Geomechanics and Engineering

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Estimation of ultimate bearing capacity of shallow foundations resting on cohesionless soils using a new hybrid M5'-GP model

  • Khorrami, Rouhollah (Department of Civil Engineering, Faculty of Engineering, Shahed University) ;
  • Derakhshani, Ali (Department of Civil Engineering, Faculty of Engineering, Shahed University)
  • Received : 2018.02.25
  • Accepted : 2019.09.26
  • Published : 2019.10.10
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Abstract

Available methods to determine the ultimate bearing capacity of shallow foundations may not be accurate enough owing to the complicated failure mechanism and diversity of the underlying soils. Accordingly, applying new methods of artificial intelligence can improve the prediction of the ultimate bearing capacity. The M5' model tree and the genetic programming are two robust artificial intelligence methods used for prediction purposes. The model tree is able to categorize the data and present linear models while genetic programming can give nonlinear models. In this study, a combination of these methods, called the M5'-GP approach, is employed to predict the ultimate bearing capacity of the shallow foundations, so that the advantages of both methods are exploited, simultaneously. Factors governing the bearing capacity of the shallow foundations, including width of the foundation (B), embedment depth of the foundation (D), length of the foundation (L), effective unit weight of the soil (${\gamma}$) and internal friction angle of the soil (${\varphi}$) are considered for modeling. To develop the new model, experimental data of large and small-scale tests were collected from the literature. Evaluation of the new model by statistical indices reveals its better performance in contrast to both traditional and recent approaches. Moreover, sensitivity analysis of the proposed model indicates the significance of various predictors. Additionally, it is inferred that the new model compares favorably with different models presented by various researchers based on a comprehensive ranking system.

