Experimental and Field Performance of PP Band–Retrofitted Masonry: Evaluation of Seismic Behavior
Bibliographic record
Abstract
Unreinforced masonry (URM) buildings exhibited extreme vulnerability during past earthquakes, although these are shelters for a majority population in many earthquake-prone developing countries. Most of the current retrofitting techniques used for such structures are either expensive or require highly skilled labor or sophisticated equipment for implementation. On the other hand, the retrofitting technique proposed in this paper is economical and easy to apply. This paper aims at examining the performance of the retrofitting technique using polypropylene (PP) band. The displacement-controlled lateral deformation has been investigated experimentally. The monotonic load-displacement behaviors of a URM wall and the wall retrofitted with PP band are compared. It was found that the URM wall retrofitted by PP band improves the ductility and energy absorption capacity by three and two times, respectively. Performance of a full-scale masonry building retrofitted with PP band in Nepal during the last Gorkha earthquake of April 25, 2015, has also been presented in this paper. It was observed that the PP band–retrofitted masonry building survived, whereas many nearby buildings experienced severe damage and some of them collapsed. This study demonstrates the efficacy and practicability of use of PP band for improving seismic resistance of a URM structure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".