Design Change Management Using BIM-based Visualization Model
Bibliographic record
Abstract
Building projects frequently experience a number of changes in design development to satisfy owners’ space and functional needs within allocated budgets. These iterative changes, while necessary, individually and collectively have ripple effect on what appears to be the unchanged scope of projects work, with varying impacts on project delivery time and cost. Efficient management of design changes requires scope rendition and minimization of time and cost impacts of these changes. This paper presents an automated model, developed to help design professionals and owners visualize the ripple effect of contemplated design changes. This visualization covers those design changes requested by owners after completion of the design phase and before commencement of the construction phase. The developed model is expected to help owners and their agents to better grasp the ripple effect of design changes and consequently, to make better decisions as to approving or rejecting contemplated changes. The model is also able to calculate the impact of changes on projects cost and time. The model is developed on the basis of comparing the original model of a building to its revised model, which incorporates the introduced changes. The changes included in the developed model encompass addition, deletion as well as changes in quantities and specifications of building components. The model is then integrated with Building Information Modeling (BIM) to provide visualization and documentation of the design changes. The use of BIM provides significant benefits in coordinating changes across different views in the model, thereby enabling users to study the ripple effect of a change from different views, such as plans, elevations and 3D views. The model is capable of illustrating the ripple effect in architectural, mechanical, electrical and HVAC systems. For evaluation purposes, the model has been applied to a case study.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".