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Record W4401628887 · doi:10.22215/etd/2024-16090

Analysis and Modelling of Settlement and Temperature Data from an Operational Landfill in Ste. Sophie, Québec, Canada

2024· dissertation· en· W4401628887 on OpenAlexaffabout
Wameed Alghazali

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsSettlement (finance)Municipal solid wasteMultiphysicsEnvironmental scienceGeotechnical engineeringPore water pressureBiodegradationHeat transferWaste managementCreepEnvironmental engineeringEngineeringMaterials scienceChemistryStructural engineering

Abstract

fetched live from OpenAlex

The settlement of Municipal Solid Waste (MSW) is an important factor that landfill operators need to estimate, particularly during the filling stages, to maximize airspace capacity.In landfills located in northern climates, MSW undergoes partial freezing when placed at the curbside during the winter months causing a delay in biological processes and the resulting biodegradationinduced settlement.To investigate the settlement of MSW in northern climates, a 12-year field study was conducted at the Ste.Sophie Landfill in Québec, Canada.Field data, including settlement and temperature, were collected during the filling and post-closure phases from 12 instrument bundles that were placed at varying depths.The collected data showed resistance to compressibility with an increase in overburden pressure.The data also indicated that waste lifts placed under freezing conditions remained at sub-zero temperatures for 12-18 months causing a delay in the biodegradation-induced settlement.To model the observed MSW settlement and temperature in the field, a Thermal-Mechanical-Biological (TMB) model was developed in COMSOL Multiphysics.The model integrated a Generalized Kelvin-Voigt (GKV) model to simulate the instantaneous and mechanical creep settlements.The modulus of elasticity of the springs was expressed as a function of the applied stress to account for increased resistance due to the accumulation of waste lifts.Biodegradation-induced settlement was represented as the ratio of waste expended energy over time to its total potential expended energy, using an established heat generation model.The thermal model simulated heat transfer through conduction and included a biodegradation heat generation source term.The proposed TMB model effectively predicted the settlement and temperature at the Ste.Sophie Landfill. IIIThe suitability and effectiveness of common models in literature for predicting MSW settlement were evaluated using data from the Ste.Sophie Landfill.This evaluation included models based on a primary/secondary compression ratio, Modified Cam-Clay (MCC) model, rheological model, and first-order decay model.A simple one-dimensional equation was proposed to assist practitioners in estimating MSW settlement.In this equation, instantaneous, mechanical creep, and biodegradation-induced settlements were expressed by a primary compression ratio, modified version of a reported rheological model, and modified version of a reported first-order decay equation, respectively.

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)0.001
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 score0.037
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.249
Teacher spread0.232 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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