Adaptation Methods for Transportation Infrastructure Built on Degrading Permafrost
Bibliographic record
Abstract
Abstract Climate warming since the second half of the 20 th century has begun to significantly impact infrastructure integrity in permafrost environments and has already resulted in expensive maintenance operations. Engineers in countries with permafrost are actively working to adapt the design of structures to degrading permafrost conditions. Here, we review permafrost degradation processes and their geotechnical impacts. We also summarise mitigation techniques for protecting transportation infrastructure built on permafrost and for preventing permafrost degradation near these facilities based on the results of field and laboratory tests, numerical simulations and engineering practices on such infrastructure. We draw four conclusions: (1) climate warming and local surface changes have caused permafrost degradation, and resulted in instability and damage leading to infrastructure maintenance and repair; (2) passive cooling methods, including high‐albedo surfacing, sun‐sheds, air convection embankments, air ducts, heat drains and thermosyphons, have shown consistent cooling effects, if designed appropriately; (3) mitigation and adaptation methods are more expensive than conventional construction techniques as shown by construction cost data for a test site in Canada; and (4) the influence of continued climate warming on permafrost and infrastructure design must be considered within the design of new or rehabilitated infrastructure and within the context of the infrastructure's service life. Copyright © 2016 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".