Advertisement

Prediction of Coefficient of Consolidation Using Multi-Gene Genetic Programming

  • Shivpreet Sharma
  • Anil Kumar Mishra
  • Bimlesh KumarEmail author
Original Article
  • 7 Downloads

Abstract

The accurate determination of the coefficient of consolidation is significantly important for analyzing settlement of clayey deposits. The coefficient of consolidation is generally determined from the consolidation test results by performing a curve-fitting method on laboratory test data. However, the consolidation test on clayey sample is quite time-consuming and expensive. This paper proposes a simple, quick, versatile, and reliable procedure to estimate the coefficient of consolidation. In this paper, Multi-Gene Genetic Programming (MGGP) model is suggested for prediction of the coefficient of consolidation from basic soil properties such as liquid limit, cation exchange capacity (CEC), pressure, void ratio, montmorillonite content, activity, exchangeable sodium percentage (ESP) and clay content.

Keywords

Coefficient of consolidation Clay Multi-Gene Genetic Programming 

Notes

References

  1. ASTM (1984) Standard test method for Methylene Blue Index of clay C 837-99. American Society for Testing and Materials, PhiladelphiaGoogle Scholar
  2. ASTM (1996) Standard test method for one-dimensional consolidation properties of soils, D 2435. American Society for Testing and Materials, PhiladelphiaGoogle Scholar
  3. ASTM (2001) Standard test method for Swell Index of clay mineral component of geosynthetic clay liners. D 5890. American Society for Testing and Materials, PhiladelphiaGoogle Scholar
  4. ASTM (2002a) Standard test method for particle size analysis of soils D 422-63. American Society for Testing and Materials, PhiladelphiaGoogle Scholar
  5. ASTM (2002b) Standard test method for particle-size analysis of soils, D 422-63. American Society for Testing and Materials, PhiladelphiaGoogle Scholar
  6. Bhattarai S, Zhou Y, Zhao C, Zhou H (2018) Predicting temperature drop rate of mass concrete during an initial cooling period using genetic programming. In: IOP conference series, materials science and engineering, vol 311, pp 012018Google Scholar
  7. Chapman HD (1965) Cation exchange capacity. In: Methods of Soil Analysis, Part 2 Chemical and Microbiological Properties, 2nd edn. Soil Science Society of America, Madison, Wisconsin, USA, pp 891–895Google Scholar
  8. Dutta J, Mishra AK (2016) Consolidation behaviour of bentonites in the presence of salt solutions. Appl Clay Sci 120:61–69CrossRefGoogle Scholar
  9. Javadi AA, Rezania M, Nezhad MM (2006) Evaluation of liquefaction induced lateral displacements using genetic programming. Comput Geotech 33(4–5):222–233CrossRefGoogle Scholar
  10. Mishra AK, Ohtsubo M, Li L, Higashi T (2011) Controlling factors of the swelling of various bentonites and their correlations with the hydraulic conductivity of soil–bentonite mixtures. Appl Clay Sci 52:78–84CrossRefGoogle Scholar
  11. Pratt PF (1965) Sodium, Methods of Soil Analysis, Part 2 Chemical and Microbiological Properties, 2nd edn. Soil Science Society of America, Madison, Wisconsin, USA, pp 1031–1034Google Scholar
  12. Venkata Rao M, Rama Mohan Rao P (2016) genetic programming and multivariate adaptive regression splines for prediction of bridge risks and comparison of performances. Int J Optim Civil Eng 6(4):547–555Google Scholar

Copyright information

© Indian National Academy of Engineering 2019

Authors and Affiliations

  1. 1.Civil EngineeringIndian Institute of Technology PatnaPatnaIndia
  2. 2.Civil EngineeringIndian Institute of Technology GuwahatiGuwahatiIndia

Personalised recommendations