whereas the RF model outperforms them. For this study, a dataset containing 52 observations obtained from the laboratory experiments. Total data set (52 observations) has been segregated in two different groups. The larger group (36) was used for model development and the smaller group (16) was used for testing the models. Input dataset consists of dispersive soil (%), cement clinker (%), fly ash (%) and curing time (days), whereas unconfined compressive strength (UCS) of soil (MPa) was taken as a target. Sensitivity testing results conclude that the curing time is the most essential factor in estimating the strength of dispersive soil with cement clinker and fly ash for RF-based modelling. The results of this study also suggest that the combined mix of cement clinker and fly ash are used to increase the UCS of dispersive soil than an alone mix.


Random forest Gaussian process regression M5P tree Artificial neural network Unconfined compressive strength Dispersive soil 



  1. Angelaki A, Singh N, Singh V, Sihag P (2018) Estimation of models for cumulative infiltration of soil using machine learning methods. ISH J Hydraul Eng. Google Scholar
  2. Bell FG (1993) Engineering treatment of soils. Spon, London, p 346Google Scholar
  3. Besalatpour A, Hajabbasi MA, Ayoubi S, Afyuni M, Jalalian A, Schulin R (2013) Soil shear strength prediction using intelligent systems: artificial neural networks and an adaptive neuro-fuzzy inference system. Soil Sci Plant Nutr 58(2):149–160. CrossRefGoogle Scholar
  4. Bohlooli H, Nazari A, Khalaj G, Kaykha MM, Riahi S (2012) Experimental investigations and fuzzy logic modelling of compressive strength of geopolymers with seeded fly ash and rice husk bark ash. Compos Part B 43:1293–1301. CrossRefGoogle Scholar
  5. Breiman L (2001) Random For Mach Learn 45(1):5–32CrossRefGoogle Scholar
  6. Brown T, Brown M, Sorini S, Huntington G (1991) The use of coal fly ash for soil stabilization. University of Wyoming Research Corp., Laramie, WY (United States). Western Research Inst.
  7. Choquette M, Berube MA, Locat J (1987) Mineralogical and microtextural changes associated with lime stabilization of marine clays from eastern Canada. Appl Clay Sci 2(3):215–232. CrossRefGoogle Scholar
  8. Firoozi AA, Olgun CG, Firoozi AA, Baghini MS (2017a) Fundamentals of soil stabilization. Int J Geo-Eng 8(1):26. CrossRefGoogle Scholar
  9. Firoozi AA, Firoozi AA, Baghini MS (2017b) A review of physical and chemical clayey. J Civ Eng Urban 6(4):64–71Google Scholar
  10. Haghiabi AH, Azamathulla HM, Parsaie A (2017) Prediction of head loss on cascade weir using ANN and SVM. ISH J Hydraul Eng 23(1):102–110. CrossRefGoogle Scholar
  11. Haghiabi AH, Parsaie A, Ememgholizadeh S (2018) Prediction of discharge coefficient of triangular labyrinth weirs using adaptive neuro fuzzy inference system. Alexandria Eng J 57(3):1773–1782. CrossRefGoogle Scholar
  12. Haykin S (1999) Neural networks: a comprehensive foundation. Mc Millan, New JerseyGoogle Scholar
  13. Heinzen RT, Arulanandan K (1977) Factors influencing dispersive clays and methods of identification. ASTM Spec Tech Publ 623:202–217Google Scholar
  14. Holmgren GG, Flanagan CP (1977) Factors affecting spontaneous dispersion of soil materials as evidenced by the crumb test. ASTM Spec Tech Publ 623:219–239Google Scholar
  15. Indraratna B, Nutalaya P, Kuganenthira N (1991) Stabilization of a dispersive soil by blending with fly ash. Q J Eng Geol Hydrogeol 24(3):275–290. CrossRefGoogle Scholar
  16. IS: 2720-Part 7 (1980) Indian standard methods of test for soils: determination of water content-dry unit weight relation using light compaction. BIS, New DelhiGoogle Scholar
  17. IS: 2720-Part 16 (1987) Indian Standard Method of test for soils. Laboratory Determination of CBR. Bureau of Indian Standards, New DelhiGoogle Scholar
  18. Kalkan E, Akbulut S, Tortum A, Celik S (2009) Prediction of the unconfined compressive strength of compacted granular soils by using inference systems. Environ Geol 58(7):1429–1440. CrossRefGoogle Scholar
  19. Kolias S, Kasselouri-Rigopoulou V, Karahalios A (2005) Stabilisation of clayey soils with high calcium fly ash and cement. Cem Concr Compos 27(2):301–313. CrossRefGoogle Scholar
  20. Locat J, Bérubé MA, Choquette M (1990) Laboratory investigations on the lime stabilization of sensitive clays: shear strength development. Can Geotech J 27(3):294–304. CrossRefGoogle Scholar
  21. Macphee DE, Black CJ, Taylor AH (1993) Cements incorporating brown coal fly ash from the Latrobe Valley region of Victoria, Australia. Cem Concr Res 23(3):507–517. CrossRefGoogle Scholar
  22. Mehdipour V, Stevenson DS, Memarianfard M, Sihag P (2018) Comparing different methods for statistical modeling of particulate matter in Tehran, Iran. Air Qual Atmos Health 11(10):1155–1165. CrossRefGoogle Scholar
