Principles and guidelines of deterioration modelling for water and waste water assets
Bibliographic record
Abstract
Deterioration modelling is an important analytical component in risk-informed infrastructure asset management. Many asset managers find it very challenging because of its technicality, paucity of deterioration data and difficulty in model selection. Traditional approaches emphasised the mean deterioration trend and heeded too little the characterisation of uncertainty involved. This paper attempts to revert this trend and bring stochastic deterioration modelling back to focus. Following a systems approach, the author argues that deterioration modelling involves not only the data-driven process that asset managers have traditionally perceived, but also a system analysis that carries the empirical deterioration modelling at the level of performance data up to the level of the performance hierarchy at which decisions are made. In addition, deterioration modelling is an important and integral component of risk analysis, and therefore, the characterisation and quantification of aleatory uncertainty and epistemic uncertainty become an essential component of deterioration modelling. Moreover, deterioration data include not only hard data collected from inspection and condition assessment, but also soft data that can be gleaned from expert opinions, design manuals and professional judgements. Although mainly for water and waste water assets, the principles, guidelines and model selection flow chart are equally applicable to other infrastructure assets.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".