Value of information-based decision analysis of the optimal next inspection type for deteriorating structural systems
Bibliographic record
Abstract
Deteriorating infrastructure systems require inspections and maintenance to ensure safe operation. System operators are often required to decide the optimal type of inspection to perform, where some inspections are of higher accuracy, and correspondingly higher cost. Life cycle analysis is typically used to determine the optimal inspection type. While life cycle analysis is effective at determining the optimal inspection type, it is also inefficient, requiring analysis of the entire decision sequence throughout the system life cycle. This paper presents an efficient methodology to approximate the decision of the optimal next inspection type without performing a life cycle analysis. This methodology determines the range of the value of information provided by only the next inspection. When the inspection cost is outside the range of the value of information then this method yields the decision of which inspection type to choose, negating the need for life cycle analysis. When the inspection cost for some inspection types lies within the bounds then a subsequent life cycle analysis is required, but perhaps some inspection types can be eliminated, simplifying the life cycle analysis. Thus, this method is complimentary to life cycle analysis, functioning as a quick preliminary assessment. The methodology is demonstrated through a numerical example of a corroding pipeline.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".