Strategies for One Dimensional (1D) Compression Testing of Large-Particle-Sized Tire Derived Aggregate
Bibliographic record
Abstract
Abstract Laboratory testing of a mass of large-particle-sized tire derived aggregate (TDA) to assess performance-related properties such as void ratio, compressive creep, and hydraulic conductivity under large loads poses a number of experimental challenges. Large-particle-sized TDA is shredded scrap tires with particle sizes from 50 mm to over 305 mm. The large particle size of the TDA mass results in experimental challenges, such as the need for a large test chamber and the need for a load application system with a capacity to apply and sustain large loads, while accommodating large vertical displacements from the compression of the TDA mass. As an example, to put these requirements into perspective, a mass of TDA with a nominal particle size of 150 mm requires a test cell diameter of at least 600 mm and preferably a diameter of 700 mm. If a load of 400 kPa were to be applied onto the TDA mass to simulate approximately 35 m to 40 m of overlying material (waste and routinely applied cover materials) in an application such as a landfill, the test apparatus must be capable of delivering over 150 kN of applied load. Furthermore, for a reasonable initial mass of TDA that is 1.2 m thick, the test cell will have to be designed to maintain that load over 0.6 m of vertical displacement because of the compression of the TDA mass. This article presents a number of practical strategies that were implemented to overcome the experimental challenges with testing large particle size, highly compressible TDA mass to establish the performance related properties for use in service. In some instances, components of the test equipment had to be re-engineered to accommodate exigencies that had not been anticipated, such as differential compression of the TDA mass. The focus of this article is on equipment design and experimental methodologies. A few sample results from the study are presented to illustrate the successful implementation of the design methodologies. Although TDA has been studied in this work, the strategies described herein can be applied to a wide range of highly compressible materials under large loads.
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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 | 0.002 |
| 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".