Monuments and Historic Buildings: Monuments and Historic Buildings: Earthquakes and Structural Engineering
Bibliographic record
Abstract
Unreinforced masonry (URM) buildings are a relevant part of the worldwide building stock. These include stone, brick, adobe or earthen masonry structures and represent globally about 50% of the buildings stock. The widespread of most of this built heritage has been achieved based on empirical knowledge passed by generation to generation and, therefore, URM structural behavior was often ill-understood. These constructions have been typically made to withstand vertical loads only and, having a low strength/mass ratio, are rather vulnerable to dynamic horizontal loads as earthquakes, impact or blast actions. This addresses the importance of carrying out urgent measures in the URM built stock to avoid human and societal consequences and to minimize future economic impacts. Yet, intervening in these constructions is a complex process, due to the lack of structural information and due to their high importance. A scientifically based process is less susceptible to inadequate actions, which clearly sets a convenient context for the continuous development of more powerful numerical strategies. The talk addresses first the concepts of risk in structural engineering. Then, the damage in a recent earthquake in Greece is shown, together with shaking table tests. Subsequently, tools used for safety assessment at territorial scale and building scale are presented, together with some case studies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".