Setting Baseline Rates for On-Site Work Categories in the Construction Industry
Bibliographic record
Abstract
Labor performance drives construction project performance. Labor performance can be improved by increasing the direct-work rate, which is the time spent by workers on installing materials and equipment. However, setting baseline rates for direct-work rate and determining expectation levels during the construction phase requires further investigation. The focus of the research reported in this paper is to establish a methodology for setting a desirable and realistic baseline rate based on activity analysis, primarily for industrial projects. First, an adaptive neurofuzzy inference system (ANFIS)-based method was developed as a means of estimating baseline rates based on existing knowledge. The method was trained using 272 data points. Its flexibility and functionality validate its usefulness; however, three additional methods of defining baseline rates were also developed based on simpler concepts and demonstrated with data points available from 14 projects, and the experience associated with these projects. As a result, comprehensive methods and a valuable initial dataset for industrial construction projects to better establish baseline rates for direct work and supporting activities were contributed. This should help project managers to estimate appropriate baselines and set realistic goals for direct-work rate which ultimately may lead to improvement of labor performance.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".