Abstract
This paper discusses the use of multivariate adaptive regression splines (MARS) and functional networks (FN) for prediction of the lateral load capacity of piles in clay. The results obtained from MARS and FN have been compared with different empirical models and artificial neural network in terms of statistical parameters such as correlation coefficient (R), Nash–Sutcliff coefficient of efficiency (E), absolute average error, maximum average error and root mean square error. Based on the statistical parameters, MARS and FN were found to have a better predictive capacity. Predictive equations are provided based on the MARS and FN model. A sensitivity analysis is also presented to determine the importance of inputs in prediction of the lateral load capacity of piles.
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References
Hansen, B.: The ultimate resistance of rigid piles against transversal force. Copenhagen, Danish Geotechnical Institute, Bulletin no. 12., pp. 5–9 (1961)
Broms, B.B.: Lateral resistance of piles in cohesive soils. J. Soil Mech. Found Engg. ASCE 90((SM. 2), 27–63 (1964a)
Broms, B.B.: Lateral resistance of piles in cohesionless soils. J. Geotech. Eng. 90, 123–156 (1964b)
Poulos H.G., Davis E.H.: Pile foundation analysis and design. Wiley, New York (1980)
Matlock H., Reese L.C.: Generalized solutions for laterally loaded piles. Trans ASCE 127, 1220–1248 (1962)
Portugal, J.C.; Seco e Pinto, P.S.: Analysis and design of pile under lateral loads.In: Proceedings of the 11th international geotechnical seminar on deep foundation on bored and auger piles, Belgium, pp. 309–313 (1993)
Muthukkumaran K., Sundaravadivelu R., Gandhi S.R.: Effect of slope on p–y curves due to surcharge load. Soils Found 48(3), 353–361 (2008)
Begum N.A., Muthukkumaran K.: Experimental investigation on single model pile in sloping ground under lateral load. Int. J. Geotech. Eng. 3(1), 133–146 (2009)
Muthukkumaran K.: Effect of slope and loading direction on laterally loaded piles in cohesionless soil. Int. J. Geomech. ASCE 14(1), 1–7 (2014)
Das S.K., Basudhar P.K.: Undrained lateral load capacity of piles in clay using artificial neural network. Comput. Geotech. 33, 454–459 (2006)
Hamid M., Reza R.: The estimation of rock mass deformation modulus using regression and artificial neural networks analysis. Arab. J. Sci. Eng. 35(1A), 205–217 (2010)
Das S.K., Biswal R.K., Sivakugan N., Das B.: Classification of slopes and prediction of factor of safety using differential evolution neural networks. Environ. Earth. Sci., Springer 64, 201–210 (2011)
Muduli P.K., Das M.R., Samui P., Das S.K.: Uplift capacity of suction caisson in clay using artificial intelligence techniques. Mar. Georesour. Geotechnol. 31(4), 375–390 (2013)
Tarawneh B., Imam R.: Regression versus artificial neural networks: predicting pile setup from empirical data. KSCE J. Civ. Eng. 18(4), 1018–1027 (2014)
Goh A.T.C.: Empirical design in geotechnics using neural networks. Geotechnique 45(4), 709–714 (1995)
Goh A.T.C.: Pile driving records reanalyzed using neural networks. J. Geotech. Eng., ASCE 122(6), 492–495 (1996)
Chan W.T., Chow Y.K., Liu L.F.: Neural network: an alternative to pile driving formulas. J. Comput. Geotech. 17, 135–156 (1995)
Lee I.M., Lee J.H.: Prediction of pile bearing capacity using artificial neural networks. Comput. Geotech. 18(3), 189–200 (1996)
Teh C.I., Wong K.S., Goh A.T.C., Jaritngam S.: Prediction of pile capacity using neural networks. J. Comput. Civ. Eng., ASCE 11(2), 129–138 (1997)
Abu-Kiefa M.A.: General regression neural networks for driven piles in cohesionless soils. J. Geotech. Geoenviron. Eng., ASCE 124(12), 1177–1185 (1998)