  23. Motamedi S, Shamshirband S, Petkovic D, Hashim R (2015) Application of adaptive neuro-fuzzy technique to predict the unconfined compressive strength of PFA-sand-cement mixture. Powder Technol 278:278–285. CrossRefGoogle Scholar
  24. Nain SS, Sihag P, Luthra S (2018) Performance evaluation of fuzzy-logic and BP-ANN methods for WEDM of aeronautics super alloy. MethodsX. Google Scholar
  25. Nazari A, Khalaj G (2012) Prediction compressive strength of lightweight geopolymers by ANFIS. Ceram Int 38:4501–4510. CrossRefGoogle Scholar
  26. Ogundipe OM (2013) An investigation into the use of lime-stabilized clay as subgrade material. Int J Sci Technol Res 2(10):82–86Google Scholar
  27. Parsaie A, Haghiabi AH (2017) Mathematical expression of discharge capacity of compound open channels using MARS technique. J Earth Syst Sci 126(2):20. CrossRefGoogle Scholar
  28. Parsaie A, Haghiabi AH, Saneie M, Torabi H (2016) Applications of soft computing techniques for prediction of energy dissipation on stepped spillways. Neural Comput Appl. Google Scholar
  29. Parsaie A, Haghiabi AH, Saneie M, Torabi H (2017) Predication of discharge coefficient of cylindrical weir-gate using adaptive neuro fuzzy inference systems (ANFIS). Front Struct Civil Eng 11(1):111–122. CrossRefGoogle Scholar
  30. Quinlan JR (1992) Learning with continuous classes. In: Adams A, Sterling L (eds) 5th Australian joint conference on artificial intelligence, vol 92, pp 343–348.
  31. Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. MIT Press, MassachusettsGoogle Scholar
  32. Sharma NK, Swain SK, Sahoo UC (2012) Stabilization of a clayey soil with fly ash and lime: a micro level investigation. Geotech Geol Eng 30(5):1197–1205. CrossRefGoogle Scholar
  33. Sherard JL, Decker RS eds (1977) Dispensive clays, related piping, and erosion in geotechmical projects, (Vol 623). ASTM InternationalGoogle Scholar
  34. Sihag P (2018) Prediction of unsaturated hydraulic conductivity using fuzzy logic and artificial neural network. Model Earth Syst Environ 4(1):189–198. CrossRefGoogle Scholar
  35. Sihag P, Tiwari NK, Ranjan S (2017a) Modelling of infiltration of sandy soil using gaussian process regression. Model Earth Syst Environ 3(3):1091–1100. CrossRefGoogle Scholar
  36. Sihag P, Tiwari NK, Ranjan S (2017b) Prediction of unsaturated hydraulic conductivity using adaptive neuro-fuzzy inference system (ANFIS). ISH J Hydraul Eng. Google Scholar
  37. Sihag P, Tiwari NK, Ranjan S (2018a) Support vector regression-based modeling of cumulative infiltration of sandy soil. ISH J Hydraul Eng. Google Scholar
  38. Sihag P, Singh B, Gautam S, Debnath S (2018b) Evaluation of the impact of fly ash on infiltration characteristics using different soft computing techniques. Appl Water Sci 8(6):187. CrossRefGoogle Scholar
  39. Sihag P, Singh B, Sepah Vand A, Mehdipour V (2018c) Modeling the infiltration process with soft computing techniques. ISH J Hydraul Eng. Google Scholar
  40. Sihag P, Tiwari NK, Ranjan S (2018d) Prediction of cumulative infiltration of sandy soil using random forest approach. J Appl Water Eng Res. Google Scholar
  41. Sihag P, Jain P, Kumar M (2018e) Modelling of impact of water quality on recharging rate of storm water filter system using various kernel function based regression. Model Earth Syst Environ 4(1):61–68. CrossRefGoogle Scholar
  42. Singh B, Sihag P, Singh K, Kumar S (2018) Estimation of trapping efficiency of vortex tube silt ejector. Int J River Basin Manag. Google Scholar
  43. Standard, A.S.T.M., D4221-99, 1999 (2005) Standard test method for dispersive characteristics of clay soil by double hydrometer, ASTM International, West Conshohocken, PAGoogle Scholar
  44. Tiwari NK, Sihag P (2018) Prediction of oxygen transfer at modified Parshall flumes using regression models. ISH J Hydraul Eng. Google Scholar
  45. Tiwari NK, Sihag P, Kumar S, Ranjan S (2018) Prediction of trapping efficiency of vortex tube ejector. ISH J Hydraul Eng. Google Scholar
  46. Umesh T, Dinesh S, Sivapullaiah PV (2011) Characterization of dispersive soils. Mater Sci Appl 2:629–633. Google Scholar
  47. Umesha TS, Dinesh SV, Sivapullaiah PV (2009) Control of dispersivity of soil using lime and cement. Int J Geol 3(1):8–16Google Scholar
  48. Vand AS, Sihag P, Singh B, Zand M (2018) Comparative evaluation of infiltration models. KSCE J Civil Eng 22(10):4173–4184. CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Civil Engineering DepartmentNational Institute of Technology, RourkelaRourkelaIndia
  2. 2.Civil EngineeringNIT KurukshetraKurukshetraIndia

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