Construction progress visualisation for varied stages of the individual elements with BIM: A case study
Bibliographic record
Abstract
Building information modeling (BIM) ?is an intelligent 3D design and modeling process that gives architects, engineers, construction and facility managers the ability and tools to plan, design, construct and manage buildings more effectively and efficiently. Currently, the construction progress is monitored by comparing the baseline project schedules, which include the planned dates and resources, with the actual dates in the updated schedules. 4D scheduling is used in the construction industry for linking individual model elements with the schedule activities to visualize the progress of construction activities. Also, it provides analyzing tools in the 3D environment to improve the efficiency of project management, such as earned value analysis, project critical path analysis, and analysis of resource allocation. However, the limitation of this approach is a need for the creation of a dedicated activity for monitoring each model element, which can result in an excessive number of activities. Similarly, the required volume of data limits the generation of a dedicated activity for multiple statuses of building elements, such as “testing an element,” and “inspecting an element’. This paper presents a construction progress visualization method, which uses a custom developed add-in to present the status of building elements (e.g., planned, installed) without linking them with the schedule. The new tool enables a visual presentation of the progress of each element within the BIM model during different stages of the construction process to increase the decision-making capabilities. A case study is used to demonstrate the capabilities of the developed BIM add-in tool for construction progress visualization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".