Competitive Assessment of Ice and Frozen Silt Mat for Crane Ground Support Using Finite-Element Analysis
Bibliographic record
Abstract
High capacity cranes are the backbone of the heavy construction industry, which, over the last few years, has embraced modularization. Consequently, ground stability has become a critical issue for their safe utilization. In practice, ground stability includes laying several thicknesses of adequately compacted construction aggregates and one or more layers of timber/steel mats on top. In the present study, a novel alternative is explored whereby an artificially created layer of ice or frozen silt constitutes the base upon which timber or steel mats can be stacked for ancillary crane support. However, to understand the challenges and feasibility of the proposed technology, a theoretical study using finite-element analysis (FEA) is carried out in order to gain insight into the factors that can affect the structural behavior of ice/frozen silt and their comparison with commonly used mat materials, timber (Coastal Douglas fir), and steel (G40.21-44W). The comparison is built using mechanical properties under identical boundary conditions. The results show that the performance of frozen silt is on par with that of Coastal Douglas fir. A preliminary cost comparison is also established to develop the value proposition of using frozen silt as a crane mat.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".