Autor según el artículo: Rosa, Ana Carolina; Hammad, Ahmed W A; Boer, Dieter; Haddad, Assed
Departamento: Enginyeria Mecànica
Autor/es de la URV: Boer, Dieter-Thomas
Palabras clave: Optimisation; Mathematical programming; Machine learning; Concrete mix design; Artificial neural-network; sustainable concrete; silica fume; self-consolidating concrete; optimisation; mechanical-properties; mathematical programming; fly-ash; elastic-modulus; concrete mix design; compressive strength prediction; carbonation depth; autogenous shrinkage
Resumen: Traditional methods for designing concrete mixtures provide good results; however, they do not guarantee the optimum composition. Consequently, applying operational research techniques is motivated by an increasing need for designers to proportion the concrete's raw materials that satisfy the concrete performance requirements such as mechanical properties, chemical properties, workability, sustainability, and cost. For this reason, many authors have been looking for mathematical programming and machine learning solutions to predict concrete mix properties and optimise concrete mixtures. Therefore, a comprehensive review of operational research techniques concerning the design and proportioning of concrete mixtures and a classification framework are presented herein.
Áreas temáticas: Multidisciplinary sciences; Multidisciplinary; Medicina i; Ciências biológicas ii; Ciências biológicas i; Biotecnología
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: dieter.boer@urv.cat
Fecha de alta del registro: 2025-02-24
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://www.sciencedirect.com/science/article/pii/S2405844023025690
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Heliyon. 9 (4): e15362-
Referencia de l'ítem segons les normes APA: Rosa, Ana Carolina; Hammad, Ahmed W A; Boer, Dieter; Haddad, Assed (2023). Use of operational research techniques for concrete mix design: A systematic review. Heliyon, 9(4), e15362-. DOI: 10.1016/j.heliyon.2023.e15362
DOI del artículo: 10.1016/j.heliyon.2023.e15362
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2023
Tipo de publicación: Journal Publications
Repositori URV