Autor según el artículo: Rosa, Ana Carolina; Elomari, Youssef; Calderon, Alejandro; Mateu, Carles; Haddad, Assed; Boer, Dieter
Departamento: Enginyeria Mecànica
Autor/es de la URV: Boer, Dieter-Thomas / Elomari, Youssef
Palabras clave: Thermal conductivity Thermal conductivit Powder Performance Mlp Impac Gan Concrete Compressive strength Artificial neural networks
Resumen: The energy consumption of buildings presents a significant concern, which has led to a demand for materials with better thermal performance. Thermal conductivity (TC), among the most relevant thermal properties, is essential to address this demand. This study introduces a methodology integrating a Multilayer Perceptron (MLP) and a Generative Adversarial Network (GAN) to predict the TC of concrete based on its mass composition and density. Three scenarios using experimental data from published papers and synthetic data are compared and reveal the model's outstanding performance across training, validation, and test datasets. Notably, the MLP trained on the GAN-augmented dataset outperforms the one with the real dataset, demonstrating remarkable consistency between the model's predictions and the actual values. Achieving an RMSE of 0.0244 and an R2 of 0.9975, these outcomes can offer precise quantitative information and advance energy-efficient materials.
Áreas temáticas: Química Process chemistry and technology Physics, applied Materials science, multidisciplinary Materials science (miscellaneous) Materials science (all) Materiais Instrumentation General materials science General engineering Fluid flow and transfer processes Engineering, multidisciplinary Engineering (miscellaneous) Engineering (all) Engenharias ii Engenharias i Computer science applications Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências agrárias i Ciência de alimentos Chemistry, multidisciplinary Biodiversidade Astronomia / física
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: youssef.elomari@urv.cat youssef.elomari@urv.cat dieter.boer@urv.cat
Identificador del autor: 0000-0002-5532-6409
Fecha de alta del registro: 2024-09-28
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://www.mdpi.com/2076-3417/14/17/7598
Referencia al articulo segun fuente origial: Applied Sciences-Basel. 14 (17): 7598-
Referencia de l'ítem segons les normes APA: Rosa, Ana Carolina; Elomari, Youssef; Calderon, Alejandro; Mateu, Carles; Haddad, Assed; Boer, Dieter (2024). Methodology for the Prediction of the Thermal Conductivity of Concrete by Using Neural Networks. Applied Sciences-Basel, 14(17), 7598-. DOI: 10.3390/app14177598
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
DOI del artículo: 10.3390/app14177598
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2024
Tipo de publicación: Journal Publications