A transformer-based approach for similar layout retrieval and difference detection in architectural drawings of wood frame buildings
Bibliographic record
Abstract
With labor shortages and increasing housing demands, efficient design methods are essential. Prefabricated buildings offer faster construction, better quality, and reduced waste. Typically, similar architectural layouts in prefabricated buildings imply comparable structural designs. Leveraging this correlation enables builders to reference previous designs, thereby reducing design time. However, manually searching databases can be time-consuming. The use of deep learning techniques can expedite this process. Through deep learning, designers can efficiently and accurately search databases for buildings with similar layouts. In this study, a drawing segmentation model was used to extract wall information from layout drawings. Buildings were then grouped into clusters based on this information. Subsequently, a pixel-wise difference method was developed to identify buildings with similar features and to highlight the differences between drawings. Finally, the proposed method was evaluated through two case studies. The results showed the proposed method can achieve acceptable accuracy in finding similar projects and identifying differences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".