An enhanced machine learning-based rapid visual screening framework for low-rise RC buildings considering model uncertainty and decision threshold optimization
Bibliographic record
Abstract
Existing structures can become seismically vulnerable over time due to various factors including deterioration and outdated or seismic design provisions. Rapid visual screening (RVS) methods are commonly used to quickly filter large building inventories for at-risk structures, typically based on simple visual inspections, such as sidewalk surveys. In a previous study, the authors developed a machine learning (ML)-based RVS method for low-rise reinforced concrete (RC) buildings capable of identifying buildings that are likely to be severely damaged in an earthquake with an accuracy of 71%. However, uncertainty in the model’s predictions remains a concern. This study refines the previously proposed RVS methodology by addressing model uncertainty and minimizing misclassifications. Two primary approaches are proposed: the first analyzes class probabilities from the ML-based screening model to assess the prediction uncertainty rather than relying on the final predicted damage class. With this approach, buildings for which the ML model shows high uncertainty can be prioritized for more detailed evaluation. The second approach aims to optimize the decision threshold used by the ML model to more accurately identify buildings at risk of severe damage. This is done by evaluating the relative cost of misclassifications, low risk buildings identified as high risk (false positives) and high-risk buildings identified as low risk (false negatives). Building on the findings, this paper proposes a comprehensive three-level machine learning-based methodology for enhanced rapid seismic vulnerability assessments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".