Integrating Building Information Modelling and Artificial Intelligence in Construction Projects: A Review of Challenges and Mitigation Strategies
Article
Article Title | Integrating Building Information Modelling and Artificial Intelligence in Construction Projects: A Review of Challenges and Mitigation Strategies |
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ERA Journal ID | 214271 |
Article Category | Article |
Authors | Khan, Ayaz, Bello, Abdulkabir Opeyemi, Arqam, Mohammad and Ullah, Fahim |
Editors | Fragulis, G.F. and Roy, D. |
Journal Title | Technologies |
Journal Citation | 12 (10) |
Article Number | 185 |
Number of Pages | 19 |
Year | 2024 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2227-7080 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/technologies12100185 |
Web Address (URL) | https://www.mdpi.com/2227-7080/12/10/185 |
Abstract | Artificial intelligence (AI), including machine learning and decision support systems, can deploy complex algorithms to learn sufficiently from the large corpus of building information modelling (BIM) data. An integrated BIM-AI system can leverage the insights to make smart and informed decisions. Hence, the integration of BIM-AI offers vast opportunities to extend the possibilities of innovations in the design and construction of projects. However, this synergy suffers unprecedented challenges. This study conducted a systematic literature review of the challenges and constraints to BIM-AI integration in the construction industry and categorise them into different taxonomies. It used 64 articles, retrieved from the Scopus database using the PRISMA protocol, that were published between 2015 and July 2024. The findings revealed thirty-nine (39) challenges clustered into six taxonomies: technical, knowledge, data, organisational, managerial, and financial. The mean index score analysis revealed financial (µ = 30.50) challenges are the most significant, followed by organisational (µ = 23.86), and technical (µ = 22.29) challenges. Using Pareto analysis, the study highlighted the twenty (20) most important BIM-AI integration challenges. The study further developed strategic mitigation maps containing strategies and targeted interventions to address the identified challenges to the BIM-AI integration. The findings provide insights into the competing issues stifling BIM-AI integration in construction and provide targeted interventions to improve synergy. |
Keywords | Artificial Intelligence; building information modelling; challenges; construction industry; systematic literature review |
Article Publishing Charge (APC) Amount Paid | 2735.0 |
Article Publishing Charge (APC) Funding | Researcher |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 330201. Automation and technology in building and construction |
330204. Building information modelling and management | |
Byline Affiliations | University of South Australia |
IVE: Australian Research Centre for Interactive and Virtual Environments, Australia | |
Federal University of Technology, Nigeria | |
SAF Polytechnic, Nigeria | |
University of Technology Sydney | |
School of Surveying and Built Environment |
https://research.usq.edu.au/item/z9vyx/integrating-building-information-modelling-and-artificial-intelligence-in-construction-projects-a-review-of-challenges-and-mitigation-strategies
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