Numerical analysis of buried mine explosions with emphasis on effect of soil properties on loading
Bibliographic record
Abstract
During armed conflicts or peace-support operations, most casualties are attributed to vehicle-landmine accidents and thus, mine protection fea-tures are a prerequisite for vehicles serving in these areas. Previously, mine protection research was predominantly experiment driven and focussed on structural deformation. Soil parameters were not observed and the influence of soil was not considered. Accurate soil modelling is necessary because experimental studies have shown that soil, in particular saturated soil, has a significant effect on the magni-tude of landmine blast loading on a vehicle. This research describes a numerical modelling approach for studying soil-blast interaction in landmine explosions. The numerical analysis is carried out using the non-linear dynamic analysis software, AUTODYN. The research progressed from (1) the explosion of hemispherical charge laid on a rigid surface, through (2) the study of the explosion of mine deployed in dry sand, to (3) the validation of the mine explosion in cohesive soil for different setups. A framework for deriving the model for soil with varying moisture contents was proposed. The subject of the study is prairie soil (cohesive soil). Standard soil laboratory data are used to determine soil properties that are then used to define a numerical soil model. Validity of the modelling procedure was ascertained by comparison with experimental results from the horizontal pendulum series that were conducted at Defence R&D Canada – Suffield. The applicability of the model was ascertained for (i) different soil types, (ii) varying moisture content, (iii) different mine deployment, and (iv) various high explosive. The numerical results are in reasonable agreement for all observed range of the moisture content. The model and the methodology is generic and extensible and it is argued that such models greatly complement mine experiments.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".