Skip to main content
SpringerLink
Log in

Evaluating unconfined compressive strength of cohesive soils stabilized with geopolymer: a computational intelligence approach

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

Soil stabilization using geopolymers is a new technique for improvement of weak cohesive soils. Evaluating behavior of improved soils requires an initial estimation of strength parameters. In this study, extensive experimental results on geopolymer-stabilized soil specimens were collected and analyzed. A model was then developed using group method of data handling (GMDH) and employing particle-swarm optimization algorithm to estimate the unconfined compressive strength (UCS) of stabilized cohesive soils using geopolymers. Type of additives and their compositions as well as soil characteristics were taken as the influential parameters on the UCS of soil specimens. Subsequently, sensitivity analysis was carried out to verify the performance of the proposed UCS model. Finally, the developed GMDH-based model was compared with artificial neural network model to predict unconfined compressive strength of stabilized soils. The results clearly illustrate the reasonable accuracy of the developed computational Intelligence-based model for estimating the unconfined compressive strength of geopolymer-stabilized cohesive soils.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Javdanian H, Haddad A, Mehrzad B (2012) Interference effect on bearing capacity of multiple shallow foundations supported by geosynthetic-reinforced sand. In: 5th Asian Regional Conference on Geosynthetics, 13 December, Bangkok, Thailand

  2. Marandi SM, Javdanian H (2012) Laboratory studies on bearing capacity of strip interfering shallow foundations supported by geogrid-reinforced sand. Adv Mater Res 472:1856–1869

    Article  Google Scholar 

  3. Latifi N, Horpibulsuk S, Meehan CL, Abd Majid MZ, Tahir MM, Mohamad ET (2016) Improvement of problematic soils with biopolymer—an environmentally friendly soil stabilizer. J Mater Civ Eng 29(2):04016204

    Article  Google Scholar 

  4. Javdanian H, Haddad A, Mehrzad B (2012) Experimental and numerical investigation of the bearing capacity of adjacent footings on reinforced soil. Electron J Geotech Eng 17(R):2597–2617

    Google Scholar 

  5. Javdanian H, Hamedi A, Ayoubi H (2012) Interference effect on bearing capacity of shallow foundations constructed on geosynthetic-reinforced soil. In: 9th International Congress on Civil Engineering, 8 May, Isfahan

  6. Javdanian H (2017) On the behaviour of shallow foundations constructed on reinforced soil slope-a numerical analysis. Int J Geotech Eng. https://doi.org/10.1080/19386362.2017.1416971.

    Google Scholar 

  7. Sukmak P, De Silva P, Horpibulsuk S, Chindaprasirt P (2014) Sulfate resistance of clay-portland cement and clay high-calcium fly ash geopolymer. J Mater Civ Eng 27(5):04014158

    Article  Google Scholar 

  8. Arulrajah A, Kua TA, Phetchuay C, Horpibulsuk S, Mahghoolpilehrood F, Disfani MM (2015) Spent coffee grounds–fly ash geopolymer used as an embankment structural fill material. J Mater Civ Eng 28(5):04015197

    Article  Google Scholar 

  9. Javdanian H (2017) The effect of geopolymerization on the unconfined compressive strength of stabilized fine-grained soils. Int J Eng Trans B Appl 30(11):1673–1680

    Google Scholar 

  10. Mozumder RA, Laskar AI (2015) Prediction of unconfined compressive strength of geopolymer stabilized clayey soil using artificial neural network. Comput Geotech 69:291–300

    Article  Google Scholar 

  11. Santoni RL, Tingle JS, Webster SL (2003) Stabilization of silty sand with non-traditional additives, transportation research record 1787. TRB, National Research Council, Washington, DC, pp 33–41

    Google Scholar 

  12. Verdolotti L, Iannance S, Lavorgna M, Lumanaa R (2008) Geopolymerization reaction to consolidate incoherent pozzolanic soil. J Mater Sci 43:865–873

