Value of Information-Based Inspection Planning using a Population Approach
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
<p>A value-of-information analysis is proposed that is tailored toward the inspection planning of damaged and deteriorating engineering systems where defect-specific matching and individual deterioration growth analysis are not computationally feasible. Defects and/or damage are treated using a population perspective with a probabilistic formulation of the evolution of the number of defects and the extent of deterioration rather than the modelling of individual defects. Measurement errors associated with inspection such as detectability, false calls, and sizing errors are considered to relate the observed to the actual state of structural deterioration. The population- based deterioration model is embedded in a Bayesian pre-posterior analysis to assess the value of information that is achieved by performing an inspection. Applications include the inspection planning of critical components of structural systems such as bridges, tunnels, pipelines, and offshore structures. The proposed analysis is illustrated using a numerical example where the long- term cost benefit of implementing an inspection scheme is determined.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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