An Approach to Sinkhole Prevention on Post Pipeline Construction at Trenchless Road Crossings
Bibliographic record
Abstract
Sinkholes manifest unpredictably at road crossings long after the completion of pipeline installation. In recent pipeline projects, Alberta Clipper Expansion and Line 4 Extension, over 1200 km of NPS 36 oil pipeline was constructed across Alberta, Saskatchewan, and Manitoba. Over 600 road crossings were executed across these provinces utilizing standard industry crossing techniques and under a wide variety of soil conditions. Several months after construction, sinkholes appeared on roads at locations along the centerline of the newly constructed pipeline. It is hypothesized that bores which were observed to have been over-reamed, re-reamed, or had pipe pulled back, may have contributed in development of unconsolidated soil or “voids” in comparison to adjacent native subsurface soil, which then manifested into sinkholes. In other cases, the evolution of voids may have been attributed to pre-existing soil conditions. Since sinkholes pose safety concerns to the public as well as the integrity of the pipeline mitigation, control measures were taken to assess and remediate other locations prior to sinkhole manifestation. An approach to prevent sinkhole manifestation is identifying high-risk crossings, scanning for voids, and void remediation. Identification of high-risk sinkhole manifestation at crossings involved desktop evaluation which was based on: observations noted inspectors’ reports, geotechnical conditions, depth of crossings, the elevation difference between the entry and exit holes, and crossing method. Once prioritized, selected road crossings were scanned for voids using a technology called Ground-Penetrating Radar (GPR), which is the focus of this paper. Ground-penetrating radar employs a system of radio waves at various frequencies directed at the subsoil. The changing velocities between consolidated and unconsolidated soil provides different views of the subsurface. Factors such as pipeline depth, soil type, and interference, played a factor in the ability to accurately scan for voids. For remediation, the injection of polyurethane foam was used. This paper describes the approach, process, accuracy factors, and findings of Ground-penetrating radar used on pipeline projects.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".