Performance-Based Seismic Design for Unreinforced Masonry: A Literature Review
Bibliographic record
Abstract
Worldwide momentous loss to property, structures, and life due to past earthquakes has been reported in previous research studies. Ancient unreinforced masonry structures are at risk against seismic loading as were designed before existence of seismic codes. To mitigate such hazards some criteria need to be incorporated to improve the seismic performance of the buildings. This paper reviews the contributions of research studies toward the development of the methodologies associated with performance-based seismic engineering (PBSE). Performance-based design is used to provide an appropriate and apparent relationship between an earthquake event and the corresponding seismic structural performance of a structure. Experimental and analytical seismic performance based evaluation of various existing structures like a hospital in California, USA, residential building in Barcelona, Spain, and various unreinforced masonry (URM) structures in Europe, Asian, and America (Albania, Pakistan, US, Mexico, and Canada) by a nonlinear static pushover analysis using various software and risk assessment tools has been reviewed and briefly discussed to perceive various structure performance levels, to determine desirable approach, scenario, and design codes. On reviewing existing literature it is observed that mostly analytical and very less performance-based seismic design studies on unreinforced masonry has been reported world over; however, no literature is available on performance-based seismic design of unreinforced clay brick masonry in India and elsewhere. Reviewed study concludes that performance-based approach appears to be more effective and economical. Moderate to severe structural damage level is anticipated under both deterministic and probabilistic scenario. The structural deficiencies are precisely predicted by SAP2000 software. Structural seismic response assessment not clearly explained in American code, whereas Mexican codes are complex in earthquake-resistant design. Probability on slight damage is indicated in HAZUS, whereas ELER predicts complete damage. Review findings show that a vast scope of further research study is essentially required on the performance-based seismic engineering design for evaluation target performance of other structural types.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".