Evidence map›Paper›PMID 37151679›Full record

ArticleHeliyon2023

Use of operational research techniques for concrete mix design: A systematic review.

Ana Carolina Rosa, Ahmed W A Hammad, Dieter Boer, Assed Haddad

Abstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ana Carolina RosaPrograma de Engenharia Ambiental, UFRJ, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 21941-901, Brazil.
Ahmed W A HammadSchool of Built Environment, UNSW Sydney (University of New South Wales), Sydney, 2052, Australia.
Dieter BoerDepartment of Mechanical Engineering, University Rovira i Virgili, Av. Països Catalans, 26, 43007, Tarragona, Spain.
Assed HaddadPrograma de Engenharia Ambiental, UFRJ, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 21941-901, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Concrete mix designMachine learningMathematical programmingOptimisation

Identifiers

PMID37151679
PMCPMC10161616

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.