Building information modeling utilization for optimizing milling quantity and hot mix asphalt pavement overlay quality
Bibliographic record
Abstract
An approach to a practice paving technique using building information modeling (BIM) was developed. When planning hot mix asphalt (HMA) overlay on a concrete slab, in-advance paving simulations can help to preemptively evaluate pavement quality, such as HMA thickness, and prevent excessive HMA quantity. The BIM technique has the capabilities of ‘in-advance simulation’, ‘3-D visualization’, ‘interference identification’, and ‘quantification’. Building information modeling could be successfully implemented to optimize milling quantity and improve HMA pavement quality in an actual paving project. Based on the established BIM model, alternative paving levels were derived and paving sequences were simulated. Through 3-D visualized images, locations where HMA thickness was inadequate could be effectively identified. Quantified information for simulation results enabled optimization of milling and paving options. Milling was selectively conducted for the identified undulations. The cost was reduced by approximately 12%. Paving thickness and density had coefficients of variation (CV) of approximately 15% and 0.2%, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".