Towards Performance-Based Design of Masonry Buildings: Literature Review
Bibliographic record
Abstract
Masonry is among the most widely used construction materials around the world. Contemporary masonry buildings are primarily designed to comply with prescriptive building code regulations. In recent decades, performance-based design (PBD) has gained increasing attention and achieved significant success in critical structures or infrastructure systems. Instead of being the first mover, the masonry research and practice community can be a faster follower in response to the design paradigm shift towards PBD for masonry buildings. A reliable performance assessment of masonry buildings is of paramount importance in the PBD framework. To facilitate this, this paper presents an up-to-date comprehensive literature review of experimental and analytical studies with emphasis on their contributions to advancement towards performance assessment of masonry buildings. This review categorized available works into two sub-topics: (1) traditional unreinforced masonry and (2) modern reinforced masonry. In each sub-topic, studies focusing on the structural behaviors of masonry at the component-level (i.e., masonry wall) are discussed first, followed by the building system-level-related studies. Through this literature review, the current state of the art and remaining research gaps are identified to provide guidance for future research needs and to pave the way for implementing PBD in the masonry industry.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".