Streamlining Building Information Model creation using Agile project management
Bibliographic record
Abstract
Building Information Modelling (BIM) offers tremendous advantages to multidisciplinary teams from a coordination standpoint throughout the project lifecycle.That said, there are potential pitfalls with the use of BIM if coordination and communication between the disciplines is not properly managed, resulting in rework and inefficiencies in delivery.Agile project management techniques, widely adopted within the software industry to allow incremental development of products, have great potential for application to BIM model development throughout the project lifecycle.These techniques focus on simple and regular communication between core project members and stakeholders, regular priority identification to set the goals for the next phase ("Sprint") and incremental product development.Applied to BIM model development, these techniques provide a framework for multi-disciplinary coordination and interface management and reduce re-design and abortive effort from miscommunication and poor task sequencing.This paper presents two case studies to demonstrate the effectiveness of this approach.In the first, an Architectural BIM model was created at schematic design and these techniques were used to guide the development of the design and the associated BIM model.In the second, a BIM model was created during the operational phase of a building to consolidate the data from a variety of facilities management and operations systems (maintenance, asset management, health and safety, sustainability and space planning).In each, the various stakeholders are consulted to set priorities for model development, which are incrementally implemented.These case studies demonstrate the benefits of using an Agile approach to BIM and inform the framework presented in this paper.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".