Owner-driven implementation of BIM : process, challenges, information quality, and value
Bibliographic record
Abstract
Building information modeling (BIM) is emerging as a potential solution for asset owners to address the challenges of poor information quality and interoperability during project handover and inadequate facilities management during building operations. However, the implementation of BIM as a solution for asset owners is a complex challenge that creates disruptions in the conventional project delivery practices, including planning, design, construction, and handover. Although previous studies have documented the potential benefits of BIM adoption for owners, such as improvements in work order processing, very little research has specifically looked at the actual implementation of BIM driven by owners through the development and application of information requirements. Therefore, a significant gap between theory and practice remains, limiting the widespread and efficient adoption of BIM among asset owners. This dissertation investigates the implementation of BIM through the lens of two large public asset owner organizations in Canada going through the strategic process of adopting BIM in their projects. The research involved embedded case study analyses and a mixed method contextualist research approach incorporating iterative grounded theory and systematic combining. The case studies were conducted through interviews, document analysis, meeting observations, and a survey on three large projects with owner-defined information requirements from project inception to investigate three central aspects of BIM implementation for owners: (i) the asset information delivery process, (ii) information quality (IQ), and (iii) value. This study makes multiple contributions. First, it addresses the lack of empirical data from real-world case studies on owner-driven BIM implementation by providing a detailed description and characterization of the asset information delivery process, including information requirements, main activities, information workflow, scope of each stakeholder, tools, and challenges. Second, it contributes to the emerging literature on IQ in BIM by bridging theoretical dimensions of IQ with well-documented IQ issues derived from modeling practices employed as part of conventional project delivery practices. Finally, it proposes an analytical framework to facilitate a structured assessment of the costs and benefits of owner-driven BIM implementation, offering a nuanced and continuous approach to inform BIM adoption and demonstrating its potential value for asset owners, influencing industry-level strategies and resource allocation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".