A Taxonomy of Sources of Waste in BIM Information Flows
Bibliographic record
Abstract
Since its introduction, Building Information Modelling has evolved into a major technology in the construction industry, where information flows play a major role. However, the very presence of waste within these flows prevents the technology from reaching its full potential. This paper aims to develop a taxonomy focused on the sources of waste within information flows in BIM projects, as existing taxonomies focus primarily on work and material flows. Using a collaborative design science research approach, the study was divided into two phases: first, semi-structured interviews were used to collect data on BIM practices. The data was then used to identify similarities and contradictions in the information flows using process maps. Second, the ShareLab approach was used to validate the findings through a common agreement. The paper’s main contribution is the taxonomy of sources of waste in BIM project information flows, as it closes knowledge gaps in one of the main flows of construction projects. Another contribution is its use of a new approach to validate an artefact in a Design Science Research methodology named the ShareLab approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".