Predicting construction labor productivity using lower upper decomposition radial base function neural network
Bibliographic record
Abstract
ABSTRACT Construction labor productivity is affected by many factors such as scope changes, weather conditions, managerial policies, and operational variables. Labor productivity is critical in project development. Its modeling, however, can be a very complex task for it requires consideration of the factors stated above. In this article, a novel methodology is proposed for quantifying the impact of multiple factors on productivity. The data used in the present study was prepared using data processing techniques and was subsequently used in the development of a predictive model for labor productivity utilizing radial basis function neural network. The model focuses on labor productivity in a formwork installation using data gathered from two high‐rise buildings in the downtown area of Montreal, Canada. The predictive capability of the developed model is then compared with other techniques including adaptive neuro‐fuzzy inference system, artificial neural network, radial basis function (RBF), and generalized regression neural network. The results show that LU‐RBF predicts productivity more accurately and thus can be utilized members of project teams to validate the estimated productivity based on available data. The advantages and limitations of the proposed model are discussed in this article.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".