Identification of Invariant Average Weighted Haul Distance to Simplify Earthmoving Simulation Modeling in Planning Site Grading Operations
Bibliographic record
Abstract
This research is intended to generate relevant and quantitative decision support based on limited data and information available in the context of planning earthmoving operations at the site-grading design or early project planning stage. The researchers apply and extend the concept of haul effort in a two-axis grid. This enables calculation of the average weighted haul distance, which is shown to be invariant by conducting simulation experiments using a heuristic algorithm and making a quantitative comparison to the center of mass for a rigid body. This research applies the discrete event simulation approach commonly used to model the handling of multiple earthmoving jobs in a grading site, each job having a particular haul distance. The significant and unique contribution that has been made in this research is to substitute one unique average weighted haul distance for multiple haul distances between numerous cut and fill cells in the site grid system as simulation modeling input. When the same fleet is applied in both detailed and simplified simulation models, the question of validation concerns whether the simplified model still maintains the accuracy of the detailed model. Using a simple test problem and a real-world case, the researchers validate the proposed method and demonstrate the benefits of applying simulation for construction planning without incurring the previously necessary and prohibitively high cost associated with constructing detailed simulation models.
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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".