Asset Management of Urban Drainage Systems
Bibliographic record
Abstract
Abstract Asset management issues are and will always be key concerns for many stakeholders in the water sector. Despite this, there is still a lack of awareness and clear guidance on the topic. There has been some focus on the management of drainage pipes, but more effort needs to be dedicated to examining the various regulations, practices, and research within this discipline. It's paramount to consider the long-term management of urban drainage assets, given the role they play in ensuring the wellbeing of our communities. Asset Management of Urban Drainage Systems is the first comprehensive handbook that deals with the asset management of infrastructure dedicated to both sewage and stormwater, including blue-green infrastructure. It gives an insight into the theoretical background of asset management itself and showcases regulations and legislation influencing it. The methods used to investigate the condition of assets, and how they can be modelled and represented while accounting for the associated limitations, are also presented. The book describes how the discipline can move from a purely condition-based approach to a service-based one using risk-management strategies, seen in the broader context of decision-making. Data management and techniques for the rehabilitation of urban drainage assets are also explored. From technicians who want to know more about the tools and methods, to researchers and students who want a broad overview, to professionals who are tasked with developing short, medium, and long-term asset management strategies, this book provides important content for a wide audience. ISBN: 9781789063042 (paperback) ISBN: 9781789063059 (eBook) ISBN: 9781789063066 (ePub)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".