On-Site Rock Blasting: When Shot Rock Impact Loading Predictions Don't Meet Client Expectations
Bibliographic record
Abstract
Kiewit Engineering Group, Inc. (KEG) was contracted by Kiewit Barnard Joint Venture (KBJV) to design temporary works for the Gross Reservoir Expansion site in Boulder, Colorado. KBJV planned to blast rock at the left abutment of Gross Dam to construct a new dam foundation. Blasted debris was expected to fall 300 ft (91 m) down the abutment slope and impact an existing tunnel. KEG estimated the risk associated with rockfall using RocFall2 software and three rockfall impact loading methods: the Swiss algorithm method, the Labiouse method, and the Japan Road Association method. Each method predicted unfavorable outcomes with respect to the structural integrity of the tunnel. Ultimately, KEG prescribed a maximum allowable blasted rock size that would, theoretically, not damage the tunnel. Based on previous construction observations, KEG expected that the actual blasted rock size would exceed that of the maximum allowable blasted rock. As to not assume risk associated with this activity, KEG did not provide a stamped recommendation to KBJV. In the field, the tunnel was significantly damaged due to shot rock impact loading. This paper explores the adequacy of published rock impact loading calculation methods to represent an on-site rock blasting scenario and potential consequences of presenting engineering estimates.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".