Frost Heave Laboratory Investigation on Crushed Rock Aggregates
Bibliographic record
Abstract
Crushed rock aggregates are widely used for transport infrastructure construction in Norway. During winters 2009/10 and 2010/11, differential frost heave severely affected the Norwegian transport network. Currently there is no system in the Norwegian road design to calculate expected frost heave from sub-grade and crushed rock aggregates. The segregation potential (SP) can be used to estimate heaving according to climatic data with the SSR model. Consequently, the frost protection of roads and railways project was created in part to improve knowledge about the frost susceptibility sensibility of crushed rock aggregates. The goals of this paper are to a) introduce the Norwegian University of Science and Technology (NTNU) freezing cell apparatus and laboratory methodology, b) present soil characterization and SP results of 7 different rock types, and c) discuss SP relationship with fine fraction content and mineralogy. A 150 mm diameter by 200 mm high multi-ring frost heave apparatus was used to perform the tests. Samples were frozen from top and hydraulic pressure other than cryosuction was avoided. Temperatures, heaving rate and magnitude, and water mass were recorded for the 96 hours duration of each test. A wide variety of rocks, including granitic gneiss, gneiss, anortosite, granodiorite, slate, gabbro, gneiss, and porphyr, was chosen. Fine fraction <63, <20, and <2 μm varies from 12.5% to 25.6%, 7.1% to 14.8%, and 0.53% to 2.45% respectively. The SPs varied from 60 to 197 mm2/°C·d. SP results are in accordance with value range showed by Konrad (2005), Konrad and Lemieux (2005), and Nurmikolu (2005) for granitic aggregates. SPs for the other rock type are proposed to be used as reference values. The SP as a function of fine content <2 μm calculated from the <80 μm fraction showed a trend of R2=0.84 for this study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".