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Record W4409887531 · doi:10.2166/wst.2025.057

Quantifying sediment deposition in sewers through hydraulic performance analysis

2025· article· en· W4409887531 on OpenAlexaff
Zhi Yang, Biao Huang, David Z. Zhu

Bibliographic record

VenueWater Science & Technology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsUniversity of Alberta
FundersNational Key Research and Development Program of China
KeywordsDeposition (geology)SedimentFlow (mathematics)Environmental scienceVolumetric flow rateSanitary sewerHydrology (agriculture)Range (aeronautics)GeologyGeotechnical engineeringEnvironmental engineeringEngineeringMechanicsGeomorphologyGeometryMathematics

Abstract

fetched live from OpenAlex

This study investigates the effects of fixed sediment deposition on the hydraulic characteristics of sewer flow to support the diagnosis of sewer blockage. Sediment beds extending over the entire pipe (i.e., continuous deposition) and localized deposits were examined under different flow rates and outlet control conditions. Continuous deposition changes the cross-sectional area of the sewer pipe, while localized deposits act similarly to short bottom obstructions. The energy losses induced by a localized deposit at various locations were found to be nearly identical, particularly in cases with backwater effects. To illustrate the relationships between the flow rate, water level, and deposit characteristics, hydraulic performance curves can be developed. The inverse problem, which involves estimating parameters characterizing sediment deposition using observed flow rates and upstream and downstream water levels, can be solved by matching hydraulic performance curves with numerous scatter points from actual monitoring data to obtain the best fit. As there is a wide range of sediment deposition patterns that result in the same overall energy loss, the concept of equivalent sediment bed height is introduced to be applied in real-world scenarios.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.008
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.250
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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