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Record W2805243121 · doi:10.7939/r34t6f813

Sediment Study in Storm Sewer Catchbasins and Submerged Pipes

2016· article· en· W2805243121 on OpenAlexaboutno aff
Yangbo Tang

Bibliographic record

VenueUniversity of Alberta Library · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsStormHydrology (agriculture)SedimentCombined sewerSanitary sewerStormwaterGeologyEnvironmental scienceOceanographyGeotechnical engineeringSurface runoffGeomorphologyEnvironmental engineering

Abstract

fetched live from OpenAlex

Studying sediment in storm sewer systems is important for their operation and design of storm sewer systems. This thesis presents a literature review on the existing work on sediment in storm sewer and the current results of experimental studies on sediment motion and settlement in storm sewer catchbasins and submerged pipes. Sediment may pollute downstream water, adversely impacting aquatic life, source waters for drinking water supplies, and recreational uses. Sedimentation in sewer pipes may cause sewer blockage problems, reduce the flow area and cause surcharged flows and urban flooding. Storm sewer sediment characteristics reported in the literature include storm sewer sediment sources, classification based on sediment sizes, particle median size surveys, particle size distribution investigations, particle settling velocity calculations, and the particle pollution potentials. During the rainfall, the sediment moves from catchment surface into storm sewer catchbasins, and then enters storm sewer pipes. Thus, related literature on sediment loading estimation and sediment movement in sewer pipes is included. A number of factors need to be considered in estimating sediment loading: sediment buildup, rainfall intensity, rainfall energy, runoff rate, sediment sizes, and land surface characteristics. Sediment movement includes three parts: erosion, transport and deposition. In terms of sediment blockage problems, sediment critical erosion velocity and sediment self-cleansing velocity are discussed in detail. Also, a collection of different experiments about sediment movement in storm sewers is presented. A laboratory experiment was conducted on catchbasins to predict sediment removal efficiency under different conditions (including flow rate, particle size, and inlet control device). For Calgary’s catchbasins, particles with d50 of 1800 µm can be easily captured, while smaller particles of 62 µm d50 can be easily flushed out of catchbasins, even at low flow discharge. An equation is developed for predicting sediment capture efficiency in a catchbasin for different particle sizes and flow rates. The proposed equation can be used for catchbasin design. Consequently, a laboratory experiment about sediment movement in submerged pipes was completed. According to observations, the development of deposition appears to have two stages: the rapid developing stage (sand deposit grows both in height and in length directions), and the equilibrium developing stage (sand deposit only grows in the length direction). With respect to sediment transport capacity, it can be described by an equation consisting of a sediment transport parameter, bed shear intensity, and relative bed thickness.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.990
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.158
Teacher spread0.151 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2016
Admission routes1
Has abstractyes

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