Keywords

References

  1. Abu-Farsakh, M.Y. and Titi, H.H. (2004), "Assessment of direct cone penetration test methods for predicting the ultimate capacity of friction driven piles", J. Geotech. Geoenviron. Eng., 130(9), 935-944. https://doi.org/10.1061/(ASCE)1090-0241(2004)130:9(935).
  2. Akbas, S.O. and Kulhawy, F.H. (2009), "Axial compression of footings in cohesionless soils. II: Bearing capacity", J. Geotech. Geoenviron. Eng., 135(11), 1575-1582. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000136.
  3. Alavi, A.H., Gandomi, A.H., Mousavi, M. and Mollahasani, A. (2010), "High-precision modeling of uplift capacity of suction caissons using a hybrid computational method", Geomech. Eng., 2(4), 253-280. https://doi.org/10.12989/gae.2010.2.4.253.
  4. Avval, Y.J. and Derakhshani, A. (2018), "New formulas for predicting liquefaction-induced lateral spreading: Model tree approach", Bull. Eng. Geol. Environ., 1-13. https://doi.org/10.1007/s10064-018-1319-1.
  5. Banzhaf, W., Nordin, P., Keller, R.E. and Francone, F.D. (1998), Genetic Programming: An Introduction, Morgan Kaufmann, San Francisco, California, U.S.A.
  6. Bhattacharya, B. and Solomatine, D.P. (2005), "Neural networks and M5 model trees in modelling water level-discharge relationship", Neurocomputing, 63, 381-396. https://doi.org/10.1016/j.neucom.2004.04.016.
  7. Bonakdar, L., Oumeraci, H. and Etemad-Shahidi, A. (2015), "Wave load formulae for prediction of wave-induced forces on a slender pile within pile groups", Coastal Eng., 102, 49-68. https://doi.org/10.1016/j.coastaleng.2015.05.003.
  8. Briaud, J.L. and Gibbens, R. (1997), "Large scale load tests and data base of spread footings on sand", https://rosap.ntl.bts.gov/view/dot/4563.
  9. Briaud, J.L. and Gibbens, R. (1999), "Behavior of five large spread footings in sand", J. Geotech. Geoenviron. Eng., 125(9), 787-796. https://doi.org/10.1061/(ASCE)1090-0241(1999)125:9(787)
  10. Cerato, A.B. and Lutenegger, A.J. (2007), "Scale effects of shallow foundation bearing capacity on granular material", J. Geotech. Geoenviron. Eng., 133(10), 1192-1202. https://doi.org/10.1061/(ASCE)1090-0241(2007)133:10(1192).
  11. Das, B.M. (2015), Principles of Foundation Engineering, Cengage Learning
  12. Derakhshani, A. (2017), "Estimating uplift capacity of suction caissons in soft clay: A hybrid computational approach based on model tree and GP", Ocean Eng., 146, 1-8. https://doi.org/10.1016/j.oceaneng.2017.09.025.
  13. Derakhshani, A. (2018), "On the uncertainty analysis of uplift capacity of suction caissons in clay based on the fuzzy sets theory", Ocean Eng., 170, 416-425. https://doi.org/10.1016/j.oceaneng.2018.10.045.
  14. Derakhshani, A. and Foruzan, A.H. (2019), "Predicting the principal strong ground motion parameters: A deep learning approach", Appl. Soft Comput., https://doi.org/10.1016/j.asoc.2019.03.029
  15. Eastwood, W. (1951), "A comparison of the bearing power of footings on dry and inundated sand", Struct. Engineer, 29(1), 1-11.
  16. Etemad-Shahidi, A. and Ghaemi, N. (2011), "Model tree approach for prediction of pile groups scour due to waves", Ocean Eng., 38(13), 1522-1527. https://doi.org/10.1016/j.oceaneng.2011.07.012
  17. Foye, K., Salgado, R. and Scott, B. (2006), "Assessment of variable uncertainties for reliability-based design of foundations", J. Geotech. Geoenviron. Eng., 132(9), 1197-1207. DOI: http://10.1061/(ASCE)1090-0241(2006)132:9(1197).
  18. Gandhi, G. (2003), "Study of bearing capacity factors developed from lab. Experiments on shallow footings on cohesionless soils", Ph.D. Thesis, Shri GS Institute of Technology and Science, Indore, India.
  19. Golder, H., Fellenius, W., Kogler, F., Meischeider, H., Krey, H. and Prandtl, L. (1941), "The ultimate bearing pressure of rectangular footings", J. Inst. Civ. Eng., 17(2), 161-174. https://doi.org/10.1680/ijoti.1941.13728.
  20. Hansen, J.B. (1970), "A revised and extended formula for bearing capacity", https://trid.trb.org/view/125129.
  21. Jafariavval, Y. and Derakhshani, A. (2019), "New formulae for capacity energy-based assessment of liquefaction triggering", Mar. Georesour. Geotechnol., 1-9. https://doi.org/10.1080/1064119X.2019.1566297.
  22. Javadi, A. and Rezania, M. (2009), "Applications of artificial intelligence and data mining techniques in soil modeling", Geomech Eng. 1(1), 53-74. https://doi.org/10.12989/gae.2009.1.1.053.
  23. Kalinli, A., Acar, M.C. and Gunduz, Z. (2011), "New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization", Eng. Geol., 117(1), 29-38. https://doi.org/10.1016/j.enggeo.2010.10.002.
  24. Kaveh, A., Hamze-Ziabari, S.M. and Bakhshpoori, T. (2018), "Soft computing-based slope stability assessment: A comparative study", Geomech. Eng., 14(3), 257-269. https://doi.org/10.12989/gae.2018.14.3.257.
