Damage assessment automation for single storey detached masonry houses: a probabilistic approach
Bibliographic record
Abstract
Assessing the existing condition of aging masonry houses are of high interest as the cost of retrofitting and repairing becomes significantly higher. Conventional condition assessment tools and methods for single storey detached masonry houses (SSDMH) are time-consuming, subjective, tedious, and sparse. This study aims to formulate a novel framework for assessing the condition of those houses by proposing a user-friendly, effective, and impartial model, for existing structures considering cracks in the masonry walls and the age of the house. This study adopted the bayesian belief network (BBN) method since the existing data on building assessment are subjective and consider multiple parameters. The application of the proposed model was formulated using wall cracks observed in a sample of thirty SSDMH. The Expectation Maximization (EM) algorithm was used to compute the conditional probabilities from the data set. The model was tested on ten houses for which the results were positive and validated with the Receiver Operating Characteristic (ROC) curve. However, the scope of the model is limited to SSDMH. Further development of this model may benefit the Surveyors, Engineers, and Architects to make informed decisions quickly by placing the structure at the correct severity level to decide on the renovation strategies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".