Characterization of Sandstone for Application in Blast Analysis of Tunnel
Bibliographic record
Abstract
Abstract The present work aims to gain an understanding of the stress-strain response of sandstone, a sedimentary rock, under high loading rate and further determine the appropriate specimen dimension of sandstone for dynamic testing. The high strain rate characterization of sandstone is done for two different diameters and five different slenderness ratios of sandstone specimens using a 76-mm–diameter split Hopkinson pressure bar (SHPB) device. The stress-strain response of sandstone is studied by systematically varying the length of the striker bars and gas gun pressure of the SHPB device. The petrological and static characterizations of the sandstone rock are also carried out. Finally, the appropriate specimen size of sandstone for SHPB testing is proposed by checking the strength gain of the rock and amount of energy absorbed during the tests. Further, finite element (FE) analysis of the SHPB test on sandstone is performed using the strain rate–dependent Johnson-Holmquist (JH-2) model available in the FE software, LS-DYNA. The simulation results are compared with the experimental data in order to determine the parameters of the JH-2 model for sandstone. A parameter database is thus prepared for sandstone. The determined parameters are then used in the blast analysis of tunnels for a 20-kg trinitrotoluene (TNT) explosion and the tunnel response is studied.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".