Development and verification of a numerical model for the analysis of geosynthetic-reinforced soil segmental walls under working stress conditions
Bibliographic record
Abstract
The paper describes a numerical model that was developed to simulate the response of three instrumented, full-scale, geosynthetic-reinforced soil walls under working stress conditions. The walls were constructed with a fascia column of solid modular concrete units and clean, uniform sand backfill on a rigid foundation. The soil reinforcement comprised different arrangements of a weak biaxial polypropylene geogrid reinforcement material. The properties of backfill material, the method of construction, the wall geometry, and the boundary conditions were otherwise nominally the same for each structure. The performance of the test walls up to the end of construction was simulated with the finite-difference-based Fast Lagrangian Analysis of Continua (FLAC) program. The paper describes FLAC program implementation, material properties, constitutive models for component materials, and predicted results for the model walls. The results predicted with the use of nonlinear elastic-plastic models for the backfill soil and reinforcement layers are shown to be in good agreement with measured toe boundary forces, vertical foundation pressures, facing displacements, connection loads, and reinforcement strains. Numerical results using a linear elastic-plastic model for the soil also gave good agreement with measured wall displacements and boundary toe forces but gave a poorer prediction of the distribution of strain in the reinforcement layers.Key words: numerical modelling, retaining walls, reinforced soil, geosynthetics, FLAC.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".