Data Analytics of Production Cycle Time for Offsite Construction Projects
Bibliographic record
Abstract
Offsite construction has been widely used in the construction industry. The process improves productivity that leads to shortened project schedule and lower budget. Over the decades, offsite construction industry has continuously evolved with the aspects of management and technology. However, offsite construction companies still have various challenges such as accurately obtaining productivity metrics, which helps in production planning. These challenges result from lack of understanding the process itself because of high variation of wall panel design specifications along with high variability of cycle time at each work station. To solve the problem, productivity data needs to be collected in context to offsite construction. In this paper, a time study was conducted in one of Alberta’s-based offsite construction factory. From the collected data and product design specifications, multiple linear regression models were developed to represent the actual work station time. The comparison between actual collected duration and modeled duration for assembly station demonstrate its accuracy that ranges from 80 -99%. In the near future, findings will be used for simulation to forecast factory production and optimize the utilization of the resources.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".