A time-varying critical acceleration framework for an embedded cantilever retaining wall in saturated sand
Bibliographic record
Abstract
Accurate prediction of seismic deformation for embedded cantilever retaining walls with saturated backfill remains a critical challenge due to transiently evolving excess pore pressures. This study presents a novel time-varying critical acceleration framework, calibrated against and assessed using a dynamic centrifuge test. Unlike conventional models that assume a fixed resistance threshold, the proposed framework dynamically adjusts the critical acceleration using a softening indicator linked to excess pore pressure ratio, and a recovery indicator reflecting suction-induced strength regain during unloading. Two model variants were developed: a uniform coefficient model and a multi-phase calibrated model with phase-specific softening and recovery coefficients. Implemented through a modified Newmark sliding block approach, the multi-phase model captures both the build-up and the permanent magnitude of measured wall displacement reasonably. Comparison with measured strain time histories along the wall shows that the model reasonably reproduces the internal strain response. An uncertainty analysis using Monte Carlo sampling quantifies sensitivity to the baseline critical acceleration threshold and to softening/recovery coefficients, showing that even modest softening, when repeatedly activated, contributed substantially to wall displacement accumulation, while the recovery mechanism was crucial in mitigating collapse-level deformations. The framework provides a mechanism-based interpretation of complex wall dynamics by explicitly accounting for excess pore pressure driven softening and suction-induced recovery, underscoring their essential role in performance-based seismic evaluation of embedded cantilever retaining walls.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".