Seismic Collapse Risk Assessment of Low-Aspect-Ratio Reinforced Concrete Shear Walls Using the FEMA P695 Methodology
Bibliographic record
Abstract
Several recent research studies have demonstrated that the seismic performance of low-aspect-ratio reinforced concrete (RC) shear walls (i.e., defined herein as walls with height-to-length ratios less than 2) has not been yet adequately quantified to allow for robust risk assessment. This is mainly attributed to the complex nonlinear flexure/shear interaction behavior of such walls, along with their wide spectrum of possible design parameters, leading to major discrepancies in their seismic performance. Despite this unique behavior, most building codes and design standards do not assign distinctive seismic performance factors for such walls. To address this, the main objective of the current study is to propose seismic performance factors for low-aspect-ratio RC shear walls when different wall geometrical configurations and design parameters (e.g., aspect ratios, axial load levels, and seismic design categories) are adopted. These factors are evaluated against the acceptance criteria of the FEMA P695 methodology for Quantification of Building Seismic Performance Factors. In this respect, a numerical model was developed and experimentally validated to simulate the seismic response of 36 low-aspect-ratio RC shear wall archetypes. The model was utilized to perform nonlinear static and dynamic analyses, and collapse fragility curves were then generated to assess the collapse risk of such wall archetypes following the FEMA P695 methodology. According to the methodology, the proposed seismic performance factors were assessed by quantifying the ratio between the median collapse intensity and the intensity of the maximum considered earthquake (MCE). The results showed that R factors of 2.0 and 3.0 for special low-aspect-ratio RC walls with low and high axial load levels, respectively, can limit the probability of collapse under the MCE and are subsequently able to meet the FEMA P695 acceptance criteria.
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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".