From condition-based to service-based strategies
Bibliographic record
Abstract
Abstract Condition assessment often serves as the primary, and at times, the exclusive factor driving the prioritization of rehabilitation requirements. While this methodology signifies a substantial departure from a ‘run-to-failure’ strategy, it does possess inherent limitations. Notably, it has been demonstrated to exhibit constrained efficiency, potentially resulting in the refurbishment of assets with minimal risks or inconsequential impacts. An evolved and more sophisticated perspective on asset management involves a comprehensive evaluation of the functions delivered by infrastructure elements, whether they pertain to drainage pipes or other stormwater control measures. By harmonizing the state of assets with the contextual stakes and vulnerabilities within the specific territory or region under the purview of the utility manager responsible for the upkeep of the drainage network, a more precise targeting of rehabilitation necessities can be achieved. This precision, in turn, culminates in a notable enhancement of the system's overall performance. This holistic approach, commonly referred to as a risk-based strategy, furnishes an inclusive framework for optimizing location strategies. This optimization hinges on the prioritization of rehabilitation requisites through a meticulous multi-criteria analysis. This chapter delves into the foundational functionalities inherent in urban drainage systems, coupled with their associated services. Subsequently, the succeeding section elucidates the shift from a condition-centred methodology to a performance-centric approach. A series of illustrative case studies follow, providing real-world context to the concepts discussed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".