Testing and Evaluation of Reclaimed Materials as Aggregate for OPSS Granular B Type II
Bibliographic record
Abstract
In urbanized regions of Ontario, the road construction industry faces a number of challenges due to the growing scarcity of locally-sourced natural aggregate materials and increased restrictions on the approval and development of new aggregate extraction sites. In an effort to maintain sustainable, efficient and economical sources of construction aggregates, companies are increasingly seeking to supplement or replace natural aggregates with available artificial materials such as crushed reclaimed concrete aggregate (RCA) and reclaimed asphalt pavement (RAP). Currently, Ontario Provincial Standard Specification (OPSS) 1010 permits the use of processed reclaimed construction materials in a variety of road base, subbase and asphaltic concrete layers, with the exception of Granular B Type II, which is a higher-performance subbase specification that solely allows primary materials produced from crushed bedrock. Consequently, there is a need to better understand the performance of reclaimed materials as alternative aggregates in Granular B Type II. This paper focuses on a laboratory testing program which examined five different aggregate blends conforming to Granular B Type II gradation requirements which vary in composition from 100% natural crushed rock to 100% processed reclaimed material. The granular materials in the study were sampled before and after construction of test pads at two job sites in Ontario. These samples were analyzed via sieve gradation, standard and modified Proctor, permeability, California Bearing Ratio (CBR) and resilient modulus (MR) test procedures. The testing results indicate that RCA and RAP can be successfully utilized as aggregate materials in Granular B Type II subbase applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".