Bibliographic record
Abstract
The use of fully grouted reinforced masonry shear walls (RMSWs) has been growing in several areas around the world owing to their relative ease of construction and their in-plane ductile behavior. However, RMSWs possess low out-of-plane ductility which amplifies the vulnerability of such components under blast loading. Furthermore, the long time and high costs of recovery following devastating (deliberate or accidental) explosions have created a need for resilience-based design for risk mitigation, especially considering the different sources of associated uncertainty. As such, this study aims to lay out the foundations of a probabilistic resilience–based blast analysis and design framework. The framework should have the capability of quantifying the overall building post-blast functionality in order to estimate its recovery cost and time, and thus the building resilience following such a demand. The proposed framework will be specifically applied for RMSW buildings incurring blast loads through a profound investigation for the behavior of rectangular RMSWs as being a primary structural element in reinforced masonry buildings. The investigation will subsume an experimental and analytical evaluation for the performance of load-bearing RMSWs with different in-plane ductility levels subjected to out-of-plane quasi-static loading. This will be followed by a numerical investigation of RMSWs to conclude the blast probabilistic performance of RMSWs that can be applied within the proposed probabilistic resilience-based blast framework. The work in this dissertation presents a key step towards adopting resilience based analysis and design in future editions of blast-resistant construction standards and provides the decisionmakers with a complete insight into post-blast building functionality and its recovery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".