Risk Based Decision Making Tools for Sewer Infrastructure Management
Bibliographic record
Abstract
Wastewater utilities in the United States face an aging workforce, higher consumer expectations, stricter environmental regulations, security concerns, and an aging infrastructure.As a result, many utilities have turned to Asset Management for better decision making to prioritize their needs.According to numerous studies that were conducted in the past decade, most notably the USEPA's Clean Water and Drinking Water Infrastructure GAP Analysis Report and the ASCE Report Card, wastewater utilities will need to invest approximately 390 billion in capital infrastructure over the next two decades.Meanwhile, the field of Asset Management is emerging to improve the decision making process to renew, replace, or rehabilitate the nation's infrastructure.Asset management can be defined as set of activities, guidelines, and decision tools that seek to minimize the life cycle costs of capital and O&M spending while maintaining an acceptable minimum level of service (USEPA 2006).This research provides a road map for the implementation of asset management in wastewater utilities with a strong focus on the critical tools that are needed to identify, quantify, and manage risk associated with the structural failure of sewers.The two components of the Business Risk Exposure; namely the probability and consequences of failure were thoroughly evaluated.Criticality matrices for linear assets were developed using expert opinion.A GIS based criticality tool was developed to identify the most critical assets.The GIS model was developed to eliminate biases and establish a systematic methodology to quantify the impact of failure of an asset.Subsequently, maps were generated showing the critical sewers that the utility needs to focus its efforts on to reduce its risk exposure.Probability curves of sewer failure were developed using historical data extracted from repair history performed between 1997 and 2009.Closed Circuit Television (CCTV) condition assessment methodologies are the basis for the development of deterioration curves used by academics in the U.K., the U.S., Australia, and Canada.Condition based methodologies that are dependent of CCTV data are resource intensive and their output
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".