Classical Planning Model-Based Approach to Automating Construction Planning on Earthwork Projects
Bibliographic record
Abstract
Extensive research in automated operation planning has led to significant advances in the area of robotics. Theories and methods resulting from robotics have yet to be adapted to enable automated project planning in construction engineering. Aiming to demonstrate the potential of implementing automated planning theory and methods in construction project planning, we developed an automated earthwork planner prototype following the principles and framework of classical planning model in computer science. As time is not explicitly represented, implementing classical planning model to perform optimization and planning simultaneously results in potential temporal–spatial conflicts (TSCs). The present research develops a two-step approach to separate operations optimization and earthwork planning in typical rough grading projects. As such, TSCs encountered in existing mathematical programming based earthwork planning methods are resolved. To enable fully integrated and automated earthwork planning, the prototype system has been seamlessly integrated with project scheduling and operations simulation software for higher level analyses. To demonstrate advantages of the automated planning methodology, construction plans were independently produced by 14 graduate student teams on the same “testbed” project; results were evaluated and compared with the plan generated by the proposed system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".