Modeling Construction labour productivity using Evolutionary Polynomial Regression
Bibliographic record
Abstract
Construction projects are labour-intensive and labour costs are a substantial percentage of total budget. Impaired labour productivity causes an increase in construction project schedule. Labour productivity is one of the most frequently discussed topics in the construction industry, and modelling labour productivity by utilising different techniques has been getting more attention. It is a challenging task as it requires identifying the influencing factors as well as considering the associated interdependencies. This paper investigates the application of evolutionary polynomial regression (EPR) for modelling labour productivity in formwork installation. EPR is a data-driven hybrid modelling technique based on evolutionary computing and has been successfully applied to solving civil engineering problems. Results obtained from the EPR model were compared with the outcomes of three other methods: best subset, stepwise, and general regression neural network (GRNN). Results demonstrate the predictive superiority of the developed EPR model for nonlinear problems based on statistical performance indicators.
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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 | 0.000 |
| 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 | 0.001 |
| 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".