Autor segons l'article: Rosa, AC; Hammad, AWA; Boer, D; Haddad, A
Departament: Enginyeria Mecànica
Autor/s de la URV: Boer, Dieter-Thomas
Paraules clau: 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
Resum: 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.
Àrees temàtiques: Multidisciplinary sciences Multidisciplinary Medicina i Ciências biológicas ii Ciências biológicas i Biotecnología
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: dieter.boer@urv.cat
Identificador de l'autor: 0000-0002-5532-6409
Data d'alta del registre: 2023-12-17
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://www.sciencedirect.com/science/article/pii/S2405844023025690
URL Document de llicència: http://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Heliyon. 9 (4):
Referència de l'ítem segons les normes APA: Rosa, AC; Hammad, AWA; Boer, D; Haddad, A (2023). Use of operational research techniques for concrete mix design: A systematic review. Heliyon, 9(4), -. DOI: 10.1016/j.heliyon.2023.e15362
DOI de l'article: 10.1016/j.heliyon.2023.e15362
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2023
Tipus de publicació: Journal Publications
Repositori URV