Predicting Industrial Construction Labor Productivity Using Fuzzy Expert Systems
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
The objective of this technical note is to illustrate the application of fuzzy expert systems to the modeling of a practical problem—that of predicting the labor productivity of two common industrial construction activities: rigging pipe and welding pipe. This note illustrates how to develop and test such a model, given the realistic constraints of subjective assessments, multiple contributing factors, and limitations on data sets. The factors that affect the productivity of each activity are identified, and fuzzy membership functions and expert rules are developed. The models are validated using data collected from an actual construction project. The resulting models are found to have high linguistic prediction accuracies. This note is of relevance to researchers by demonstrating how a fuzzy expert system can be developed and tested. It is of relevance to industry practitioners by illustrating how fuzzy logic and expert systems modeling can be exploited to help them solve real world problems.
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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 | 0.000 | 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 it