Degradation of soft subgrade soil from slow, large, cyclic heavy-haul road loads: a review
Bibliographic record
Abstract
Extraction of resources in remote locations can require temporary haul roads to transport extremely large, slow-moving, indivisible loads (e.g., plant, oil–gas production modules, and reactors, weighing in excess of 1000 t) without interruptions. Poor subgrade soils may experience larger cyclic strains and greater cyclic degradation under these conditions than under conventional roads, yet the short engineering life precludes many foundation-strengthening options due to cost. As there is little research into this unique situation, this paper synthesizes research from a broad range of applications to discuss implications on expected soil response. Reference is made to critical state theory and discrete element method (DEM) modelling to develop fundamental concepts considering particle-scale interactions. Cyclic failure is proposed to be a kinematically unstable process, triggered by shear banding on the Hvorslev surface, tensile liquefaction or fabric-governed meta-stable liquefaction; the latter is particularly influenced by stress history and anisotropy. This paper finds pore-water pressure accumulation under load and dissipation between loads are key to cyclic degradation and furthermore to be dependent upon load duration, principal stress rotation, and repetition frequency. For meta-stable, liquefiable soils in particular, inclination of principal stresses is at least as important in assessing failure risk as magnitude of stresses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".