    Article  Google Scholar 

  13. Cristelo N, Glendinning S, Farnandes L, Pinto AT (2011) Effect of calcium content on soil stabilization with alkaline activation. Construct Build Mater 29:167–174

    Article  Google Scholar 

  14. Cristelo N, Glendinning S, Pinto AT (2012) Deep soft soil improvement by alkaline activation. Proc Inst Civ Eng Ground Improve 164(2):73–82

    Article  Google Scholar 

  15. Phetchuay C, Horpibulsuk S, Suksiripattanapong C, Chinkulkijniwat A, Arulrajah A, Disfani MM (2014) Calcium carbide residue: alkaline activator for clay–fly ash geopolymer. Construct Build Mater 69:285–294

    Article  Google Scholar 

  16. Zhang M, Guo H, El-Korchi T, Zhang G, Tao M (2013) Experimental feasibility study of geopolymer as the next-generation soil stabilizer. Construct Build Mater 47:1468–1478

    Article  Google Scholar 

  17. Yaolin Y, Cheng L, Songyu L (2014) Alkali-activated ground-granulated blast furnace slag for stabilization of marine soft clay. J Mater Civ Eng. https://doi.org/10.1061/(ASCE)MT.1943-5533.0001100:04014146

    Google Scholar 

  18. Jafarian Y, Haddad A, Javdanian H (2014) Predictive model for normalized shear modulus of cohesive soils. Acta Geodyn Geomater 11(1):89–100

    Google Scholar 

  19. Javdanian H, Haddad A, Jafarian A (2015) Evaluation of dynamic behavior of fine-grained soils using group method of data handling. Transp Infrastruct Eng 1(3):77–92

    Google Scholar 

  20. Javdanian H, Jafarian Y, Haddad A (2015) Predicting damping ratio of fine grained soils using soft computing methodology. Arab J Geosci 8(6):3959–3969

    Article  Google Scholar 

  21. Javdanian H (2017) Assessment of shear stiffness ratio of cohesionless soils using neural modeling. Model Earth Syst Environ 3(3):1045–1053

    Article  Google Scholar 

  22. Sharma LK, Singh R, Umrao RK, Sharma KM, Singh TN (2017) Evaluating the modulus of elasticity of soil using soft computing system. Eng Comput 33(3):497–507

    Article  Google Scholar 

  23. Alavi AH, Gandomi AH, Sahab MG, Gandomi M (2010) Multi expression programming: a new approach to formulation of soil classification. Eng Comput 26(2):111–118

    Article  Google Scholar 

  24. Khandelwal M, Kumar DL, Yellishetty M (2011) Application of soft computing to predict blast-induced ground vibration. Eng Comput 27(2):117–125

    Article  Google Scholar 

  25. Hasanipanah M, Naderi R, Kashir J, Noorani SA, Qaleh AZA (2017) Prediction of blast-produced ground vibration using particle swarm optimization. Eng Comput 33(2):173–179

    Article  Google Scholar 

  26. Baziar MH, Jafarian Y (2007) Assessment of liquefaction triggering using strain energy concept and ANN model: capacity energy. Soil Dyn Earthq Eng 27(12):1056–1072

    Article  Google Scholar 

  27. Javdanian H (2017) Evaluation of soil liquefaction potential using energy approach: experimental and statistical investigation. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-017-1201-6

    Google Scholar 

  28. Javdanian H, Heidari A, Kamgar R (2017) Energy-based estimation of soil liquefaction potential using GMDH algorithm. Iran J Sci Technol Trans Civ Eng 41(3):283–295

    Article  Google Scholar 

  29. Pradhan B, Lee S (2010) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci 60(5):1037–1054

    Article  Google Scholar 

  30. Pradhan B, Lee S (2010) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw 25(6):747–759

    Article  Google Scholar 

  31. Lee S, Ryu JH, Min K, Won JS (2003) Landslide susceptibility analysis using GIS and artificial neural network. Earth Surf Proc Land 28(12):1361–1376

