A Prediction Model to Cost-Optimize Clean-Out of Permeable Interlocking Concrete Pavers
Bibliographic record
Abstract
Permeable Interlocking Concrete Paver (PICP) systems provide onsite stormwater management by detaining runoff and removing contaminants. However, a major problem with PICPs is the significant maintenance cost associated with their clean-out to restore the original functionality, which discourages landowners and municipalities from adopting the systems. A combination of laboratory experiments and machine-learning techniques are applied to address this challenge. A total of 376 laboratory experiments were conducted to investigate four independent variables (cleaning equipment speed over the pavement, air speed in the cleaning jets, top opening width of the cupule, and filter media gradation) that affect the cleaning of PICPs. The Buckingham Pi-Theorem was used to express the four main input variables in three dimension-less parameters. This current investigation provides a novel understanding of variables affecting the sustainable and economically feasible maintenance of PICPs. A new model is derived to more accurately predict the percentage of mass removal from PICPs during clean-out using a machine-learning technique. The Group Method of Data Handling (GMDH) model exhibits high performance, with a correlation coefficient (R2) of 0.87 for both the training and testing stages. The established simple explicit equation can be applied to optimize the maintenance costs for industrial applications of Regenerative Air Street Sweepers for sustainable and cost-effective PICP maintenance. Pavements with larger surface areas are found to have lower maintenance costs ($/m2/year) compared to the ones with smaller surface areas. This study estimates $0.32/m2/year and $0.50/m2/year to maintain pavements with larger (5000 m2) and smaller (1000 m2) surface areas, respectively.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".