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Least square support vector machine and multivariate adaptive regression spline for modeling lateral load capacity of piles

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Abstract

This article adopts least square support vector machine (LSSVM) and multivariate adaptive regression spline (MARS) for prediction of lateral load capacity (Q) of pile foundation. LSSVM is firmly based on the theory of statistical learning, uses regression technique. MARS is a nonparametric regression technique that models complex relationships. Diameter of pile (D), depth of pile embedment (L), eccentricity of load (e), and undrained shear strength of soil (S u) have been used as input parameters of LSSVM and MARS. Equations have been presented from the developed MARS and LSSVM. This study also presents a comparative study between the developed MARS and LSSVM.

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Authors and Affiliations

  1. Centre for Disaster Mitigation and Management, VIT University, Vellore, 632014, India

    Pijush Samui

  2. Department of Civil Engineering, Kunsan National University, Kunsan, Jeonbuk, South Korea

    Dookie Kim

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  1. Pijush Samui
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  2. Dookie Kim
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Correspondence to Pijush Samui.

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Samui, P., Kim, D. Least square support vector machine and multivariate adaptive regression spline for modeling lateral load capacity of piles. Neural Comput & Applic 23, 1123–1127 (2013). https://doi.org/10.1007/s00521-012-1043-x

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  • DOI: https://doi.org/10.1007/s00521-012-1043-x

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