Modelling of hydraulic impacts arising from wipe-caused blockages in sewers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sewer networks face significant challenges from blockages caused by fats, oils, grease, tree roots and non-biodegradable items like wet wipes. Increased flushing of wipes exacerbates blockages, while the hydraulic impacts of wipe accumulation and methods for modelling them in sewer remain underexplored. This study addresses this gap by simulating wipe accumulation in sewer defects under varying flow rates and blockage sizes. Results demonstrated that upstream water levels consistently increased as blockages grew. These hydraulic effects were modelled in the Storm Water Management Model (SWMM) using four methods: adjusting Manning’s roughness coefficient, filling the pipe, modifying the head-loss coefficient and incorporating an orifice. The simulation results quantified the dependency of model parameters on both flow rate and blockage size. This research provides practical guidance on simulating wipe-caused blockages, enabling municipal water utilities to assess surcharge risk, model capacity loss and enable targeted inspection and maintenance in existing sewer asset management plans.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it