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Cited by 127BIM and Construction IntegrationCivil and Structural Engineering ResearchInfrastructure Maintenance and Monitoring

The DOI matches its Crossref record; 142 of the 142 references it cites were found in a registry or an index.

Influence of Artificial Intelligence in Civil Engineering toward Sustainable Development—A Systematic Literature Review

Open access
Manzoor, Bilal, Othman, Idris, Durdyev, Serdar, Ismail, Syuhaida, Wahab, Mohammad·2021·Applied System Innovation
DOI 10.3390/asi4030052
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Abstract

The widespread use of artificial intelligence (AI) in civil engineering has provided civil engineers with various benefits and opportunities, including a rich data collection, sustainable assessment, and productivity. The trend of construction is diverted toward sustainability with the aid of digital technologies. In this regard, this paper presents a systematic literature review (SLR) in order to explore the influence of AI in civil engineering toward sustainable development. In addition, SLR was carried out by using academic publications from Scopus (i.e., 3478 publications). Furthermore, screening is carried out, and eventually, 105 research publications in the field of AI were selected. Keywords were searched through Boolean operation “Artificial Intelligence” OR “Machine intelligence” OR “Machine Learning” OR “Computational intelligence” OR “Computer vision” OR “Expert systems” OR “Neural networks” AND “Civil Engineering” OR “Construction Engineering” OR “Sustainable Development” OR “Sustainability”. According to the findings, it was revealed that the trend of publications received its high intention of researchers in 2020, the most important contribution of publications on AI toward sustainability by the Automation in Construction, the United States has the major influence among all the other countries, the main features of civil engineering toward sustainability are interconnectivity, functionality, unpredictability, and individuality. This research adds to the body of knowledge in civil engineering by visualizing and comprehending trends and patterns, as well as defining major research goals, journals, and countries. In addition, a theoretical framework has been proposed in light of the results for prospective researchers and scholars.

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Crossref · 10.3390/asi4030052

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Citations

Cites

Research works printed but not linked: 4

  1. Lukovac, 2018, Normalized weighted geometric bonferroni mean operator of interval rough numbers–application in interval rough dematel-copras model, Facta Univ. Ser. Mech. Eng., 16, 171
  2. Vyas, 2018, Artificial intelligence: The beginning of a new era in pharmacy profession, Asian J. Pharm, 12, 72
  3. Amico, 2016, How artificial intelligence will redefine management, Harv. Bus. Rev., 2, 1
  4. Bannerjee, 2018, Artificial intelligence in agriculture: A literature survey, Int. J. Sci. Res. Comput. Sci. Appl. Manag. Stud., 7, 1

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Metadata

Authors
Manzoor, B., Othman, I., Durdyev, S., Ismail, S., & Wahab, M.
Affiliations
  • Universiti Teknologi Petronas (MY)
  • Ara Institute of Canterbury (NZ)
  • University of Technology Malaysia (MY)
Corresponding author
Durdyev, Serdar
ORCID
Venue
Applied System Innovation
Publisher
MDPI AG
Published
2021-08-06
Volume
4
Issue
3
Pages
52
ISSN
2571-5577 (ISSN-L)
DOI
10.3390/asi4030052
OpenAlex
W3190382256
Cited by
118
References
142
Citations by year
2026: 24 · 2025: 30 · 2024: 27 · 2023: 17 · 2022: 13 · 2021: 7
FWCI
1.48
Type
journal article
Language
en
Open access
Licence
Locations
  • Applied System Innovation (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Engineering › Building and Construction › BIM and Construction Integration
  • Physical Sciences › Engineering › Civil and Structural Engineering › Civil and Structural Engineering Research
  • Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
Keywords
artificial intelligence in civil engineering, sustainable development, sustainable construction, construction automation, machine learning, computer vision, expert systems, artificial neural networks, research trends, digital technologies in construction

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