Numerical Modeling of the SR-18 Geogrid Reinforced Modular Block Retaining Walls
Bibliographic record
Abstract
The paper reports numerical model details and predictions of the end-of-construction performance for two instrumented and well-documented mechanically stabilized earth (MSE) walls. The walls were constructed as part of the highway SR-18 approach fills for a bridge near Seattle, Washington. The geogrid reinforced block face walls were modeled using a commercially available two-dimensional (2D) finite-difference program. The paper provides details on how material properties were selected from laboratory testing of wall components and how the computer modeling was carried out. The paper shows that predicted wall deformations and reinforcement strains were in reasonable agreement with measured data using both linear elastic-plastic and nonlinear elastic-plastic constitutive models for the soil. The geogrid reinforcement was simulated using a nonlinear load-strain-time secant stiffness model and cable elements. The paper compares numerical predictions of reinforcement loads at end of construction with measured values and predictions using a AASHTO Simplified Method, K-stiffness Method, and Simplified Stiffness Method. The paper is a useful benchmark for modelers because it demonstrates what the authors believe are reasonable expectations of model accuracy for Class C predictions of deformations and reinforcement strains for these types of walls when high-quality project information is available.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".