Journal
INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS
Volume 36, Issue 11, Pages 1434-1439Publisher
WILEY-BLACKWELL
DOI: 10.1002/nag.1076
Keywords
piles; ultimate capacity; multivariate adaptive regression spline; generalized regression neural network; cohesionless soil
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The determination of ultimate capacity (Q) of driven piles in cohesionless soil is an important task in geotechnical engineering. This article adopts Multivariate Adaptive Regression Spline (MARS) for prediction Q of driven piles in cohesionless soil. MARS uses length (L), angle of shear resistance of the soil around the shaft (?shaft), angle of shear resistance of the soil at the tip of the pile (?tip), area (A), and effective vertical stress at the tip of the piles'v as input variables. Q is the output of MARS. The results of MARS are compared with that of the Generalized Regression Neural Network model. An equation has been also presented based on the developed MARS. The results show the strong potential of MARS to be applied to geotechnical engineering as a regression tool. Copyright (c) 2011 John Wiley & Sons, Ltd.
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