Probabilistic Analysis of a Nailed Wall: Use of the Random Field Theory and Ordered Weighted Averaging Method
Bibliographic record
Abstract
This study aimed to emphasize the significance of spatial variability in soil strength parameters on the behavior of nailed walls, highlighting the necessity of probabilistic design approaches. The investigation involved a 7.2-m nailed wall reinforced with five nails, simulated using the local average subdivision random field theory combined with the limit equilibrium method and the FEM, known as the random limit equilibrium method (RLEM) and the random finite-element method (RFEM) approaches. Initially, the wall stability was evaluated by RLEM using 10,000 Latin hypercube sampling realizations. The wall was globally stable among all samples for a correlation length equal to its height (7.2 m). The wall behavior, associated displacements, moments, wall shear forces, nail axial forces, and ground settlements were examined using RFEM. The RFEM analysis reveals that different random fields influence the maximum displacement (Hmax), maximum moment (Mmax), and maximum shear force (Vmax) experienced by the wall. The cumulative distribution function plots were generated for the wall critical parameters, including Hmax, Mmax, and Vmax. Leveraging the simple weighted averaging and ordered weighted averaging techniques, different combinations of Hmax, Mmax, and Vmax were assessed with varying weight assumptions. This allowed us to identify critical random field realizations and estimate the level of risk using a newly introduced parameter, the decision index. Finally, the effect of different correlation lengths (isotropic and anisotropic) for two different coefficients of variation of soil strength parameters on the distribution of Hmax, Mmax, and Vmax was studied. The findings highlight the importance of considering the spatial variability of soil properties to achieve a reliable design of nailed walls.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".