User cost savings in eliminating pavement excavations through employing trenchless technologies
Bibliographic record
Abstract
When a pipeline is installed under flexible pavement structures using open excavation and fill methods, the excavation can result in premature pavement deterioration due to difficulties associated with trench site restoration and soil and asphalt compaction. In developed areas, trenchless construction methods have become a popular alternative to open trench excavation because of reduced surface disruptions and reinstatement costs, a shorter construction period, and significantly reduced traffic delay costs associated with construction by saving time in construction operations. This paper summarizes a recent study that examined how pipeline installations on 1- and 7-year-old Ontario municipal collectors and minor arterial roads impact the 30-year life cycle of a pavement in terms of performance, future maintenance costs, and user-delay costs. Study results indicated that (1) approximately 30% reduction in pavement life can be expected once an excavation is made in a road; (2) increased maintenance and rehabilitation costs for excavating 1-year-old pavement were determined to be approximately Can$146/m 2 , and the costs for excavating 7-year-old pavement vary from Can$85 to $140/m 2 ; and (3) the use of trenchless technology with good construction practices has the potential to significantly reduce road maintenance and rehabilitation costs and user-delay costs.Key words: pavement performance, open excavation, trenchless technologies, user-delay costs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".