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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".