Arch Stone Bridges: Procedures for Evaluation of Structural Integrity
Bibliographic record
Abstract
In the Balkan Peninsula, the single-arched stone masonry bridge has served for centuries as an essential part of transportation infrastructure. Being exposed to the natural elements and to aging, several of these structures are in a state of disrepair that is accelerated by neglect. Nevertheless, these are important samples of the built heritage encapsulating historical materials, methods of construction and ancient craftsmanship. Still standing for over half a millennium, such bridges testify the wisdom by which the masons chose the location and form of the arch to span over torrents minimizing the wear from water scouring and floods. Preservation of the bridges that are still standing is a priority. With increasing number of bridge collapses reported in the past years under extreme flood, an immediate need emerges for methods of evaluation of the structural vulnerability and measures to enhance their resilience. Using as a case study a bridge built in fifteenth century in Greece, this paper unfolds the essential attributes of a comprehensive assessment framework that combines non-destructive evaluation techniques to reveal information about technologies and details, with a numerical investigation of the resilience of the structure to floods and seismic hazards in order to identify risks of bridge integrity.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".