Author, as appears in the article.: Rosa, AC; Hammad, AWA; Boer, D; Haddad, A
Department: Enginyeria Mecànica
URV's Author/s: Boer, Dieter-Thomas
Keywords: 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
Abstract: 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.
Thematic Areas: Multidisciplinary sciences Multidisciplinary Medicina i Ciências biológicas ii Ciências biológicas i Biotecnología
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: dieter.boer@urv.cat
Author identifier: 0000-0002-5532-6409
Record's date: 2024-08-03
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: https://www.sciencedirect.com/science/article/pii/S2405844023025690
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Heliyon. 9 (4):
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
Article's DOI: 10.1016/j.heliyon.2023.e15362
Entity: Universitat Rovira i Virgili
Journal publication year: 2023
Publication Type: Journal Publications