Application of a hyperbolic model to municipal solid waste
Bibliographic record
Abstract
An understanding of the stress–strain behaviour of landfilled waste is important in landfill design. Based on evaluation of numerous strain–strain curves obtained from triaxial compression testing of samples of municipal solid waste (MSW) ranging from fresh to degraded, it is proposed that the stress–strain behaviour of MSW under loading follows a non-linear elastic (hyperbolic) model. The hyperbolic model is typically described using σ ult and E, but can also be described using c′ and φ′ in conjunction with three other parameters (K, n and R f ). The use of the parameters K, n and R f to describe the stress–strain behaviour may be particularly useful for MSW, because it is often difficult to obtain values of σ ult and E. Stress–strain data from 15 datasets comprising 57 individual triaxial tests have been compiled. These data originated both from published studies and from the authors' own laboratory testing of large intact and recompacted samples of MSW from three different landfills in Canada. Best-fit hyperbolic model parameters K, n and R f were determined for 12 of these datasets (comprising 50 individual triaxial tests), and upper- and lower-bound values of K, n and R f were determined for the 90% confidence level. By using these upper- and lower-bound values, the five-parameter hyperbolic model was effectively reduced to a two-parameter (c′–φ′) model. It is proposed that the resulting lower and upper bounds of the stress–strain curves plotted using the proposed lower and upper bounds of K, n and R f in conjunction with site-specific c′–φ′ values can reasonably embrace the experimental strain–strain curves up to an axial strain of 20%, even given the significant variability of the waste samples considered. This approach is given further credence as it yields a reasonable prediction of the stress–strain behaviour when compared with the last three datasets (comprising seven individual triaxial tests), which had not been included in the statistical analysis.
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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 | 0.000 |
| 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".