Use of pore pressure build-up as damage metric in computation of equivalent number of uniform strain cycles
Bibliographic record
Abstract
The build-up of earthquake-induced excess pore-water pressure may be viewed as analogous to the cumulative damage of saturated granular materials caused by cyclic loading, and consequently as a damage metric when converting an irregular earthquake loading to an equivalent number of uniform cycles, Neq. In this paper, a comprehensive series of strain-controlled tests have been conducted using the new combined triaxial simple shear (TxSS) apparatus developed at Institute de Recherche d’Hydro-Quebec (IREQ) in collaboration with the geotechnical group at the Université de Sherbrooke to verify the hypothesis of adopting the pore-water pressure ratio, Ru, as a damage metric when converting earthquakes to an equivalently damaging number of uniform strain cycles. Different reconstituted saturated samples of Baie-Saint-Paul, Carignon, and Quebec sands have been tested under undrained conditions up to liquefaction. The experimental results from this study have been utilized to develop an empirical expression to compute Neqγ from both the number of cycles required to trigger liquefaction, Nliq, and the material parameter, r. The parameter r had been experimentally calibrated a priori from a separate set of tests using uniform strain cycles following the theoretical framework outlined by Green and Lee in 2006. The present results reveal that the measured pore-water pressure ratio, Ru, is in agreement with predicted cumulative damage using the Richart and Newmark (R–N) hypothesis. However, the Palmgren–Miner (P–M) hypothesis underestimates the cumulative damage (i.e., the generated pore-water pressure) during cyclic loading.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".