Change Management with Building Information Models: A Case Study
Bibliographic record
Abstract
Successful management of design changes is critical for the efficient delivery of construction projects. Building Information Models (BIM) and the use of parametric modeling provide significant benefits in coordinating changes across different views in a model. However, coordinating changes across several discipline-specific models is significantly more challenging to manage. This paper describes a case study that examines change management in the context of a multi-disciplinary collaborative BIM environment during the design and construction of a fast-track project. We documented the design changes, analyzed the change management processes and evaluated existing BIM tools in support of this process. Using two examples from the case study, we identify the characteristics of design changes required for tracking the history of changes and understanding the consequences of changes. We develop an ontology of changes based on the identified characteristics and patterns in the observed changes. The ontology characterizes design changes based on changed component attributes (the geometry, position, and specification), and based on dependencies between components (analytical and spatial). We further examine what needs to be updated in an information model corresponding to each type of change. The results of this study provide some possible directions for future developments in change management systems, particularly in reference to a BIM-based delivery process.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".