Controlled heavy-haul traffic loading as a method to remediate liquefiable soft silts
Bibliographic record
Abstract
Transporting of extremely large indivisible loads (10 000–30 000 t) is becoming increasingly popular to allow offsite modular construction of infrastructure for oil and gas, mining, and renewable energy projects in remote areas. Such exceptionally large transient loads could encounter unusual geohazards: there is a risk of metastable liquefaction when crossing soft alluvium, causing sudden failure, potential casualties, and severe production delays. Furthermore, temporary roads for these payloads are a large cost to such projects; conventionally designed earthworks and (or) ground improvement are often unaffordable or logistically impossible. This laboratory study indicates the fabric can be strengthened, and the hazard reduced, if the soil is subjected to careful repeated loading that rearranges the initially precarious fabric through gradual accumulation of plastic strains. A novel remediation technique for these temporary haul roads is proposed: managed deployment of increasingly heavy haul vehicles could result in staged fabric rearrangement that strengthens the soil to the point where it would be safe for the heavy vehicles to use it. In so doing, a more economic temporary haul road is open to operations (coupled with observation methods to ensure adequate performance throughout) and production activities are not overly disrupted.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".