Skip to main content

Advertisement

Log in

Estimation of Pore Water Pressure of Soil Using Genetic Programming

  • Original paper
  • Published:
Geotechnical and Geological Engineering Aims and scope Submit manuscript

Abstract

Soil–water characteristic curve (SWCC) is one of the input components required for conducting the transient seepage analysis in unsaturated soil for estimating pore water pressure (PWP). SWCC is usually defined by saturated volumetric water content (θs), residual water content (RWC) and air entry value (AEV). Mathematical model of PWP could be useful to unearth the important SWCC components and the physics behind it. Based on authors’ knowledge, rarely any mathematical models describing the relationship between PWP and SWCC components are found. In the present work, an evolutionary approach, namely, multi-gene genetic programming (MGGP) has been applied to formulate the relationship between the PWP profile along soil depth and input variables for SWCC (θs, RWC and AEV) for a given duration of ponding. The PWP predicted using the MGGP model has been compared with those generated using finite element simulations. The results indicate that the MGGP model is able to extrapolate the PWP satisfactory along the soil depth for a given set of boundary conditions. Based on the given AEV and saturated water content, the PWP along the depth can be determined from the newly developed MGGP model, which will be useful for design and analysis of slopes and landfill covers.

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

Access this article

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
Fig. 9

Similar content being viewed by others

References

  • Biddle PG (1998) Tree root damage to buildings. Volume 1: causes, diagnosis and remedy. Volume 2: patterns of soil drying in proximity to trees on clay soils. Willowmead Publishing Ltd, Wantage

  • Blight GE (2005) Desiccation of a clay by grass, bushes and trees. Geotech Geol Eng 23(6):697–720

    Article  Google Scholar 

  • Garg A, Tai K (2011) A hybrid genetic programming-artificial neural network approach for modeling of vibratory finishing process. In: International proceedings of computer science and information technology (ICIIC 2011-International Conference on Information and Intelligent Computing), vol 18, pp 14–19

  • Garg A, Tai K (2012a) Comparison of regression analysis, Artificial Neural Network and genetic programming in Handling the multicollinearity problem. In: Proceedings of 2012 international conference on modelling, identification and control (ICMIC2012), Wuhan, China, 24–26 June 2012. IEEE

  • Garg A, Tai K (2012b) Review of genetic programming in modeling of machining processes. In: Proceedings of 2012 international conference on modelling, identification and control (ICMIC2012), Wuhan, China, 24–26 June 2012. IEEE

  • Garg A, Tai K (2013a) Comparison of statistical and machine learning methods in modelling of data with multicollinearity. Int J Model Identif Control 18(4):295–312

    Article  Google Scholar 

  • Garg A, Tai K (2013b) Modelling of FDM process using genetic programming with classifiers for model selection. In: Proceedings of 43rd international conference on computers and industrial engineering (CIE 43rd), Hong Kong, pp.123-1-10

  • Garg A, Tai K (2013c) Selection of a robust experimental design for the effective modeling of the nonlinear systems using genetic programming. In: Proceedings of 2013 IEEE symposium series on computational intelligence and data mining (CIDM), Singapore, 16–19 April 2013, pp 293–298

  • Garg A, Bhalerao Y, Tai K (2013a) Review of empirical modeling techniques for modeling of turning process. Int J Model Identif Control 20:121–129

    Article  Google Scholar 

  • Garg A, Rachmawati L, Tai K (2013b) Classification-driven model selection approach of genetic programming in modelling of turning process. Int J Adv Manuf Tech 69:1137–1151

    Article  Google Scholar 

  • Garg A, Garg A, Tai K (2013c) A multi-gene genetic programming model for estimating stress dependent soil water retention curves, Computational Geosciences (in press) doi:10.1007/s10596-013-9381-z

  • Garg A, Sriram S, Tai K (2013d) Empirical analysis of model selection criteria for genetic programming in modeling of time series system. In: Proceedings of 2013 IEEe conference on computational intelligence for financial engineering and economics (CIFEr), Singapore, 16–19 April 2013, pp 84–88

