BIM-Based Automated Drainage System Design in Prefabrication Construction
Bibliographic record
Abstract
Building information modeling is a technology applied in many construction projects to improve cost and time management, also enhancing the building processes of off-site construction. This paper proposes an automated method to design residential drainage systems in the BIM model, adapted for panelized construction manufactory. The drainage pipeline is separated into smaller components at the plumbing panel geometry boundaries in order to improve production efficiency at the prefabrication plant. The automated design application is created using C# programming in Visual Studio, providing an assembly plan for residential drainage systems using a prefab drainage planning approach. Meanwhile, a quantity take-off list for each plumbing panel is generated for the purpose of further cost analysis and schedule management. The auto-design method and planning algorithm are validated in a case study. The key contribution of this research is a rule-based and knowledge-based auto-design method for residential drainage systems to improve production efficiency and enhance the planning process in panelized construction. In future work, automated drainage system design for industrial projects and concrete or steel applications will be pursued.
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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 | 0.001 |
| 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".