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
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