Seismic Collapse Risk Assessment of Reinforced Masonry Walls with Boundary Elements Using the FEMA P695 Methodology
Bibliographic record
Abstract
Using boundary elements in reinforced masonry (RM) walls allows closed ties to be used and multiple layers of vertical bars to be accommodated, thus providing a confining reinforcement cage. This enhances the overall performance of the RM wall with boundary elements relative to traditional RM walls with rectangular cross sections. This is attributed to the fact that traditional RM walls can typically only accommodate single-leg horizontal reinforcement and a single layer of vertical reinforcement because of practical limitations associated with concrete masonry unit geometrical configuration and construction techniques. Following the FEMA P695 methodology, “Quantification of Building Seismic Performance Factors,” the National Institute of Standards and Technology reported that some low-rise traditional rectangular RM walls (without boundary elements) might experience an excessive risk of collapse under the maximum considered earthquake (MCE). Moreover, although North American codes give seismic modification factors for RM shear walls with rectangular cross sections, no distinctive corresponding values are provided for RM shear walls with boundary elements. To address these issues, this study examines the effect of adopting the seismic response modification factors assigned for traditional RM shear walls on the collapse risk of RM shear walls with boundary elements. In this respect, OpenSees was used to create macro models to simulate the seismic response of 20 RM shear walls with boundary elements, designed with different configurations under different gravity load levels. The modeling approach was experimentally validated and the models were subsequently used to perform nonlinear static pushover analyses and incremental dynamic analyses following the FEMA P695 methodology. The analyses focused on evaluating the wall overstrength, period-based ductility, and seismic collapse margin ratios under the MCE. The results show that RM walls with boundary elements designed considering the ASCE7-10 force reduction factor currently assigned to RM walls with rectangular cross sections experience an enhanced performance that is enough to meet the FEMA P695 acceptance criteria for the expected seismic collapse risk under the MCE.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".