    Article  Google Scholar 

  32. Lee S, Ryu JH, Won JS, Park HJ (2004) Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng Geol 71(3):289–302

    Article  Google Scholar 

  33. Lee S, Ryu JH, Kim IS (2007) Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin. Korea Landslides 4(4):327–338

    Article  Google Scholar 

  34. Lee S, Hong SM, Jung HS (2017) A support vector machine for landslide susceptibility mapping in Gangwon Province, Korea. Sustainability 9(1):48

    Article  Google Scholar 

  35. Gordan B, Armaghani DJ, Hajihassani M, Monjezi M (2016) Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng Comput 32(1):85–97

    Article  Google Scholar 

  36. Armaghani DJ, Mohamad ET, Hajihassani M, Yagiz S, Motaghedi H (2016) Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances. Eng Comput 32(2):189–206

    Article  Google Scholar 

  37. Mousavi SM, Alavi AH, Mollahasani A, Gandomi AH, Esmaeili MA (2013) Formulation of soil angle of shearing resistance using a hybrid GP and OLS method. Eng Comput 29(1):37–53

    Article  Google Scholar 

  38. Soleimani S, Jiao P, Rajaei S, Forsati R (2017) A new approach for prediction of collapse settlement of sandy gravel soils. Eng Comput. https://doi.org/10.1007/s00366-017-0517-y

    Google Scholar 

  39. Sharma LK, Singh TN (2017) Regression-based models for the prediction of unconfined compressive strength of artificially structured soil. Eng Comput. https://doi.org/10.1007/s00366-017-0528-8

    Google Scholar 

  40. IS: 2720 (1991). Determination of unconfined compressive strength. (Part 10) Second Revision

  41. Masters T (1993) Practical neural network recipes in C++. Academic press, San Diego

    MATH  Google Scholar 

  42. Najafzadeh M, Azamathulla HM (2013) Neuro-fuzzy GMDH systems to predict the scour pile groups due to waves. J Comput Civil Eng. 10.1061/(ASCE)CP.1943-5487.0000376

  43. Hwang HS (2006) Fuzzy GMDH-type neural network model and its application to forecasting of mobile communication. Comp Ind Eng 50:450–457

    Article  MathSciNet  Google Scholar 

  44. Najafzadeh M, Lim SY (2014) Application of improved neuro-fuzzy GMDH to predict scour downstream of sluice gates. Earth Sci Inf 8(1):187–196

    Article  Google Scholar 

  45. Najafzadeh M, Barani GA, Azamathulla HM (2013) GMDH to prediction of scour depth around vertical piers in cohesive soils. Appl Ocean Res 40:35–41

    Article  Google Scholar 

  46. Najafzadeh M, Barani GA, Hessami Kermani MR (2013) GMDH network based back propagation algorithm to predict abutment scour in cohesive soils. Ocean Eng 59:100–106

    Article  Google Scholar 

  47. Najafzadeh M (2014) Neuro-Fuzzy GMDH system based particle swarm optimization for prediction of scour depth at downstream of grade control structures. Eng Sci Technol Int J 18(1):42–45

    Article  Google Scholar 

  48. Schalkoff RJ (1997) Artificial neural networks. McGraw-Hill Higher Education New York

    MATH  Google Scholar 

Download references

Acknowledgements

This research was supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM) funded by the Minister of Science, ICT and Future Planning of Korea. The support of the research deputy of Shahrekord University (grant number 95GRN1M39422) is also acknowledged.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Shahrekord University, Shahrekord, Iran

    Hamed Javdanian

  2. Geological Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon, 34132, South Korea

    Saro Lee

  3. Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon, 34113, South Korea

    Saro Lee

Authors
  1. Hamed Javdanian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saro Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hamed Javdanian.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Javdanian, H., Lee, S. Evaluating unconfined compressive strength of cohesive soils stabilized with geopolymer: a computational intelligence approach. Engineering with Computers 35, 191–199 (2019). https://doi.org/10.1007/s00366-018-0592-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-018-0592-8

Keywords

Search

Navigation