Evaluation of the accuracy of commonly used empirical correlations in predicting the compression index of Iraqi fine-grained soils


Several empirical correlations were proposed in the literature to predict the compression index of fine-grained soils using either of the soil properties such as the liquid limit, plasticity index, initial void ratio and natural moisture content. However, the accuracy of these correlations to predict the compression index of Iraqi fine-grained soils has not been investigated before. Hence, this research has been carried out to evaluate the effectiveness of the available correlations in predicting the compression index of fine-grained soils collected from different regions in Iraq. A methodology, based on statistical analysis, has been employed to analyse the differences between predicted and measured compression index values. In addition, a database of properties of fine-grained soils has been collected from the literature to enable the statistical assessment. The results showed a significant scatter in the prediction among the examined correlations, where most of the correlations performed poorly in the predictions. However, it has been shown that the correlations proposed by Rendon-Herrero (J Geotech Eng 109(5):755–761, 1983) and Al-Khafaji and Andersland (J Geotech Eng 118(1):148–153, 1992) provided a good estimate of the compression index compared with other empirical correlations.

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  1. 1.

    Abdrabbo FM, Mahmoud MA (1990) Correlations between index tests and compressibility of Egyptian clays. Soils Found 30(2):128–132

    Google Scholar 

  2. 2.

    Ahmed MD, Adkel AM (2017) Stabilization of clay soil using tyre ash. J Eng 23(6):34–51

    Google Scholar 

  3. 3.

    Al-Ani MM, Fattah MY, Al-Lamy MT (2009) Artificial neural networks analysis of treatment process of gypseous soils. Eng Technol J 27(9):1811–1832

    Google Scholar 

  4. 4.

    Al-Furaiji MAA (2016) Geotechnical study of some of the physical, chemical and engineering properties of Abi sinking soil in Babil Governorate. J Univ Babylon Eng Sci 24(2):278–300 (In Arabic)

    Google Scholar 

  5. 5.

    Al-Khafaji AWN, Andersland OB (1992) Equations for compression index approximation. J Geotech Eng 118(1):148–153

    Google Scholar 

  6. 6.

    Alkroosh I, Nikraz H (2011) Correlation of pile axial capacity and CPT data using gene expression programming. Geotech Geol Eng 29(5):725–748

    Google Scholar 

  7. 7.

    Alkroosh I, Nikraz H (2012) Predicting axial capacity of driven piles in cohesive soils using intelligent computing. Eng Appl Artif Intell 25(3):618–627

    Google Scholar 

  8. 8.

    Alkroosh I, Nikraz H (2014) Predicting pile dynamic capacity via application of an evolutionary algorithm. Soils Found 54(2):233–242

    Google Scholar 

  9. 9.

    Al-Taie AJ (2015) Profiles and geotechnical properties for some Basra soils. Al-Khwarizmi Eng J 11(2):74–85

    Google Scholar 

  10. 10.

    Al-Taie AJ, Al-Bayati AF, Taki ZNM (2017) Compression index and compression ratio prediction by artificial neural networks. J Eng 23(12):96–106

    Google Scholar 

  11. 11.

    Alzabeebee S, Chapman D (2020) Evolutionary computing to determine the skin friction capacity of piles embedded in clay and evaluation of the available analytical methods. Transp Geotech 24:100372. https://doi.org/10.1016/j.trgeo.2020.100372

    Google Scholar 

  12. 12.

    Alzabeebee S (2019) Seismic response and design of buried concrete pipes subjected to soil loads. Tunn Undergr Space Technol 93:103084

    Google Scholar 

  13. 13.

    Alzabeebee S (2020) Dynamic response and design of a skirted foundation subjected to vertical vibration. Geomech Eng 20(4):345–358

    Google Scholar 

  14. 14.

    Alzabeebee S, Chapman DN, Faramarzi A (2018) Development of a novel model to estimate bedding factors to ensure the economic and robust design of rigid pipes under soil loads. Tunn Undergr Space Technol 71:567–578

    Google Scholar 

  15. 15.

