Understanding the changes resulting from the virtualization of BIM-enabled collaborative design processes in the building construction industry
Bibliographic record
Abstract
The Canadian construction industry contributes significantly to the country's economy but faces various challenges, such as inefficiency, delayed projects, and negative environmental impact. Building Information Modelling (BIM) has been identified as a key driver for the industry's digital transformation and a potential remedy to its challenges. However, the adoption of BIM has been limited in the construction industry, which remains among the least digitalized sectors. Enabling digitalization in the construction industry, such as adopting BIM-enabled tools, requires change management competency. This study aims to understand how the virtualization led by BIM-enabled tools changes building design processes, specifically collaborative design processes. The study investigates three collaborative design processes with increasing complexity levels that employ various BIM tools. The impact of BIM-enabled tools on the collaborative design processes of maintainability design reviews, multi-disciplinary design coordination, and lean design management was investigated in this study. These design processes were studied within the context of different real-world case study projects that were selected to ensure diverse collaborative environments and contractual structures. The study utilized an action research approach, and mixed methods of data collection consisting of direct observations, interviews, and document and model analysis were used. The findings suggest that adopting BIM-enabled tools can improve collaborative design processes by enhancing communication, increasing efficiency, and improving information exchange between project stakeholders. The contributions of this study are threefold—first, the usability of virtual reality tools to improve communication and decision-making for maintainability design reviews is empirically demonstrated. Second, a framework for categorizing the changes from adopting cloud-based collaboration tools is developed and operationalized to illustrate the impact of cloud-based collaboration tools on the design coordination process. Finally, empirical evidence and practical insights are provided into the efficacy and challenges of executing lean design management processes in completely remote and virtual collaborative work environments. The findings from this study can help design professionals and project managers better plan and implement BIM-enabled tools in practice and enable the virtualization of design processes in the building construction industry.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".