A probabilistic computational framework for predicting the diagonal tensile strength of unreinforced masonry walls
Bibliographic record
Abstract
Masonry is a composite construction material consisting of units and mortar. Depending on the adopted computational modeling strategy to perform structural analysis, the composite nature of masonry is either represented via a homogenous continuous medium using averaged material properties, or the morphological features of masonry are addressed explicitly by implementing different levels of details within the discontinuum-based analysis framework. While each modeling approach has its advantages and limitations, the continuum-based approach is commonly used in large-scale simulations, requiring the tensile strength of masonry as an input parameter, which is difficult to obtain due to the complexity of experiments and the high degree of material variability. To this end, the present research proposes a probabilistic computational framework to predict the diagonal tensile strength of URM walls. It explores the tensile strength of masonry composite through computational investigations simulating the diagonal compression tests of small masonry walls based on the discrete element method (DEM). This modeling strategy captures the local failure mechanism at the unit-mortar interfaces and masonry units by representing the masonry as a system of deformable blocks interacting along their boundaries. The validated approach is used to generate a large dataset by considering the material uncertainties that are further utilized to propose predictive equations including bond shear strength under zero vertical pressure and brick (or masonry unit) tensile strength. A potential use of the proposed predictive equations is demonstrated by presenting a simple study where the load-carrying capacity of a masonry wall is estimated using a macro-modeling technique. The results of the discontinuum-based analyses demonstrate good agreement with the available experimental findings presented in the literature. Overall, the results highlight the great potential of the proposed framework to predict the capacity of masonry structures and to complement experimental campaigns.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 | 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".