Condition assessment tool for elements of drinking water treatment plant
Bibliographic record
Abstract
Condition assessment of aging assets and infrastructure is a growing concern in North America. The Canadian Water and Wastewater Association (CWWA) estimated that a $28 billion investment is needed for municipal water systems in Canada from 1997 to 2012. The condition of 43% of water supply systems in Canada is unacceptable. A systematic condition assessment is a pre-requisite of an effective asset and infrastructure management practice. The present study focuses on analyzing the principal factors that affect infrastructure condition, data requirement, and how the condition of a targeted element can be assessed. The main objectives of this research are to develop condition assessment models for selected existing elements of Drinking Water Treatment Plant (DWTP) and develop condition rating scales and a web-based tool. The condition of five DWTP elements are studied in this research: settling tank, filtration tank, chlorination tank, raw water pump and clean water pump. Condition rating (CR) models are designed using Analytical Hierarchy Process (AHP) and Multi Attribute Utility Theory (MAUT) approaches. Evaluation results show that the top ranked category for tanks is 'Physical (design and construction stage)' with a relative importance of 32% and for pumps is 'Operational' with an importance of 34%. The preference level of each parameter is being measured on a numerical scale of 0-10. Twenty five practical case studies are used to validate the developed models, which show robust results. Condition rating scales are developed for tanks and pumps and verified by municipal experts. A web-based condition rating tool is developed and coded with AHP.NET
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".