Samui P.: Prediction of friction capacity of driven piles in clay using the support vector machine. Can. Geotech. J. 45(2), 288–295 (2008)
Pal M., Deswal S.: Modelling pile capacity using Gaussian process regression. Comput. Geotech. 37, 942–947 (2010)
Alkroosh I., Nikraz H.: Evaluation of pile lateral capacity in clay applying evolutionary approach. Int. J. GEOMATE 4(1), 462–465 (2013)
Giustolisi O., Doglioni A., Savic D.A., Webb B.W.: A multi-model approach to analysis of environmental phenomena. Environ. Model. Softw. 22(5), 674–682 (2007)
Friedman J.: Multivariate adaptive regression splines. Ann. Stat. 19, 1–141 (1991)
Samui P., Das S., Kim D.: Uplift capacity of suction caisson in clay using multivariate adaptive regression spline. Ocean Eng. 38(17–18), 2123–2127 (2011)
El-Sebakhy E.A., Asparouhov O., Abdulraheem A., Al-Majed A., Wu D., Latinski K., Raharja I.: Functional networks as a new data mining predictive paradigm to predict permeability in a carbonate reservoir. Expert Syst. Appl. 39, 10359–10375 (2012)
Castillo E., Cobo A., Gutierrez J.M., Pruneda R.E.: Working with differential, functional and difference equations using functional networks. Appl. Math. Model. 23, 89–107 (1999)
El-Sebakhy, E.A.; Faisal, K.A.; Helmy, T.; Azzedin, F.; Al-Suhaim, A.: Evaluation of breast cancer tumor classification with unconstrained functional networks classifier. In: Proceeding of the 4th ACS/IEEE international conference on computer systems and applications, pp. 281–287 (2006)
Rajasekaran S.: Functional networks in structural engineering. J. Comput. Civ. Eng. 18, 172–181 (2004)
Attoh-Okine N.O.: Modeling incremental pavement roughness using functional network. Can. J. Civ. Eng. 32, 899–905 (2005)
Castillo E.: Functional networks. Neural Process. Lett. 7, 151–159 (1998)
Castillo E., Ruiz-Cobo R.: Functional equations in science and engineering. Marcel Dekker, New York (1992)
Castillo E., Cobo A., Gutierrez J.M., Pruneda E.: An introduction to functional networks with applications. Kluwer, Boston (1998)
Castillo E., Cobo A., Manuel J., Gutierrez J.M., Pruneda E.: Functional networks: a new network-based methodology. Comput. Aided Civ. Infrastruct. Eng. 15, 90–106 (2000a)
Castillo E., Cobo A., Gomez-Nesterkin R., Hadi A.S.: A general framework for functional networks. Networks 35(1), 70–82 (2000b)
Das S.K., Basudhar P.K.: Prediction of residual friction angle of clays using artificial neural network. Eng. Geol. 100(3–4), 142–145 (2008)
Abu-Farsakh M.Y., Titi H.H.: Assessment of direct cone penetration test methods for predicting the ultimate capacity of friction driven piles. J. Geotech. Geoenviron. Eng. 130(9), 935–944 (2004)
Jekabsons, G.; ARESLab: Adaptive regression splines toolbox for Matlab/Octave. http://www.cs.rtu.lv/jekabsons/ (2011)
MathWork Inc.: Matlab User’s Manual, Version 6.5. Natick (MA).(2005)
Das S.K., Sivakugan N.: Discussion of: intelligent computing for modeling axial capacity of pile foundations. Can. Geotech. J. 47, 928–930 (2010)
Gandomi, A.H.; Yun, G.J.; Alavi, A.H.: An evolutionary approach for modeling of shear strength of RC deep beams. Mater. Struct. (2013) doi:10.1617/s11527-013-0039-z
Rao, K.M.; Suresh Kumar, V.: Measured and predicted response of laterally loaded piles. In: Proceedings of the sixth international conference and exhibition on piling and deep foundations, India, pp. 1.6.1–1.6.7 (1996)
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Das, S.K., Suman, S. Prediction of Lateral Load Capacity of Pile in Clay Using Multivariate Adaptive Regression Spline and Functional Network. Arab J Sci Eng 40, 1565–1578 (2015). https://doi.org/10.1007/s13369-015-1624-y
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DOI: https://doi.org/10.1007/s13369-015-1624-y