Advanced Water Main Deterioration Model Using Bayesian Geoadditive Quantile Regression
Bibliographic record
Abstract
Abstract Once installed, water pipes “condition degrade” until failure. Utility managers anticipate the failure of water mains through a proactive management of urban water systems. In proactive management, deterioration models are used to predict the actual condition of uninspected pipes and forecast the future condition of all the pipes in the network. For an efficient planning, these models' predictions should be as close as the observed conditions of pipes during inspections. High‐degree polynomials can capture the complexity of water pipes deterioration process. However, these polynomials wiggly estimate such relationships and are unsatisfactory in some regions where they fail to fit the observed data. Flexible regression techniques that enable automatic data‐driven estimation of nonlinear relations between covariates and response constitute an alternative approach that can represent the stochastic deterioration process. In this article, a semiparametric deterioration model based on geoadditive quantile regression with smooth nonlinear function estimation of the effects of continuous covariates is proposed. The results confirm the nonlinear assumption of the continuous covariates and highlight factors contributing to the appearance of extreme values in the response variables. Maps representing the effects of the unobserved covariates on the pipes breakage rate are also produced.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".