Chaos and Complexity in Modeling and Detection of Spatial Temporal Clashes in Construction Processes
Bibliographic record
Abstract
Spatial temporal clashes happen when workspaces overlap to share the same location at the same time period. The impacts of such overlap, as stated by the literature could extend from loss of productivity to property damage or death. The determination of the magnitude of the clash is based upon many parameters such workspace type and activity criticality. Most of the current clash detection models are deterministic. However, the behavior of the crew (their performance and approach taken towards management of clashes) is more chaotic. The chaos mainly emerges from the dependency of clashes and their consequences on the human interactions among crew members and between different crews. Stochastic modeling and simulation can help, to some extents, in capturing the chaotic nature of such behavior. Increasing the complexity of the model (due to introducing stochastic variables) is theoretically expected to add to the accuracy of simulation outcomes. The first step however, is to determine the sensitivity of the clash detection problem to possible chaotic behavior. This paper defines four possibilities of non-deterministic behavior: uncertain movement, variable productivity, dynamic scheduling, and clash impact butterfly effect. Additionally, this paper reports the results of a simple case study applied to define the fitness of selected clash evaluation models to the changes in workspaces due to the non-deterministic behaviors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".