Assessing BIM adoption in the Canadian construction industry: a fuzzy Bayesian network approach

Building information modeling (BIM) adoption has been recognized as a promising development within the construction industry. While BIM offers significant advantages for managing the entire lifecycle of construction projects, its uptake in the Canadian construction sector remains limited. This study employs Bayesian belief networks (BBN) to evaluate the factors influencing the successful implementation of BIM in Canada. Key drivers were identified through a comprehensive literature review, while occurrence probability data were gathered from Canadian construction professionals and analyzed using BBN. The results of this cross-sectional analysis indicate that under current conditions, the probability of successful BIM implementation is relatively low. To improve the likelihood of successful adoption, addressing the shortage of critical BIM resources is essential. The proposed BBN framework serves as a prototype for assessing BIM implementation success across various regions, providing valuable insights for industry stakeholders