Numerical Modelling of Flat Arch Masonry Retaining Walls
Bibliographic record
Abstract
A vast majority of arches can be found as bridges in railway and roadway systems, aqueducts, and roofs.However, employing arch action to resist lateral earth pressure has not been exploited broadly in the literature.A recent study has investigated the potential of utilizing flat arch unreinforced concrete block retaining wall to resist the lateral earth pressure and surcharge loading.The proposed retaining wall was constructed as a segmental circular flat arch.This novel concept was a success, with experimental stresses and deflections well below critical limits.Despite the success, the wall's deflection profile was unexpected, possibly due to loss of fixity at the abutments.Therefore, the authors were unable to numerically replicate their experimental measurements, preventing them from presenting a general response of this structural system.This paper investigates the possibility of explaining the experimental results as a result of abutment slippage.A 3-D finite element simplified micro model, verified using a thick-cylinder analysis, is used to numerically reproduce the experimental setup.The influence of grout stiffness and arch wall-abutment coefficient of friction on the loss of fixity is investigated.A detailed discussion on the influence of these factors and a renewed analysis of the experimental results is presented.The model confirmed the hypothesis of fixity loss as producing the experimental deflected shape.The development of this model makes possible a parametric analysis characterising the response of the structural system.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".