An Analysis Model for Partially Grouted Shear Walls Using Macro-Modelling: Importance of Reporting Joint Shear Strength
Bibliographic record
Abstract
Partially grouted (PG) masonry shear walls are widely used as lateral force resisting systems in North America due to their economic value and practicality. Only cells containing reinforcing steel are grouted in PG walls, leaving the remaining cells hollow. The in-plane lateral behavior of PG walls is complex due to the different materials in the assembly (masonry block, mortar, grout and reinforcing steel), and discontinuities formed by the voids in the ungrouted cells. To characterize and understand better the in- plane response of PG walls, finite element (FE) methods have been used, based on micro- and macro- modelling formulations. Micro-modelling involves the definition of each material present in the masonry assemblage (i.e. masonry block, mortar, grout and reinforcing steel) separately as well as the interfaces between them. Although accurate, this process is both time-consuming and computationally expensive. Thus, more efficient analysis models are preferred, such as macro-modeling (i.e. treating the masonry assemblage as a region with averaged properties). In this paper, a macromodeling approach was presented to investigate the inplane shear behavior of PG walls, using a finite-element software that implements the modified compression field theory originally developed for reinforced-concrete members. The approach was validated with nine PG walls experimentally tested from the literature. These walls covered a wide range of design parameters, including compressive strength, aspect ratio, axial stress, and reinforcement ratio, which add more credibility to the defined model. The defined model simulated the response of the experimental satisfactorily up to the peak strength. The validation process revealed the necessity of reporting the joint shear strength in the literature, which was found to have a significant influence on the model results.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".