An Automatic Scheduling Approach: Building Information Modeling-based Onsite Scheduling for Panelized Construction
Bibliographic record
Abstract
Panelized/modular construction is increasingly adopted within the industry as a primary construction method, with in-plant fabrication and onsite assembly as two of the main processes. Each of these two processes involves a different emphasis regarding productivity improvement: for in-plant fabrication, manufacturing process management is the main focus, whereas for onsite assembly, scheduling and management of assembly operations are of particular interest. This paper proposes a generic approach by which to generate the onsite schedule automatically based on a Building Information Model (BIM), considering the structural supporting and topological relationships among building elements, as well as knowledge of steel panel construction. The BIM is developed in an Autodesk Revit environment, based on which precedence relationships of elements are derived automatically and is utilized to perform the onsite schedule through the Autodesk Revit application programming interface (API). The generated schedule results are exported into Microsoft Project for further analysis, such as resource leveling. A case example is provided to demonstrate and validate the methodology. This paper explores the implementation of BIM, with the scheduling of panelized construction as the focus. This research lays the foundation for further implementation of BIM using Autodesk Revit.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".