Towards an advanced work packaging simulation-based approach for industrial construction projects
Bibliographic record
Abstract
Planning and scheduling construction industrial projects is considered one of the most challenging tasks due to the nature of these projects. Often, the project delivery method of these projects follows the fast-track approach, where there is a lack of detailed engineering information in the early stage, and the construction overlaps the design phase. As such, advanced work packaging (AWP) and planning of the projects at this early stage remains an issue that faces the construction professionals. This research focuses on developing a simulation-based approach for advanced work packaging, planning and scheduling of industrial projects in the early stages. The approach deploys simulation techniques, that utilize historical data, to divide the project into several construction work areas (CWA), to identify various construction work packages (CWP), and finally to specify a defined set of activities or Installation work packages (IWP), resulting in a schedule that can be of aid to the project stakeholders during the early project stage. To verify the proposed concepts, a case study of an industrial project located in Canada, is presented and the output of the simulation model is discussed. The results were validated by experts in the field, and they highlighted that there is a great potential for the simulation-based scheduling approach especially that the model allows for updates, by feeding real-time as-built data once the project commences and this data become available.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".