Equipment workspace analysis in infrastructure projects
Bibliographic record
Abstract
Workspace conflicts are one of the important problems that can delay construction activities, reduce productivity, or cause accidents that threaten the safety of workers. Workspace planning is particularly important in the case of large infrastructure projects, such as bridge construction, and rehabilitation projects where equipment with complex workspace requirement is required. This paper aims to extend the previous research on workspace representation and analysis in the case of large infrastructure projects focusing on the following issues: (i) specific representation of equipment workspaces using composite shapes, (ii) semiautomatic generation and analysis of workspaces, and (iii) development of a prototype system that can generate workspaces and detect spatiotemporal conflicts in a three-dimensional environment. The computational aspects of the proposed approach are discussed and demonstrated through the development of a prototype system, which has been applied in a case study of the deck replacement of Jacques Cartier Bridge in Montréal.Key words: workspace analysis, spatiotemporal conflicts, construction equipment, infrastructure projects, bridge redecking, constructive solid geometry.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".