    Alzabeebee S, Chapman DN, Faramarzi A (2019) Economical design of buried concrete pipes subjected to UK standard traffic loading. Proc Inst Civ Eng Struct Build 172(2):141–156

    Google Scholar 

  16. 16.

    ASTM D2216-10 (2010) Standard test methods for laboratory determination of water (moisture) content of soil and rock by mass. ASTM International, West Conshohocken, PA. www.astm.org

  17. 17.

    ASTM D2435/D2435M-11 (2011) Standard test methods for one-dimensional consolidation properties of soils using incremental loading. ASTM International, West Conshohocken, PA. www.astm.org

  18. 18.

    ASTM D2487-17e1 (2017) Standard practice for classification of soils for engineering purposes (unified soil classification system). ASTM International, West Conshohocken, PA. www.astm.org

  19. 19.

    ASTM D4186/D4186M-12e1 (2012) Standard test method for one-dimensional consolidation properties of saturated cohesive soils using controlled-strain loading. ASTM International, West Conshohocken, PA. www.astm.org

  20. 20.

    ASTM D4318-10 (2010) Standard test methods for liquid limit, plastic limit, and plasticity index of soils. ASTM International, West Conshohocken, PA. www.astm.org

  21. 21.

    ASTM D854-10 (2010) Standard test methods for specific gravity of soil solids by water pycnometer. ASTM International, West Conshohocken, PA. www.astm.org

  22. 22.

    Azzouz AS, Krizek RJ, Corotis RB (1976) Regression analysis of soil compressibility. Soils Found 16(2):19–29

    Google Scholar 

  23. 23.

    Bowles JE (1979) Physical and geotechnical properties of soils. McGraw-Hill Book Company, New York

    Google Scholar 

  24. 24.

    Briaud JL, Tucker LM (1988) Measured and predicted axial response of 98 piles. J Geotech Eng 114(9):984–1001

    Google Scholar 

  25. 25.

    Chen W, Sarir P, Bui XN, Nguyen H, Tahir MM, Armaghani DJ (2019) Neuro-genetic, neuro-imperialism and genetic programing models in predicting ultimate bearing capacity of pile. Eng Comput. https://doi.org/10.1007/s00366-019-00752-x

    Google Scholar 

  26. 26.

    Cozzolino VM (1961) Statistical forecasting of compression index. In: Proceedings of the 5th international conference on soil mechanics and foundation engineering Paris, vol 1, pp 51–53

  27. 27.

    Das BM, Sobhan K (2010) Principles of geotechnical engineering, 8th edn. Cengage Learning, Boston

    Google Scholar 

  28. 28.

    Do J, Heo O, Yoon YW, Chang I (2018) Geotechnical design parameter evaluation using the alluvial plain characteristics in southeastern Iraq. Arab J Geosci 11(20):647

    Google Scholar 

  29. 29.

    Fattah MY, Al-Neami MA, Al-Suhaily AS (2017) Estimation of bearing capacity of floating group of stone columns. Eng Sci Technol Int J 20(3):1166–1172

    Google Scholar 

  30. 30.

    Fattah MY, Al-Soudani WH, Omar M (2016) Estimation of bearing capacity of open-ended model piles in sand. Arab J Geosci 9(3):242

    Google Scholar 

  31. 31.

    Fattah MY, Baqir HH, Al-Rawi OF (2006) Field and laboratory evaluation of a soft clay southern Iraq. In: Proceeding of the 4th Jordanian civil engineering conference, pp 28–30

  32. 32.

    Fattah MY, Shlash KT, Mohammed HA (2014) Bearing capacity of rectangular footing on sandy soil bounded by a wall. Arab J Sci Eng 39(11):7621–7633

    Google Scholar 

  33. 33.

    Hough BK (1957) Basic soils engineering. The Ronald Press Company, Minneapolis

    Google Scholar 

  34. 34.

    https://en.wikipedia.org/wiki/Governorates_of_Iraq. Accessed 14 May 2020

  35. 35.

    Huang CF, Li Q, Wu SC, Liu Y, Li JY (2019) Assessment of empirical equations of the compression index of muddy clay: sensitivity to geographic locality. Arab J Geosci 12(4):122. https://doi.org/10.1007/s12517-019-4276-5

    Google Scholar 

  36. 36.

