Compressive Strength Classification of Lightweight Aggregate Concrete Using a Support Vector Machine Model
Por favor, use este identificador para citar o enlazar este ítem:
http://hdl.handle.net/10045/99857
Título: | Compressive Strength Classification of Lightweight Aggregate Concrete Using a Support Vector Machine Model |
---|---|
Autor/es: | Tenza-Abril, Antonio José | Satorre Cuerda, Rosana | Compañ, Patricia | Navarro-González, Francisco J. | Villacampa, Yolanda |
Grupo/s de investigación o GITE: | Tecnología de Materiales y Territorio (TECMATER) | Grupo de Investigación en Tecnologías Inteligentes para el Aprendizaje (Smart Learning) | Modelización Matemática de Sistemas |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Ingeniería Civil | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial | Universidad de Alicante. Departamento de Matemática Aplicada |
Palabras clave: | Lightweight concrete | Aggregate | Vibration | Classification | Modelling |
Área/s de conocimiento: | Ingeniería de la Construcción | Ciencia de la Computación e Inteligencia Artificial | Matemática Aplicada |
Fecha de publicación: | 2019 |
Editor: | WIT Press |
Cita bibliográfica: | WIT Transactions on Engineering Sciences. 2019, 125: 173-183. doi:10.2495/CMEM190171 |
Resumen: | Lightweight aggregates (LWA) are used to produce low-density concretes required for building applications. Lightweight aggregate concrete (LWAC) is a multi-purpose material for construction, which offers technical, economical and environment benefits, and it is produced by replacing the normal-weight aggregates with LWA, depending upon the requirements of density and strength. LWAC is a complex composite material, and a model of its compressive strength must be highly nonlinear because it is very sensitive to its ingredients, so modelling its behaviour is a difficult task. Many studies have tried to develop accurate and effective predictive models for LWAC compressive strength. In this study, a support vector machine (SVM) learning algorithm is used to propose a model to classify the compressive strength of a wide range of LWAC. A dataset of 241 different LWACs were used for classifying the compressive strength into six different classes (from low-strength to high-strength) using different variables – quantity of cement, water and LWA in the dosage and density of the LWAC produced. The results show that increasing the variables means the model becomes more accurate up to approximately an 80% rate of success. The SVM model proved to be a significant tool to classify the compressive strength of LWAC contributing to engineers avoiding costly experimental trial tests. |
Patrocinador/es: | This research was supported by the University of Alicante (GRE13-03) and (VIGROB-256). |
URI: | http://hdl.handle.net/10045/99857 |
ISSN: | 1743-3533 |
DOI: | 10.2495/CMEM190171 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2019 WIT Press |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.2495/CMEM190171 |
Aparece en las colecciones: | INV - TECMATER - Artículos de Revistas INV - Smart Learning - Artículos de Revistas INV - MMS - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
---|---|---|---|---|
2019_Tenza-Abril_etal_WITTransEngSci.pdf | 477,92 kB | Adobe PDF | Abrir Vista previa | |
Todos los documentos en RUA están protegidos por derechos de autor. Algunos derechos reservados.