Use of artificial neural networks to predict 3-D elastic settlement of foundations on soils with inclined bedrock

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Title: Use of artificial neural networks to predict 3-D elastic settlement of foundations on soils with inclined bedrock
Authors: Díaz Castañeda, Esteban | Brotons, Vicente | Tomás, Roberto
Research Group/s: Ingeniería del Terreno y sus Estructuras (InTerEs)
Center, Department or Service: Universidad de Alicante. Departamento de Ingeniería Civil
Keywords: Artificial neural networks (ANNs) | Foundation | Soil/structure interaction | Settlement | Elasticity | Finite-element modelling
Knowledge Area: Ingeniería del Terreno | Mecánica de Medios Continuos y Teoría de Estructuras
Issue Date: 1-Sep-2018
Publisher: Elsevier
Citation: Soils and Foundations. 2018, 58(6): 1414-1422. doi:10.1016/j.sandf.2018.08.001
Abstract: The application of the theory of elasticity for the calculation of foundation settlements has yielded equations that are well-established and consolidated in geotechnical standards and/or that are recommended for use. These equations are corrected by an influence factor in order to increase their precision and to encompass the existing complex geotechnical casuistry. The study presented herein utilizes neural networks to improve the determination of the influence factor (Iα), which considers the effect of a finite elastic half-space limited by the inclined bedrock under a foundation. The results obtained through the utilization of artificial neural networks (ANNs) demonstrate a notable improvement in the predicted values for the influence factor in comparison with those of existing analytical equations.
Sponsor: The work was partially supported by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO), the State Agency of Research (AEI) and European Funds for Regional Development (FEDER), under projects TEC2017-85244-C2-1-P and TIN2014-55413-C2-2-P, and by the Spanish Ministry of Education, Culture and Sport, under project PRX17/00439.
URI: http://hdl.handle.net/10045/81208
ISSN: 0038-0806 (Print) | 2524-1788 (Online)
DOI: 10.1016/j.sandf.2018.08.001
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2018 Production and hosting by Elsevier B.V. on behalf of The Japanese Geotechnical Society
Peer Review: si
Publisher version: https://doi.org/10.1016/j.sandf.2018.08.001
Appears in Collections:INV - INTERES - Artículos de Revistas

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