Optimizing Earthmoving Job Planning Based on Evaluation of Temporary Haul Road Networks Design for Mass Earthworks Projects
Bibliographic record
Abstract
As a critical component of planning mass earthworks projects, designing effective haul road networks is conducive to delivering the project on time and under budget. The research reported in this paper proposes a grid-based temporary road network design method applicable to a site for which grading design has been completed. Further adding to the existing body of knowledge, a quantitative methodology is proposed for optimizing the detailed planning of earthmoving jobs based on a particular temporary haul road network design. Each job is defined in terms of the source cell, the destination cell, the earth volume, and the shortest-hauling-time path between source and destination. Through seamless integration of the Floyd-Warshall algorithm and linear programming model, the shortest average haul time for a truckload can be obtained while automatically fulfilling site grading design specifications. Based on the resulting average haul time, cost equations are defined to account for (1) the direct truck-hauling crew cost; and (2) building, maintenance, and removal costs of temporary haul roads. As such, the cost associated with executing the optimized earthmoving job plan over a particular haul road network design can be readily assessed, making it straightforward for project managers to compare alternatives. The proposed methodology is demonstrated in steps using a numerical example and further applied in a case study based on a real-world project in northern Alberta.
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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 | 0.000 |
| 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) | 0.002 | 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".