Framework for developing labour-hour prediction models from project design features: case study in structural steel fabrication
Bibliographic record
Abstract
Assigning labour-hours to a certain scope of work during design and estimating is still more of an art than a science. This research proposes a data-driven approach that uses multiple linear regression (MLR) and available historical data from building information models (BIM) to associate project labour-hours and project design features. The framework relies on an enhanced version of stepwise regression technique to select the most relevant predictive factors and generate a predictive model without compromising the achievable accuracy of regression. The framework also encompasses analytical methods for justifying MLR application, validating the resulting model, and establishing range estimates for point-value predictions. In collaboration with an industry partner, the framework application is exemplified by analyzing labour-hours and design features for structural steel fabrication, leading to the creation of a valid MLR model in the simplest form. Finally, pros and cons for the proposed framework and opportunities for future research are discussed.
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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 | 0.001 | 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 | 0.000 |
| 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".