Evidence map›Paper›PMID 38027981›Full record

ArticleHeliyon2023

Bio-inspired based meta-heuristic approach for predicting the strength of fiber-reinforced based strain hardening cementitious composites.

Yasar Khan, Adeel Zafar, Muhammad Faisal Rehman, Muhammad Faisal Javed, Bawar Iftikhar, Yaser Gamil

RetractedOpen access · goldAbstract readRetracted Publication
PubMed Full text at PMC Publisher
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. It has been retracted, and should not be counted. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.5field-weighted citation impact, top 39% of its field
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

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 4 institutions in 3 countries.

Yasar KhanDepartment of Structural Engineering, Military College of Engineering (MCE), National University of Science and Technology (NUST), Islamabad 44000, Pakistan.
Adeel ZafarDepartment of Structural Engineering, Military College of Engineering (MCE), National University of Science and Technology (NUST), Islamabad 44000, Pakistan.
Muhammad Faisal RehmanUniversity of Engineering and Technology Peshawar, Abbottabad Campus, Pakistan.
Muhammad Faisal JavedDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, 22060, Pakistan.
Bawar IftikharDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, 22060, Pakistan.
Yaser GamilDepartment of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Sweden.
COMSATS University Islamabad · PKNational University of Sciences and Technology · PKMonash University Malaysia · MYUniversity of Engineering and Technology Peshawar · PK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A recently introduced bendable concrete having hundred times greater strain capacity provides promising results in repair of engineering structures, known as strain hardening cementitious composites (SHHCs). The current research creates new empirical prediction models to assess the mechanical properties of strain-hardening cementitious composites (SHCCs) i.e., compressive strength (CS), first crack tensile stress (TS), and first crack flexural stress (FS), using gene expression programming (GEP). Wide-ranging records were considered with twelve variables i.e., cement percentage by weight (C%), fine aggregate percentage by weight (F

Indexed as

Compressive strengthEngineering cementitious composites (ECC)Flexural stressGene expression programming (GEP)Machine learning (ML)Tensile stress

Identifiers

PMID38027981
PMCPMC10665749
OpenAlexW4388210939

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.