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https://accedacris.ulpgc.es/handle/10553/137914
| Título: | ANN model presented in "Estimation of pile stiffness in non-homogeneous soils through Artificial Neural Networks" | Autores/as: | Quevedo Reina, Román Álamo Meneses, Guillermo Manuel Aznárez González, Juan José |
Fecha de publicación: | 2025 | Editor/a: | Zenodo | Descripción: | <p>This repository contains the best of the models developed in the scientific article "Estimation of pile stiffness in non-homogeneous soils through Artificial Neural Networks" by Román Quevedo-Reina, Guillermo M. Álamo, Juan J. Aznárez. (https://doi.org/10.1016/j.engstruct.2024.117999)</p>
<p>This is an enssemble model of 20 artificial neural networks with 7 neurons in the input layer, 3 hidden layers with 145 neurons per hidden layer, and 4 neuron in the output layer.</p> <p><strong>Included in this upload are the following files:</strong></p> <p>APP-VERSION of the surrogate model.</p> <ul> <li>Soil_Impedance_Calculation_Model_Instaler: executable for installing the app (MATLAB Runtime is downloaded and installed automatically). Matlab license is not required.</li> <li>Soil_Impedance_Calculation_Model: file for installing the app inside Matlab. Matlab license is required with "Statistics and Machine Learning" and "Deep Learning" Toolboxes.</li> </ul> <p>CODE-VERSION of the surrogate model. Matlab license required with "Statistics and Machine Learning" and "Deep Learning" Toolboxes.</p> <ul> <li>class_model_pile: Matlab class that defines the surrogate model</li> <li>model_monopile: developed surrogate model</li> <li>User_manual: document explaining the use of these files</li> </ul> |
URI: | https://accedacris.ulpgc.es/handle/10553/137914 | Otros identificadores: | https://doi.org/10.5281/zenodo.15132546 oai:zenodo.org:15132546 |
Derechos: | info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode |
| Colección: | Datasets ULPGC |
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