Productivity Analysis of Micro-Trenching Using Simphony Simulation Modeling
Bibliographic record
Abstract
Micro-trenching is an innovative method for installing fiber optic cable in residential areas and business districts which minimizes surface scarring and potential negative social and environmental impacts. This method has three major steps including cutting a narrow trench in the pavement, cable installation and trench backfilling. This paper discusses a Simphony simulation model of the micro-trenching procedure and analyzes its productivity. Brief descriptions of the micro-trenching method and two field installations used to validate the model are included. A simulation model was developed for two different installation depths of 7.6 and 23 cm using two different methods. To provide an estimation of project duration, the impact of weather conditions on micro-trenching productivity was also considered. The developed model can be used for what if scenarios and for predicting the outcomes, which may be useful for studying the procedure and verifying if any productivity improvement can be achieved. The results indicate that the influence of installation depth is more significant than the impact of weather conditions. Reducing installation depth from 23 cm to 7.6 could improve productivity up to 50% while cold weather condition can reduce productivity by 18.8%. The simulation model demonstrates that the productivity can be improved up to 16% by overlapping two steps during the installation process: starting the cleaning procedure when a portion of cutting is completed. Doi: 10.28991/cej-2020-03091607 Full Text: PDF
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".