An Integrated Framework for Worker Planning and Supervision in Construction
Bibliographic record
Abstract
Construction is a highly labour-intensive industry; therefore, the planning of worker crews is vital to achieving quality, cost, schedule and productivity targets. However, current construction practices do not initiate worker planning during the early stages of construction, nor do they pay attention to the full life cycle of the project. These pitfalls lead to worker demotivation and performance and productivity losses. Comprehensive worker performance measurement evaluation criteria the Construction Worker Performance Index (CWPI) was developed. A pilot study was carried out to test the applicability and validity of the conceptual framework. After conducting an analytic hierarchy process (AHP) analysis, relative weights for the technical skills assessment framework of Borcherding et al. (2001) were refined; and, the technical skills assessment framework was expanded by adding more sub-attributes to make it suitable for assessing construction workers of Alberta, Canada. The CWPI worker performance measurement formula was finalized using two rounds of surveys based on Delphi survey principles. A worker grading scheme was developed that classifies workers beyond current conventional worker planning, which is based only on skill levels. A cohesion measurement framework was developed based the guidelines provided Carron et al. (1985) and Widmeyer et al. (1985). By integrating different aspects related to worker planning, an overall worker planning framework was created to cover the full life cycle of a construction project. The applicability of the developed framework was field-tested on a construction site in Alberta, Canada. The CWPI and cohesion values of 114 construction workers were measured and documented. Nine construction worker crews (further divided in to twenty sub-groups) were observed, and the structures of the worker crews were studied. Characteristics of high and low performing worker crews were identified, and relationships between factors were investigated. Management level perspective about the developed framework was obtained through a survey conducted among 28 building industry practitioners. Relationships among the factors of the framework were investigated using a correlation analysis. A regression analysis was conducted to determine the causality between input factors and the cohesion level of worker crews. Finally, the limitations of the research were identified, and recommendations for future research were provided.
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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 | 0.001 | 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".