Simulation Applications in Construction Site Layout Planning
Bibliographic record
Abstract
Simulation Applications in Construction Site Layout Planning S. Razavialavi, S. AbouRizk Pages 275-284 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: In the planning phase of every construction project, layout of temporary facilities is a crucial task; site layout can affect safety, travel cost and time, construction productivity, and space utilization. However, site layout planning can be a complicated problem, due to the interdependency of influencing factors. Although interaction among activities is one of the major drivers of site layout planning, it has not been properly addressed in past research. In this study, simulation is presented as a promising tool to address this gap. The capability of simulation technology to model complex processes in construction projects makes use of simulation tools in site layout optimization problems effective, while existing methods are unable to perfectly model these problems, in some cases. Additionally, the advantages and challenges of implementing simulation are assessed and a generic framework for simulation application in site layout planning is proposed. Keywords: Site layout planning, Simulation, Layout optimization, Construction preplanning DOI: https://doi.org/10.22260/ISARC2013/0030 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".