Automatic Generation of Electrical Plan Documents from Architectural Data
Bibliographic record
Abstract
This paper explores a novel application of document generation: the automatic creation of residential electrical plans from architectural data. Electrical plan documents, crucial to all residential construction and renovation projects, are currently generated manually by electrical designers who must adhere to the local electrical code and follow industry best practices. The designers decide the type and location of all household electrical devices and outlets based on the architectural floor plans. This is a tedious, highly repetitive, and time-consuming task. We propose a procedural approach to automate the generation of residential electrical plans via a stack-based finite state machine model that mimics the electrical designer's thought process. The system receives 2D architectural data (e.g. wall location) as input and yields a customized electrical plan as output. Experimental results on a variety of architectural layouts of bathrooms, bedrooms, and kitchens are very promising and demonstrate the approach's functionality and usefulness. This paper paves the way for new algorithmic tools facilitating the design cycle of building projects.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.003 |
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".