Prioritize Your Capital Spending: Make Informed Decisions Using Non-Destructive Acoustic Testing
Bibliographic record
Abstract
This paper aims to provide a high-level structure for asset managers to choose appropriate technologies. It will describe an example of where this structure was recently used and how it is going to be applied in subsequent decisions. The water service center (WSC) in Flint, Michigan has aging infrastructure and a limited capital budget. The current asset management program is reactive in nature, making it more challenging to spend WSC’s limited funds in the right place at the right time. Focusing on their linear assets (buried pipes), the WSC would like to improve its asset management program, to be able to determine which pipes need the most attention and come up with a rehabilitation or replacement plan based on their limited capital improvement budget. The method chosen by the WSC was to separate all data sets into different tiers of information, forming a pyramid. Each tier becomes progressively more detailed as higher resolution investigation methods are applied. Higher resolution and more detail come at a cost because these types of investigations are generally more expensive per unit length, can be invasive and might require disruption of pipe service. The philosophy behind the pyramid approach is to use the lower tiers to find system-wide trends and identify mains that have high risk and consequence of failure. Subsequent tiers are used to improve confidence and resolution in the data. It is recommended that higher tier technologies are only applied if the information from the previous tier suggests that additional testing is needed or if more information is required to make an informed decision.
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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 | 0.024 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".