Predicting construction productivity using situation-based simulation models
Bibliographic record
Abstract
Construction site operations are very complex, and they involve complicated relationships among numerous tasks, factors, obstacles, risks, and uncertainties, or triggering situations that affect productivity. To improve the performance of construction operations, one needs to understand the impact these triggering situations have on productivity. The paper discusses a recently developed technique, called situation-based simulation modeling, that is used to model the triggering situations in construction to predict productivity. This tool can model the cause-and-effect relationships among various triggering situations, which previous construction models have ignored. Construction operations that were directly observed and recorded for more than 3500 person-hours served as the data source for the development of the model. The simulation results are not only able to accurately predict productivity relative to the actual productivity observed at the site, but also provide the basis for recommendations to mitigate problematic situations to improve productivity.Key words: construction, productivity, modeling, simulation, situation-modeling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".