Collection and Compilation of Water Pipeline Field Performance Data
Bibliographic record
Abstract
The objective of this project is to collect, compile and analyze high quality field data on pipeline reliability performance for water pipelines of different materials. This project will be completed in two phases: Phase I will include the data collection from the United States Bureau of Reclamation (USBR) and their 17 Western States, as well as the U.S., Canadian, and Australian Water Utilities for different materials including cast iron, ductile iron, reinforced concrete, steel, pre-stressed concrete, PVC, AC, and others. Canadian and Australian utilities’ data and knowledge will be stored as a subset of the national pipeline database. Phase I will also include the development of a Web-based, GIS-enabled water pipelines database to aggregate and standardize the collected data, and further to identify the missing data deemed critical to understanding water pipeline performance. These tasks will be geared to meet the Phase II objectives of collecting and/or generating the missing data, and the establishment of an understanding of the general state of buried water pipeline infrastructure, failure rates, general effectiveness of corrosion control measures, and the calculation of failure rates for each water pipeline material. Virginia Tech will protect data and database as per federal requirements under export-import control law. The proposed research has the following five objectives: Developing a standardized data and metadata structure for verifying, storing, updating, retrieving, and exporting/importing water pipeline infrastructure attributes; Creating a GIS-driven Web-based platform for developing, analyzing, validating, implementing, and benchmarking of models and tools for pipeline asset management; Piloting of PIPEiD with utilities of various sizes across the U.S. for demonstration; Establishing a protocol for water pipeline data security, sanitization, and publication; and Developing a plan to promote and sustain effort by engaging the water industry. This paper presents the national effort for water pipeline data collection and compilation methodologies. This project will determine the state of the knowledge in water utilities, other industries, and large Internet of Things (IoT) firms to lay the groundwork to identify key issues that can be addressed by data collection and big data analytics for water pipeline infrastructure systems.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".