Effect of seasonal climatic variance on water main failure frequencies in moderate climate regions
Bibliographic record
Abstract
The yearly water main failure frequency is a central performance indicator to describe the structural quality of a water distribution network. Besides age related deterioration, events such as severe climatic conditions or intensified third party construction may cause sudden seasonal increases in failure frequency trends. For the cold regions of Canada and the dry regions of Australia, several studies exist describing the impacts of climatic failure frequencies on water main failure variations. Failure prediction modelling applied to Austrian supply systems have shown that irregularities in overall failure trends were not explainable with commonly used model covariates like material, vintage, diameter or the number of previous breaks. Analysing the monthly failure frequencies of several Austrian utilities, seasonal differences and variations in failure frequencies are recognizable. The research described in this paper therefore focused on analysing if climatic impacts are responsible for these variations. In a first step, climatic indicators, which are able to describe seasonal climatic variations in moderate climate regions, were derived. In a next step the correlation between summer and winter failure frequencies to these climatic indicators was analysed. The indicators taken into account were, e.g. the decisive freezing index (DFI), the summer rain deficit (RDs) or the amount of successive hot days (AHD). The research has shown that in all investigated climatic zones of Austria, the severity of the winter season influences failure frequencies. A dependency between winter failure frequency and the DFI was significant especially for rigid material types and for diameters up to 200 mm. So far, soil moisture effects have only shown a slight significance. Nevertheless, the indicator AHD has shown a correlation to failure frequencies in the dryer climate zones of Austria. This is of further interest as it is very likely that the AHD is going to increase due to climate change.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".