System model for analysing construction labour productivity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Despite long-term, sustained research and industry practice, predicting construction labour productivity (CLP) using existing factor and activity modelling approaches remains a challenge. The purpose of this paper is to first demonstrate the limited usefulness of activity models and then to propose a system model approach that integrates factor and activity models for better prediction of CLP. Design/methodology/approach The system model parameters – comprising factors and practices – and work sampling proportions (WSPs) were identified from literature. Field data were collected from 11 projects over a span of 29 months. Activity models based on the relationship between CLP and WSPs were created, and their validity was tested using regression analysis for eight activities in the concreting, electrical and shutdown categories. The proposed system model was developed for concreting activity using the key influencing parameters in conjunction with WSPs. Findings The results of the regression analysis indicate that WSPs, like direct work, are not significantly correlated to CLP and fail to explain its variance. Evaluation of the system model approach for the concreting activity showed improved CLP prediction as compared to existing approaches. Research limitations/implications The system model was tested for concreting activity using data collected from six projects; however, further investigation into the model’s accuracy and efficacy using data collected from other labour-intensive activities is suggested. Originality/value This research establishes the role of WSPs in CLP modelling, and develops a system modelling approach to assist researchers and practitioners in the analysis of productivity-influencing parameters together with WSPs.
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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 | 0.001 |
| 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 it