Articles producció científica> Enginyeria Mecànica

Methodology for the Prediction of the Thermal Conductivity of Concrete by Using Neural Networks

  • Datos identificativos

    Identificador: imarina:9380945
    Autores:
    Rosa, Ana CarolinaElomari, YoussefCalderon, AlejandroMateu, CarlesHaddad, AssedBoer, Dieter
    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.
  • Otros:

    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
  • Palabras clave:

    Chemistry, Multidisciplinary,Computer Science Applications,Engineering (Miscellaneous),Engineering, Multidisciplinary,Fluid Flow and Transfer Processes,Instrumentation,Materials Science (Miscellaneous),Materials Science, Multidisciplinary,Physics, Applied,Process Chemistry and Technology
    Thermal conductivity
    Thermal conductivit
    Powder
    Performance
    Mlp
    Impac
    Gan
    Concrete
    Compressive strength
    Artificial neural networks
    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
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