Bayesian neural networks for large-scale infrastructure deterioration models
Bibliographic record
Abstract
State-space models (SSM) have been shown to be effective at modelling structural deterioration of transportation infrastructure based on visual inspections. The SSM approach was recently coupled with kernel regression (KR) to include structural attributes like age and location in the deterioration analysis to share information between similar structures. However, the existing SSM-KR method suffers from two major drawbacks: 1) it can only use a limited number of structural attributes and 2) it requires significant computational time and resources. This paper proposes a new method, titled SSM-TAGI, that uses a Bayesian neural network instead of KR for extracting information from structural attributes. The new SSM-TAGI approach is compared against SSM-KR using visual inspection data and structural attributes from a network of bridges in Canada. The new SSM-TAGI approach is shown to reduce the computational time by two orders of magnitude while maintaining comparable performance as measured by the test-set log-likelihood. SSM-TAGI also seamlessly incorporates additional structural attributes and does not require extensive preparation, making it better suited for modelling infrastructure deterioration based on visual inspections on a large scale.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".