A Predictive Settlement Modeling Framework Employing Thermal–Hydraulic–Mechanical–Biochemical Processes in Municipal Solid Waste Landfills
Bibliographic record
Abstract
Municipal solid waste (MSW) landfills using leachate recirculation optimize the waste stabilization process by providing nutrition and moisture for biological activity. However, the leachate recirculation creates an environment with complex processes due to accelerated biodegradation and generated heat. Additionally, different timing in waste placement and the heterogeneous nature of MSW cause significant variations in properties throughout the intercalated layers. In this study, a settlement framework model employing Thermal–Hydraulic–Mechanical–Biochemical processes is proposed, which considers multiple MSW properties including temperature, pH, and saturation. The framework model includes a modified Cam-clay model to simulate short-term settlement and adopts mechanical and biological creep models for long-term settlement estimation. A long-term biological creep model that uses a single decay rate constant is revised to account for environmental factors such as temperature, pH, and saturation in estimating MSW decay rates. The framework model was calibrated using the data of large-scale column experiments, which were conducted at different temperatures and saturation conditions considering biodegradation rates. Also, an MSW placement strategy was developed to consider the effect of different waste layer placement timing in the progression of MSW landfill total settlement. The modeling framework was validated using settlement data from a landfill in Canada. The results showed that temperature and saturation have a significant influence on MSW settlement and therefore should be considered in MSW landfill settlement prediction models.
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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 | 0.001 | 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 | 0.001 | 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".