Estimating Mean and Variance of In-Plane Resistance of Masonry Walls Using Inaccurate Design-Code Models and Limited High-Fidelity Data
Why this work is in the frame
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Bibliographic record
Abstract
Analyzing uncertainty or estimating statistics of load resistance of structures is crucial for reliability-based code calibrations, forming the foundation for modern limit state design. While design-code models can be used to estimate the load resistance of masonry walls against in-plane (IP) loading, it is widely recognized that these models are inherently inaccurate due to their simplifications, assumptions, or empirical features. Therefore, employing them for uncertainty analysis or statistics estimation can be challenging. On the other hand, detailed mechanics-based finite-element (FE) models and physical experimental tests are typically more accurate. Nevertheless, their application for uncertainty analysis or statistic estimation is often impractical due to their high computational or economic cost. To address this challenge, this study introduces improved estimators for mean and variance of the IP resistance of masonry walls after considering parameter uncertainties, by leveraging efficient design-code models and limited high-fidelity data generated from detailed FE models. In the proposed estimators, a large number of design-code model evaluations are introduced to improve computational efficiency, while only a limited number of FE model evaluations are integrated to ensure accuracy. Three case studies are presented to illustrate the applicability of the proposed estimators: one on unreinforced masonry (URM) walls and two on reinforced masonry (RM) walls. The results indicate that design-code model-based estimators exhibit large bias, and compared to the estimators that solely rely on the FE model, the proposed estimators achieve higher accuracy given the same computational budget.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it