Enhancing an existing composite model to estimate MSW settlement during the filling and post-closure phases in both warm and cold regions
Bibliographic record
Abstract
The settlement of municipal solid waste (MSW) is a critical factor in estimating landfill airspace capacity before final cover placement. While numerical modelling has improved MSW settlement predictions, such methods often require significant computational resources and specialized expertise, making them less practical for field applications. Existing closed-form expressions also fail to adequately capture MSW's increasing resistance to compressibility under higher stress levels (e.g., successive MSW lift placements) and the delay in biodegradation-induced settlement under freezing temperatures. This study enhances an existing composite settlement model to address these limitations. The improved model refines the mechanical creep component by modifying the creep coefficient to incorporate the effects of increased compressibility resistance under higher overburden stress. To better represent biodegradation-induced settlement, a delay time parameter was introduced. This parameter was derived by first generating a series of degree of degradation (DOD) curves using a validated thermal-mechanical-biological (TMB) model. These curves, which characterize biodegradation progression under different temperature conditions, were then fitted using a first-order decay equation that incorporates the newly introduced delay time. By integrating this parameter, the enhanced model accounts for the lag in biodegradation-induced settlement when MSW temperatures are not favourable for microbial activity. The model was validated by comparing its predicted settlement with recorded field data from the Ste. Sophie Landfill. Its effectiveness was further demonstrated by comparing the enhanced model's predictions to those of the original model, showing substantial improvements in accuracy.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".