Operation, maintenance and rehabilitation techniques
Bibliographic record
Abstract
Abstract Urban drainage operation, management and rehabilitation can be divided into two distinct segments: traditional grey infrastructures (i.e. pipes and associated components) and green infrastructures. For piped systems this boils down to maintaining the operational safety, stability and tightness of the sewers and special structures. However, this chapter provides an overview on both realms and highlights that, while there is a lot of standardization for grey infrastructures, the knowledge on green ones is much more fractured. They are often composed of both engineered and natural elements such as pipes, flow control systems, vegetation, micro-organisms in the soil or growing media, and also deliver a broad range of beneficial services to our communities and their inhabitants. Existing terminology for pipe networks is adapted by defining a similar distinction for green infrastructures based on the severity of the necessary actions. There will be no focus on other special structures and machinery. Adopting these distinctions, this chapter consists of three parts: (1) pipe network operation and maintenance (O&M), (2) structural rehabilitation of pipe networks and the connected manholes and (3) green infrastructure rehabilitation including O&M focusing on some examples. Consequently, this chapter can be used as guidance on available technologies, existing guidelines and research gaps.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".