Integrating Sewer Inspection Data into SWMM Model Calibration
Bibliographic record
Abstract
Developing and calibrating a large scale SWMM model in coordination with sewer cleaning and CCTV inspection can be a challenging, labor-intensive and time-consuming task.The common model calibration approach on closed conduit hydraulics involves estimating roughness coefficients and sediment depths throughout the sewer system, when detailed sewer condition parameters are available from CCTV inspections.If those conditions are changing throughout the calibration period due to sewer cleaning, this task becomes more complicated.For the City of Columbus's Livingston/James Sewer System I/I Remediation Project, the project team developed a comprehensive database application tool to integrate the condition parameters from 130 miles of sewer inspection into the model calibration process.The project's SWMM EXTRAN model contains 2,900-conduits ranging from 8 in.(203 mm) to 102 in.(2590 mm) in diameter.This tool takes defects recorded for each of the sewer segments being inspected, and relates them to appropriate Manning's roughness coefficients and/or sediment depths used in the calibration process.For sewers with multiple defects, the defects are rated and the calibration is based on the most severe defects.The time that pipe cleaning was performed is also taken into account because three calibration
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".