Enhanced Prediction and Uncertainty Analysis for In-Plane and Out-of-Plane Resistance of Unreinforced Masonry Walls: A Multifidelity Approach
Bibliographic record
Abstract
Unreinforced masonry (URM) walls are commonly found in historic and legacy buildings around the world. The structural resistance of URM walls under in-plane (IP) and out-of-plane (OOP) loads is of primary concern to engineers, as their failure is generally sudden, with catastrophic loss of strength and structural integrity. Due to the complex behavior and inherent uncertainties of the masonry material, engineers opt for the use of low-fidelity (LF) resistance models with limited accuracy, such as design code models and other simplified analytical models in the literature. Models with enhanced prediction accuracy have attracted growing attention, particularly when uncertainty analysis (e.g., reliability evaluation) is needed. As such, high-fidelity (HF) models, such as nonlinear finite element models based on advanced computational mechanics, have been developed and used to characterize the structural behaviors and failure modes of URM walls, particularly the resistance, with remarkable success in terms of accuracy. However, their direct use for resistance prediction and uncertainty analysis is scarce due to the computational burden and technical complications. To address this issue, this study takes an efficient multifidelity (MF) approach that leverages both HF and LF models via information fusion to enhance LF models with only a few HF model evaluations for URM walls. The main research thrust is to develop an MF surrogate model to facilitate uncertainty analysis in the IP and OOP resistance of URM walls. The analysis results indicate that the MF surrogate models developed are capable of achieving significant improvements in terms of accuracy and efficiency in predicting the IP and OOP resistances of URM walls both deterministically and probabilistically, compared with the LF model and the surrogate model developed only based on a limited number of HF model runs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".