Automated Generation of Work Breakdown Structure and Project Network Model for Earthworks Project Planning: A Flow Network-Based Optimization Approach
Bibliographic record
Abstract
The present research proposes an analytical methodology to automatically generate a work breakdown structure (WBS) and a project network model based on activity-on-node (AON), which consists of two stages: (1) optimizing earthwork volume allocation, which is intended to define haul jobs by identifying the most economical combinations of cut and fill cells, thus minimizing the total haul effort in rough-grading operations and (2) according to the optimization results from Stage 1, establishing WBS and defining precedence relationships among jobs in WBS analytically to enable automated generation of the AON project network model. To simplify the newly devised methodology, a flow network-based technique is developed to facilitate earthwork allocation optimization and AON project network generation. Simulation trace, internal validation, and comparison with related established methods were performed for evaluating the effectiveness of the proposed methodology. To reveal limitations inherent in established methods and cross validate the proposed methodology, two established methods were selected, which represent the state of art in the problem domain. Further validation of the new methodology against established ones entails elaborate simulation experiment design by randomly adjusting earth volumes in each cell of the site and varying site size and statistical analysis of simulation outputs. The comparison-based validation shows advantages of the proposed methodology in (1) ensuring practical feasibility of resulting earthmoving job plans and (2) improving achievable productivity performance of construction operations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".