Modeling the Effect of Subjective Factors on Productivity of Trenchless Technology Application to Buried Infrastructure Systems
Why this work is in the frame
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Bibliographic record
Abstract
Trenchless technology (TT) includes a large family of methods utilized for installing and rehabilitating underground utility systems with minimal surface disruption and destruction resulting from conventional excavation. Productivity of TT techniques is affected by a number of subjective factors that need to be evaluated. A productivity index (PI) model is developed in order to represent this subjective effect in refining productivity assessment. The analytic hierarchy process and fuzzy logic are used to develop the proposed PI model that relies on the actual performance of 12 subfactors under three main categories: management, environmental, and physical conditions. The developed PI model resulted in PI equal to 0.7323 and 0.7251 for microtunneling and horizontal directional drilling (HDD) projects, respectively. Multiattribute decision support system software is developed to determine the PI for a specific TT technique using Visual Basic. The PI model is tested, which shows reasonable results. This research is relevant to both industry practitioners and researchers. It provides practitioners with a model that justifies their productivity calculation by quantifying subjective factors effect, which will affect their schedule and cost estimation for trenchless projects. In addition, it provides researchers with the development methodology for the PI model.
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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 | 0.000 |
| 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