Seismic Vulnerability Assessment and Strengthening of Heritage Timber Buildings: A Review
Bibliographic record
Abstract
Recent studies highlight the potential impact of earthquakes on cultural heritage sites and monuments, which in turn yield significant adverse impacts on economies, politics, and societies. Several aspects such as building materials, structural responses, and restoration strategies must be considered in the conservation of heritage structures. Timber is an old organic construction material. Most of the historic timber structures were not designed to withstand seismic forces; therefore, the seismic vulnerability assessment of heritage timber structures in areas with high seismic hazard is essential for their conservation. For this purpose, different strategies for the numerical modeling of heritage timber buildings have been developed and validated against tests results. After performing seismic analysis using detailed analytical methods and predicting the susceptible structural components, strengthening techniques should be utilized to mitigate the risk level. To this aim, various methods using wooden components, composite material, steel components, SMA etc., have been utilized and tested and are reviewed in this study. There are still some gaps, such as full-scale numerical modeling of strengthened buildings and investigating the soil–structure interaction effects on the seismic behavior of buildings that should be investigated.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.003 | 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".