Verification of Pervious Concrete Drainage Characteristics Using Instrumentation
Bibliographic record
Abstract
Pervious concrete pavement is a Low Impact Development pavement alternative. In a Canada-wide study of the performance of pervious concrete pavement by the University of Waterloo, Cement Association of Canada and industry members field sites were constructed and laboratory testing was completed. Subsurface instrumentation was included in some of the pavement structures. Moisture gauges, commonly used for agricultural applications, were placed at various depths throughout the pavement structure of field sites. The permeability of the pervious concrete pavement was measured on the surface throughout the more than two year evaluation period. The data collected from the instrumentation was used to verify the assumed drainage characteristics of pervious concrete pavement. On-site and Environment Canada weather data was combined with the moisture gauge data to track the movement of moisture through the pervious concrete pavement structures. Within this research a method was developed to analyze and interpret the moisture gauge data. The analysis of the collected instrumentation data verified the assumed, effective, drainage characteristics of pervious concrete pavement. This paper discusses one of the field sites that was instrumented during construction. The data analysis process developed in this research is presented in the paper. The analyzed data will be used to verify the assumed drainage characteristics of pervious concrete pavement. The data and findings presented in the paper provide information not previously available regarding the subsurface drainage of pervious concrete pavement. The results of this research can be used in refining pervious concrete pavement designs in the future. For the covering abstract of this conference see ITRD record number 201310RT334E.
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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 | 0.001 | 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) | 0.002 | 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".