Author, as appears in the article.: Rosa, Ana Carolina; Elomari, Youssef; Calderon, Alejandro; Mateu, Carles; Haddad, Assed; Boer, Dieter
Department: Enginyeria Mecànica
URV's Author/s: Boer, Dieter-Thomas / Elomari, Youssef
Keywords: Thermal conductivity Thermal conductivit Powder Performance Mlp Impac Gan Concrete Compressive strength Artificial neural networks
Abstract: 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.
Thematic Areas: 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
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: youssef.elomari@urv.cat youssef.elomari@urv.cat dieter.boer@urv.cat
Author identifier: 0000-0002-5532-6409
Record's date: 2024-09-28
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: https://www.mdpi.com/2076-3417/14/17/7598
Papper original source: Applied Sciences-Basel. 14 (17): 7598-
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
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Article's DOI: 10.3390/app14177598
Entity: Universitat Rovira i Virgili
Journal publication year: 2024
Publication Type: Journal Publications
Repositori URV