A decision support system for rehabilitation of sewer pipes
Bibliographic record
Abstract
The condition of sewer pipes in North America has severely deteriorated, over the last few decades, creating a need for rehabilitation. Sewer rehabilitation methods are numerous and are constantly being developed, benefiting from emerging technologies. The implementation of these methods is driven by the need to improve quality and to reduce cost and project duration. One of the rapidly expanding fields in the sewer rehabilitation industry is trenchless technology. Due to the large number of methods associated with emerging new technologies in this field, selecting the most suitable method can be a challenging task. Selection in this environment, without a computerized tool, may also suffer from the limited knowledge and (or) experience of the decision-maker and could result in overlooking some of the suitable methods that could do the job at less cost. This paper describes a recently developed system for rehabilitation of concrete and clay sewer pipes and focuses primarily on two of its components: (i) the database management system (DBMS) and (ii) the decision support system (DSS). The system can assist municipal engineers and contractors in selecting the most suitable trenchless rehabilitation technique that specifies job conditions and user requirements. An example application is presented to demonstrate the use and capabilities of the developed system.Key words: pipe defects, rehabilitation, sewer pipes, database management systems, decision support systems, multi-attribute utility theory.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".