  • Garg A, Tai K, Lee CH, Savalani MM (2013e) A hybrid M5-genetic programming approach for ensuring greater trustworthiness of prediction ability in modelling of fdm process. J Intell Manuf (in press). doi:10.1007/s10845-013-0734-1

  • Garg A, Savalani MM, Tai K (2014a) State-of-the-art in empirical modelling of rapid prototyping processes. Rapid Prototyp J 20(2):164–178

    Google Scholar 

  • Garg A, Vijayaraghavan V, Mahapatra SS, Tai K, Wong CH (2014b) Performance evaluation of microbial fuel cell by artificial intelligence methods. Expert Syst Appl 41:1389–1399

    Article  Google Scholar 

  • Geo-Slope, Seep/W (2007) User’s guide. Geo-Slope International Limited, Calgary, Alberta

    Google Scholar 

  • Hinchliffe M, Hiden H, Mckay B, Willis M, Tham M, Barton G (1996) Modelling chemical process systems using a multi-gene genetic programming algorithm. Late Breaking Papers at the Genetic Programming, pp 28–31

  • Koza JR (1996) On the programming of computers by means of natural selection. Mit Press, USA

    Google Scholar 

  • Malaya C, Sreedeep S (2010) A study on the influence of measurement procedures on suction-water content relationship of a sandy soil. Geotech Test J ASTM 38(6):1

    Google Scholar 

  • Richards LA (1931) Capillary conduction of liquids through porous mediums. Physics 1:318–333

    Article  Google Scholar 

  • Searson DP, Leahy DE, Willis MJ (2010) Gptips: an open source genetic programming toolbox for multigene symbolic regression. Int Multiconf Eng Comp Sci 2010:77–80

    Google Scholar 

  • Shah PH, Sreedeep S, Singh DN (2006) Evaluation of methodologies used for establishing soil-water characteristic curve. J ASTM Int 3:1–11

    Google Scholar 

  • Sreedeep S (2006) Modeling contaminant transport in unsaturated soils. Doctoral dissertation, Ph. D. thesis submitted to the Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, India)

  • Sreedeep S, Singh DN (2010) A critical review of the methodologies employed for soil suction measurement. Int J Geomech ASCE. Special Issue: Environmental Geotechnology: Contemporary Issues, 99–104

  • Sreedeep S, Singh DN (2011) Critical review of the methodologies employed for soil suction measurement. Int J Geomech ASCE 11(2):99–104

    Article  Google Scholar 

  • Van Genuchten MT (1980) A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci Soc of Am J 44(5):892–898

    Article  Google Scholar 

  • Yildiz AR (2009a) A novel hybrid immune algorithm for global optimization in design and manufacturing. Robot Comp Integr Manuf 25(2):261–270

    Article  Google Scholar 

  • Yildiz AR (2009b) An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry. J Mater Process Technol 209(6):2773–2780

    Article  Google Scholar 

  • Yildiz AR (2012a) A comparative study of population-based optimization algorithms for turning operations. Inf Sci 210:81–88

    Article  Google Scholar 

  • Yildiz AR (2012b) Comparison of evolutionary based optimization algorithms for structural design optimization. Engineering applications of artificial intelligence 26(1):327–333

    Article  Google Scholar 

  • Yildiz AR (2013a) A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations. Appl Soft Comput 13(3):1561–1566

    Article  Google Scholar 

  • Yildiz AR (2013b) A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing. Appl Soft Comput 13(5):2906–2912

    Article  Google Scholar 

  • Yildiz AR (2013c) Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach. Inf Sci 220:399–407

    Article  Google Scholar 

  • Yildiz AR (2013d) Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations. Appl Soft Comput 13(3):1433–1439

    Article  Google Scholar 

  • Yildiz AR (2013e) Cuckoo search algorithm for the selection of optimal machining parameters in milling operations. Int J Adv Manuf Technol 64(1–4):55–61

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ankit Garg.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Garg, A., Garg, A., Tai, K. et al. Estimation of Pore Water Pressure of Soil Using Genetic Programming. Geotech Geol Eng 32, 765–772 (2014). https://doi.org/10.1007/s10706-014-9755-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10706-014-9755-6

Keywords

Navigation