    Kassim KA, Rashid ASA, Kueh ABH, Yah CS, Siang LC, Noor NM, Moayedi H (2015) Development of rapid consolidation equipment for cohesive soil. Geotech Geol Eng 33(1):167–174

    Google Scholar 

  37. 37.

    Koppula SD (1981) Statistical estimation of compression index. Geotech Test J 4(2):68–73

    Google Scholar 

  38. 38.

    Kurnaz TF, Kaya Y (2018) The comparison of the performance of ELM, BRNN, and SVM methods for the prediction of compression index of clays. Arab J Geosci 11(24):770

    Google Scholar 

  39. 39.

    Mekkiyah HM (2016) Improving shear strength of soft clay by using torn belts chips. Al-Khwarizmi Eng J 12(1):117–129

    Google Scholar 

  40. 40.

    Nagaraj TS, Murthy BS (1985) Prediction of the preconsolidation pressure and recompression index of soils. Geotech Test J 8(4):199–202

    Google Scholar 

  41. 41.

    Nishida Y (1956) A brief note on compression index of soil. J Soil Mech Found Div 82(3):1–14

    Google Scholar 

  42. 42.

    Onyejekwe S, Kang X, Ge L (2015) Assessment of empirical equations for the compression index of fine-grained soils in Missouri. Bull Eng Geol Environ 74(3):705–716

    Google Scholar 

  43. 43.

    Ozer M, Isik NS, Orhan M (2008) Statistical and neural network assessment of the compression index of clay-bearing soils. Bull Eng Geol Environ 67(4):537–545

    Google Scholar 

  44. 44.

    Rashed KA, Salih NB, Abdalla TA (2017) Correlation of consistency and compressibility properties of soils in Sulaimani city. Sulaimani J Eng Sci 4(5):86–94

    Google Scholar 

  45. 45.

    Rendon-Herrero O (1983) Closure to “universal compression index equation” by Oswald Rendon-Herrero (November 1980). J Geotech Eng 109(5):755–761

    Google Scholar 

  46. 46.

    Shaik S, Krishna KSR, Abbas M, Ahmed M, Mavaluru D (2019) Applying several soft computing techniques for prediction of bearing capacity of driven piles. Eng Comput 35(4):1463–1474

    Google Scholar 

  47. 47.

    Skempton AW, Jones OT (1944) Notes on the compressibility of clays. Q J Geol Soc 100(1–4):119–135. https://doi.org/10.1144/GSL.JGS.1944.100.01-04.08

    Google Scholar 

  48. 48.

    Sowers GB (1970) Introductory soil mechanics and foundations, 3rd edn. The Macmillan Company, New York

    Google Scholar 

  49. 49.

    Sridharan A, Nagaraj HB (2000) Compressibility behaviour of remoulded, fine-grained soils and correlation with index properties. Can Geotech J 37(3):712–722

    Google Scholar 

  50. 50.

    Terzaghi K, Peck RB (1967) Soil mechanics in engineering practice, 2nd edn. Wiley, New York

    Google Scholar 

  51. 51.

    Tsuchida T (1991) A new concept of e * log p relationship for clays. In: Proceedings of the 9th Asian regional conference on soil mechanics and foundation engineering, Bangkok, Thailand, pp 87–90

  52. 52.

    Vinod P, Bindu J (2010) Compression index of highly plastic clays—an empirical correlation. Indian Geotech J 40(3):174–180

    Google Scholar 

  53. 53.

    Wroth CP, Wood DM (1978) The correlation of index properties with some basic engineering properties of soils. Can Geotech J 15(2):137–145

    Google Scholar 

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Correspondence to Saif Alzabeebee.

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Alkroosh, I., Alzabeebee, S. & Al-Taie, A.J. Evaluation of the accuracy of commonly used empirical correlations in predicting the compression index of Iraqi fine-grained soils. Innov. Infrastruct. Solut. 5, 68 (2020). https://doi.org/10.1007/s41062-020-00321-y

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  • Compression index
  • Iraqi fine-grained soils
  • Statistical analysis
  • Consolidation
  • Empirical correlation