Numerical Advances in Understanding the Behavior of Gravity Retaining Wall during Seismic Motions
Bibliographic record
Abstract
The response of gravity retaining walls during ground motion is still a challenging field. Recent developments in computational methods have opened the possibility of enhancing the understanding of the non-linear nature of soil-structure systems, e.g., earth pressure thrust acting on the retaining wall, translational and rotational movements, propagation of waves in the soil more realistically and quickly. Till today, Mononobe Okabe (MO) method (pseudo-static) is the most used analytical method because of its simplicity. However, there are many limitations and gives over-conservative results in terms of earth pressure thrust, and many literatures have already justified such a response. Several improved studies are already available, but very few have considered proper soil-structure interaction, real-time input earthquake data (not sinusoidal), and a sufficient number of earthquakes to evaluate the response acting on the wall during dynamic loading. We seek contribution by analyzing the problem numerically using FE software Plaxis 2D and studying the behavior of retaining wall during seismic loading (range of amax = 0.053g to 1.2g) in terms of acceleration, displacement, rotation, and earth pressure thrust of retaining wall. The main contribution observed is the acceleration was not uniform throughout the medium instead gets amplified up to around 0.6g and later gets attenuated with maximum amplification occurring at the top of the retaining wall followed by the top of backfill soil and base of the wall. The residual displacement and rotation showed an incremental trend with an increase in horizontal seismic coefficient (kh). The earth pressure thrust obtained using numerical analysis was comparatively less than predicted by the MO method. Keywords: Gravity retaining wall; Acceleration amplification response; Earth pressure thrust; Finite element method;
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".