A Convolutional Neural Network Based Pipeline for the Streamlining of the Masonry Quality Index Analysis
Why this work is in the frame
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Bibliographic record
Abstract
YOLOv11, a CNN-based object detection and instance segmentation algorithm, is used to automatically capture Masonry Quality Index (MQI) parameters for existing masonry structures. Training is performed using a suitable dataset for detecting bricks, and its hyperparameters are adjusted systematically for optimal accuracy. A workflow is proposed in which models are trained on the "MCrack1300" dataset and evaluated using orthomosaics of an unreinforced masonry building. Optimal hyperparameters are determined iteratively, and their impact on minimizing loss is compared. The proposed model captures block size distributions and staggering ratios associated with the construction quality of masonry walls.
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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 | 0.001 |
| 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 it