  25. Koza, J.R. (1992), Genetic Programming: On the Programming of Computers by means of Natural Selection, MIT Press.
  26. Li, S., Yu, S., Shangguan, Z. and Wang, Z. (2016), "Estimating model parameters of rockfill materials based on genetic algorithm and strain measurements", Geomech. Eng., 10(1), 37-48. http://dx.doi.org/10.12989/gae.2016.10.1.037.
  27. Meyerhof, G.G. (1963), "Some recent research on the bearing capacity of foundations", Can. Geotech. J., 1(1), 16-26. https://doi.org/10.1139/cgj-2018-0433.
  28. Muhs, H. and Weiss, K. (1971), Untersuchung von Grenztragfahigkeit und Setzungsverhalten flachgegrundeter Einzelfundamente in ungleichformigen nichtbindigen Boden, Abschlussbericht des Forschungsauftrags Untersuchungen des Setzungsverhaltens und der Grenztragfahigkeit von flachgegrundeten Fundamenten in grobkornigen Sanden des Ministers fur Wohnungsbau und offentliche Arbeiten des Landes Nordrhein-Westfalen 1970, Ernst
  29. Muhs, H. and Weiss, K. (1973), "Inclined load tests on shallow strip footings", Proceedings of the 8th International Conference on Soil Mechanism and Foundation Engineering, Moscow, Russia, August.
  30. Muhs, H., Elmiger, R. and Weiss, K. (1969), Sohlreibung und Grenztragfahigkeit unter lotrecht und schrag belasteten Einzelfundamenten; mit 128 Bildern und 13 Zahlentafeln, Ernst.
  31. Padmini, D., Ilamparuthi, K. and Sudheer, K. (2008), "Ultimate bearing capacity prediction of shallow foundations on cohesionless soils using neurofuzzy models", Comput. Geotech., 35(1), 33-46. https://doi.org/10.1016/j.compgeo.2007.03.001.
  32. Quinlan, J.R. (1992), "Learning with continuous classes", Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, Hobart, Tamansia, November.
  33. Rostami, M.F., Sadrossadat, E., Ghorbani, B. and Kazemi, S.M. (2018), "New empirical formulations for indirect estimation of peak-confined compressive strength and strain of circular RC columns using LGP method", Eng. Comput., 34(4), 865-880. https://doi.org/10.1007/s00366-018-0577-7.
  34. Sadrossadat, E., Ghorbani, B., Hamooni, M. and Moradpoor Sheikhkanloo, M.H. (2018), "Numerical formulation of confined compressive strength and strain of circular reinforced concrete columns using gene expression programming approach", Struct. Concrete, 19(3), 783-794. https://doi.org/10.1002/suco.201700131.
  35. Sadrossadat, E., Soltani, F., Mousavi, S.M., Marandi, S.M. and Alavi, A.H. (2013), "A new design equation for prediction of ultimate bearing capacity of shallow foundation on granular soils", J. Civ. Eng. Manage., 19(sup1), S78-S90. https://www.tandfonline.com/doi/abs/10.3846/13923730.2013.801902.
  36. Searson, D.P. (2015), GPTIPS 2: An Open-Source Software Platform for Symbolic Data Mining, in Handbook of Genetic Programming Applications, Springer International Publishing, Cham, Germany.
  37. Shahin, M.A., Maier, H.R. and Jaksa, M.B. (2004), "Data division for developing neural networks applied to geotechnical engineering", J. Comput. Civ. Eng., 18(2), 105-114. https://doi.org/10.1061/(ASCE)0887-3801(2004)18:2(105).
  38. Shahnazari, H. and Tutunchian, M.A. (2012), "Prediction of ultimate bearing capacity of shallow foundations on cohesionless soils: An evolutionary approach", KSCE J. Civ. Eng., 16(6), 950-957. https://doi.org/10.1007/s12205-012-1651-0.
  39. Smith, G.N. (1986), Probability and Statistics in Civil Engineering, Collins Professional and Technical Books.
  40. Subrahmanyam, G. (1967), "The effect of roughness of footings on bearing capacity", J. Int. Soc. Soil Mech. Found. Eng., 6, 33-45.
  41. Talebi, A. and Derakhshani, A. (2019), "Estimation of Pmultipliers for laterally loaded pile groups in clay and sand", Ships Offshore Struct., 14(3), 229-237. https://doi.org/10.1080/17445302.2018.1495542.
  42. Terzaghi, K. (1943), Theoretical Soil Mechanics, Wiley Online Library
  43. Tsai, H.C., Tyan, Y.Y., Wu, Y.W. and Lin, Y.H. (2013), "Determining ultimate bearing capacity of shallow foundations using a genetic programming system", Neural Comput. Appl., 23(7-8), 2073-2084. https://doi.org/10.1007/s00521-012-1150-8.
  44. Vesic, A. (1973), "Analysis of ultimate loads of shallow foundations: closure of discussion of original paper", J. Soil Mech. Found. Div., 99(sm1).
  45. Wang, Y.W. (1997), "IH: Inducing model trees for predicting continuous classes", Proceedings of the European Conference on Machine Learning, Prague, Czech Republic, April.
  46. Weiss, K. (1970), Der Einflussder Fundamentform auf die Grenztragfahigkeit flachgegrundeter Fundamente, Untersuchungen ausgef.... von Klaus Weiss: mit 14 Zahlentaf, Ernst.
  47. Witten, I.H., Frank, E., Hall, M.A. and Pal, C.J. (2016), Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann.

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