First Use of Fragile Geologic Features to Set the Design Motions for a Major Existing Engineered Structure
Bibliographic record
Abstract
ABSTRACT We document the first use of fragile geologic features (FGFs) to set formal design earthquake motions for a major existing engineered structure. The safety evaluation earthquake (SEE) spectrum for the Clyde Dam, New Zealand (the mean 10,000 yr, ka, return period response spectrum) is developed in accordance with official guidelines and utilizes constraints provided by seven precariously balanced rocks (PBRs) located 2 km from the dam site and the local active Dunstan fault. The PBRs are located in the hanging wall of the fault. Deterministic PBR fragilities are estimated from field measurements of rock geometries and are the dynamic peak ground accelerations (PGAs) required for toppling. PBR fragility ages are modeled from B10e cosmogenic isotope exposure dating techniques and are in the range of 24–66 ka. The fragility ages are consistent with the PBRs having survived at least two large Dunstan fault earthquakes. We develop a PGA-based fragility distribution from all of the PBRs, which represents the cumulative toppling probability of a theoretical random PBR as a function of PGA. The fragility distribution is then used to eliminate logic-tree branches that produce PGA hazard curves that would topple the random PBR with a greater than 95% probability (i.e., less than 5% survival probability) over a time period of 24 ka (youngest PBR fragility age). The mean 10 ka spectrum of the remaining hazard estimates is then recommended as the SEE spectrum for the dam site. This SEE spectrum has a PGA of 0.55g, which is significantly reduced from the 0.96g obtained for a preliminary version of the SEE spectrum. The reduction is due to the combined effects of the PBR constraints and a substantial update of the probabilistic seismic hazard model. The study serves as an important proof-of-concept for future applications of FGFs in engineering design.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".