Success story of RCC for Heavy Loaded Pavement at Port of Montreal’s Container Terminals
Bibliographic record
Abstract
At the port of Montreal, port pavements, particularly those of container terminals, are literally bombed with various types of loadings, such as gantry cranes, forklifts or front-end loaders, non‑standard single-axle trucks and conventional trucks. The typical features of this transhipment equipment are such that it can generate significant loads and stresses in pavement structures. Indeed, wheel loads for some gantry cranes can even reach 63.7 T per wheel, which is definitely higher than the usual conventional truck load, usually a maximum of 5 tons per dual wheels. This communication will first set out the significance of such equipment and its influence on pavements, and how container terminals design methods have evolved over the past decades. Subsequently, the sustainable pavement concept, which has been in use these past years, will be presented, as well as how the design methodology has evolved through the design development. These composite pavements, which consist of RCC slabs overlaid with bituminous concrete, were developed from past experience with RCC slabs, in order to reduce maintenance requirements for transhipment area operators. The communication will also show how the methods and specifications used to construct these pavements have evolved over the past years.
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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 | 0.001 | 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 | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".