Spatio-Temporal Representation And Analysis In Infrastructure Systems
Bibliographic record
Abstract
Much information is needed to manage the activities and events that occur throughout the lifecycle of an infrastructure system. Conventional Infrastructure Management Systems provide only limited support for representing, visualizing and analyzing the spatio-temporal relationships throughout the lifecycle of the infrastructure. This paper proposes a method that integrates 4D modeling with several information technologies to facilitate space and time visualization and analysis. Based on a 4D model of a bridge, two approaches are investigated for spatio-temporal conflict detection and analysis. The first approach focuses on workspace conflicts. Combinations of different 3D shapes are used to represent the workspaces, which is more accurate than the simple prismatic element that was used in previous research. The second approach dynamically detects spatio-temporal conflicts during construction using cell-based modeling techniques. Detailed procedures for each modeling method are discussed. Both methods enable conflict analysis and visualization of the worksite and the occupation of spaces. KEY WORDS 4D modeling, infrastructure management systems, lifecycle, visualization, spatial analysis, workspace conflicts, cell-based modeling, construction simulation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".