Pipeline Trenching in Permafrost: A Review
Bibliographic record
Abstract
BP America Inc., Enbridge Pipelines Inc., and TransCanada PipeLines Limited recently sponsored a comprehensive technical review of the use of wheel and chain trenchers for excavating pipeline ditches for large diameter, long distance oil and gas pipelines in permafrost. The purpose of the review was to identify techniques that could be implemented to improve the productivity of trenchers in permafrost and reduce pipeline construction costs. This paper summarizes the key findings of the study. The study included an analysis of data obtained from previous field trials and construction case histories in permafrost, including the results from proprietary trials that have never been published. The study found that the primary subsurface conditions affecting the productivity of both wheel and chain trenchers in permafrost soil are: 1) the concentration and lithology of cobbles and boulders; 2) the presence and strength of bedrock within the depth of trenching; and 3) the tensile strength of the permafrost soil. With current technology, neither wheel nor chain trenchers can achieve satisfactory rates of production if more than 5 to 10 percent cobbles or boulders are present, or if hard bedrock exists within the depth of trenching. The study evaluated a number of techniques for improving the productivity of both wheel and chain trenchers in permafrost soil which may or may not contain hard inclusions. These methods included pre-blasting along the ditchline using either conventional blasting techniques or shaped charges. In addition, a wide variety of multi-pass trenching techniques were evaluated as part of the study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".