Development of Design System for Permeable Interlocking Concrete Pavement
Bibliographic record
Abstract
National, state/provincial and municipal legislation regulating stormwater runoff in the United States and Canada has provided increased incentives for using permeable pavements. In addition, regulatory frameworks for implementation of sustainable design have embraced permeable pavement solutions. These regulations are often called low impact development (LID) or sustainable urban drainage systems (SUDS). A logical and technically sound design process using design software can support design professionals and help permeable pavement achieve full potential in North America. In 2008, the Interlocking Concrete Pavement Institute (ICPI) introduced a non-proprietary software program called Permeable Design Pro that integrates hydrological and structural design solutions for permeable interlocking concrete pavement (PICP). The hydrological analysis determines if the volume of water from user-selected rainfall events can be stored and released by the pavement structure. User defined parameters determine how much water infiltrates the subgrade, infiltrates into the soil subgrade, enters pipe subdrains or flows from the pavement surface. The structural capacity of PICP is determined using the American Association of State Highway and Transportation Officials (AASHTO) 1993 structural design equations for base/subbase thickness to support vehicular traffic. This paper describes the development of the structural and hydrological design methodology with an example of its use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".