Automated Generation of Work Breakdown Structure and Project Network Model for Earthworks Project Planning: A Flow Network-Based Optimization Approach
Why this work is in the frame
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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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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 | 0.000 |
| 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 it