Digital Collaboration Through BIM and its Influence on Project Success in Socio-Economic Contexts
Bibliographic record
Abstract
Building Information Modelling (BIM) has been a real revolution for the traditionally fragmented construction industry. Though BIM has emerged over a few decades, many users still find it challenging to utilise the exact benefits, especially in the aspect of design collaboration. Few scientific studies have been conducted in forming and enabling design collaboration environments in BIM projects. However, the impact towards project performance from BIM design collaboration is not addressed, which has hindered the practical applications and attainment of the benefits in construction projects. Therefore, this study attempts to understand the means of successful utilisation of design collaboration for improving project performance in a BIM-enabled environment. From the literature review, 12 project performance parameters were discovered. The level of design collaboration and their impact towards BIM project parameters were reviewed through the sequential exploratory mixed method. Initially, 8 semi-structured interviews and later a questionnaire survey were conducted. Findings revealed that the achievement of the design collaboration among different parties in a construction project differs, especially in terms of forming a BIM team, guidelines and management of a digital common data environment. Yet it is, found that many people have experienced a positive impact from design collaboration to project performance via BIM. Among all, digital coordination, project schedule performance, and stakeholder satisfaction record the highly influential project performance parameters. Further, a few strategies are proposed to identify barriers in achieving design collaboration for overcoming the challenges. The findings of this study promote BIM adoption for achieving positive project performance in the real.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".