Advancements in artificial intelligence-based decision support systems for improving construction project sustainability : a systematic literature review
Smith, Craig John and Wong, Andy T. C. (2022) Advancements in artificial intelligence-based decision support systems for improving construction project sustainability : a systematic literature review. Informatics, 9 (2). 43. ISSN 2227-9709 (https://doi.org/10.3390/informatics9020043)
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Abstract
This paper aims at evaluating the current state of research into artificial intelligence (AI)-based decision support systems (DSS) for improving construction project sustainability. The literature was systematically reviewed to explore the use of AI in the construction project lifecycle together with the consideration of the economic, environmental, and social goals of sustainability. A total of 2688 research papers were reviewed, and 77 papers were further analyzed, and the major tasks of the DSSs were categorized. Our review results suggest that the main research stream is dedicated to early-stage project prediction (50% of all papers), with artificial neural networks (ANNs) and fuzzy logic (FL) being the most popular AI algorithms in use. Hybrid AI models were used in 46% of all studies. The goal for economic sustainability is the most considered in research, with 87% of all papers considering this goal, and there is evidence given of a trend towards the environmental and social goals of sustainability receiving increasing attention throughout the latter half of the decade.
ORCID iDs
Smith, Craig John and Wong, Andy T. C. ORCID: https://orcid.org/0000-0001-8942-1984;-
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Item type: Article ID code: 80703 Dates: DateEvent13 May 2022Published13 May 2022Published Online11 May 2022AcceptedSubjects: Science > Mathematics > Electronic computers. Computer science
Technology > ManufacturesDepartment: Faculty of Engineering > Design, Manufacture and Engineering Management Depositing user: Pure Administrator Date deposited: 13 May 2022 09:15 Last modified: 11 Nov 2024 13:29 URI: https://strathprints.strath.ac.uk/id/eprint/80703