Integrated DEMATEL and ANP-Based Framework to Model Construction Labor 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
Accurately modeling productivity is essential for ensuring that the results of construction process simulation models align with actual practice. Since collecting quantitative data is challenging and expensive, productivity models are often built using the information provided by industry experts. The subjectivity of this information, however, commonly results in oversimplified or inadequate productivity models. To address this challenge, this research has developed a novel framework that reduces the subjectivity associated with labor productivity modeling by identifying interrelationships between factors affecting productivity that individual subject experts may have overlooked. A Decision-Making Trial and Evaluation Laboratory (DEMATEL) is used to identify relationships (i.e., dependencies) between factors, which is integrated with an Analytic Network Process (ANP)-based approach to determine the strength (i.e., weight) of each relationship. Results can support decision-making or feed productivity data to simulation, empirical, or dynamic models of construction systems. Outputs of the proposed method yield higher-quality inputs for productivity modeling-based decision-support systems compared to traditional input preparation approaches. The effectiveness of the framework is demonstrated through an illustrative example.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it