Evidence map›Paper›PMID 39286223›Full record

ArticleHeliyon2024

Revolutionizing construction: A cutting-edge decision-making model for artificial intelligence implementation in sustainable building projects.

Ahmed Farouk Kineber, Nehal Elshaboury, Ayodeji Emmanuel Oke, John Aliu, Ziyad Abunada, Mohammad Alhusban

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Ahmed Farouk KineberCollege of Project Management, Built Environment, Asset & Maintenance Management, School of Engineering and Technology, Central Queensland University, Melbourne, Australia.
Nehal ElshabouryConstruction and Project Management Research Institute, Housing and Building National Research Center, Giza, 12311, Egypt.
Ayodeji Emmanuel OkeDepartment of Quantity Surveying, Federal University of Technology, Akure, 340110, Nigeria.
John AliuInstitute for Resilient Infrastructure Systems, College of Engineering, University of Georgia, Athens, GA, USA.
Ziyad AbunadaSchool of Engineering and Technology, Central Queensland University, Melbourne, VIC, 3000, Australia.
Mohammad AlhusbanDepartment of Civil Engineering, Middle East University, Amman, 3626, Jordan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study examines how certain artificial intelligence (AI) drivers affect the industry's adoption of this technology in the construction industry. The research methods comprised a comprehensive analysis of previous studies to pinpoint the primary factors influencing AI adoption in the construction industry. Data collection was carried out through a well-structured survey involving relevant stakeholders in the building construction sector. The three main constructs of technological devices, advancement, and knowledge were found from the set of drivers with the technique of exploratory factor analysis. The deployment of AI in construction has the potential to improve health and safety and expedite project completion, as this research has evaluated. To figure out how these factors relate to the adoption of AI in the construction industry, partial least squares structural equation modeling was used. The study's conclusions showed that the influence of AI installation in the construction industry is reasonably significant thanks to the technology, advancement, and knowledge, contributing around 15 % of the effects that have been directly witnessed. The practical implications of AI for policy makers, engineers, and construction stakeholders are extensive and provide valuable insights for customized strategies aimed at using AI's potential to improve projects, promote sustainability, and elevate health and safety standards.

Indexed as

Artificial intelligenceBenefits assessmentConstruction digitalizationDigital transformationHealth and safetyProject completionSustainable construction

Identifiers

PMID39286223
PMCPMC11402912

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.