Modeling construction labour productivity using fuzzy logic and exploring the use of fuzzy hybrid techniques
Bibliographic record
Abstract
Recent trends indicate that fuzzy techniques (fuzzy set theory, fuzzy logic, and fuzzy hybrid models) have found increased application in the construction domain, even more so in the last half decade. This paper presents the application of fuzzy expert models and fuzzy hybrid concepts in modeling construction labour productivity, which is critical information for scheduling and estimating construction projects. The fuzzy expert model addresses both subjective and objective factors affecting labour productivity of two common industrial construction processes: rigging and welding pipe. The resulting model matched highly with respect to linguistic terms; however, the numerical match was low, indicating the need to have fuzzy hybrid models to improve the predictive ability of the fuzzy expert model. Further research is underway to combine the strengths of fuzzy logic in addressing subjective and linguistic evaluations of labourer performance with the strengths of other artificial intelligence methods, such as neural networks, in training and calibrating the fuzzy model to properly address the context variables, as well as the principal variables.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".