Structural Deterioration Modeling Using Variational Inference
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Integrity and risk assessment of structures and infrastructure systems includes the evaluation of deterioration processes such as corrosion, fatigue, and wear. Future deterioration is often estimated from imprecise inspection data using stochastic deterioration models. Bayesian inference for such models mostly relies on stochastic simulation techniques to generate samples from the posterior probability distributions of the unknown model variables. This paper introduces variational inference as an alternative to simulation methods to make deterioration models more suitable for large inspection data sets. Variational inference treats inference as an optimization problem in which the posterior probability distributions of interest are iteratively determined using an optimization function that is derived from the Kullback–Leibler divergence. The variational solution for a hierarchical stochastic deterioration model is derived based on a homogeneous stochastic gamma process and noisy inspection data. Two numerical examples are provided to demonstrate the accuracy of the results and the scalability of variational inference to large inspection data problems.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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