dc.contributor.authorZhang, Wengang
dc.contributor.authorGoh, Anthony Teck Chee
dc.date.accessioned2015-12-07T09:09:46Z
dc.date.available2015-12-07T09:09:46Z
dc.date.issued2014
dc.identifier.citationZhang, W., & Goh, A. T. C. (2014). Multivariate adaptive regression splines and neural network models for prediction of pile drivability. Geoscience Frontiers, 7(1), 45-52.en_US
dc.identifier.issn1674-9871en_US
dc.identifier.urihttp://hdl.handle.net/10220/38985
dc.description.abstractPiles are long, slender structural elements used to transfer the loads from the superstructure through weak strata onto stiffer soils or rocks. For driven piles, the impact of the piling hammer induces compression and tension stresses in the piles. Hence, an important design consideration is to check that the strength of the pile is sufficient to resist the stresses caused by the impact of the pile hammer. Due to its complexity, pile drivability lacks a precise analytical solution with regard to the phenomena involved. In situations where measured data or numerical hypothetical results are available, neural networks stand out in mapping the nonlinear interactions and relationships between the system's predictors and dependent responses. In addition, unlike most computational tools, no mathematical relationship assumption between the dependent and independent variables has to be made. Nevertheless, neural networks have been criticized for their long trial-and-error training process since the optimal configuration is not known a priori. This paper investigates the use of a fairly simple nonparametric regression algorithm known as multivariate adaptive regression splines (MARS), as an alternative to neural networks, to approximate the relationship between the inputs and dependent response, and to mathematically interpret the relationship between the various parameters. In this paper, the Back propagation neural network (BPNN) and MARS models are developed for assessing pile drivability in relation to the prediction of the Maximum compressive stresses (MCS), Maximum tensile stresses (MTS), and Blow per foot (BPF). A database of more than four thousand piles is utilized for model development and comparative performance between BPNN and MARS predictions.en_US
dc.format.extent8 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesGeoscience Frontiersen_US
dc.rights© 2014 China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).en_US
dc.subjectBack propagation neural networken_US
dc.subjectMultivariate adaptive regression splinesen_US
dc.subjectPile drivabilityen_US
dc.subjectComputational efficiencyen_US
dc.subjectNonlinearityen_US
dc.titleMultivariate adaptive regression splines and neural network models for prediction of pile drivabilityen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Civil and Environmental Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.gsf.2014.10.003
dc.description.versionPublished versionen_US


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