Please use this identifier to cite or link to this item:
http://hdl.handle.net/10553/130745
Title: | Estimation of pile stiffness in non-homogeneous soils through Artificial Neural Networks | Authors: | Quevedo Reina, Román Álamo Meneses, Guillermo Manuel Aznárez González, Juan José |
UNESCO Clasification: | 330532 Ingeniería de estructuras 330533 Resistencia de estructuras |
Keywords: | Laterally Loaded Piles Impedance Functions Pile Foundation Soil-Structure Interaction Non-Homogeneous Soils, et al |
Issue Date: | 2024 | Journal: | Engineering Structures | Abstract: | Many international standards highlight the relevance of studying the compatibility of forces and displacements between the structure and the foundation that supports it. In the case of pile foundations, some authors use continuum models that rigorously reproduce the interaction of the pile with the surrounding soil. However, their high computational cost justifies the use of simplified methodologies that allow obtaining sufficiently accurate results in significantly less time. This work presents a surrogate model based on Artificial Neural Networks (ANNs) trained from a synthetic dataset generated by a continuum numerical model. A good regression capacity is observed by the proposed model, also requiring very short evaluation times. Two use examples are presented to illustrate the smooth behaviour of the ANNs model and its ability to determine the critical pile length. This surrogate model allows introducing soil-structure interaction in problems with large volume of evaluations in a feasible way without significantly compromising the confidence of the results. | URI: | http://hdl.handle.net/10553/130745 | ISSN: | 0141-0296 | DOI: | 10.1016/j.engstruct.2024.117999 | Source: | Engineering Structures [ISSN 0141-0296], v. 308, (Junio 2024) |
Appears in Collections: | Artículos |
Page view(s)
31
checked on Jul 6, 2024
Download(s)
14
checked on Jul 6, 2024
Google ScholarTM
Check
Altmetric
Share
Export metadata
Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.