Seismic assessment of buildings by rapid visual screening procedures
Bibliographic record
Abstract
Recently, several pre-earthquake screening methods have been developed in order to rapidly evaluate the vulnerability profile of the existing building stock, which has been constructed before or after the adoption and enforcement of seismic codes.The objective of these methods is to identify, inventory and rank all high-risk buildings in a specified region so that a strategy of priority based interventions to buildings can be formed.Major parameters that have affects on the seismic risk are the seismicity of the location, vulnerability and importance of the building structure.The most known rapid visual screening methods have been developed in countries of high seismic risk such as the USA, Greece, New Zealand, India and Canada and they are briefly described in this paper.Furthermore, these methods are applied to a sample of 456 reinforced concrete buildings, located in Athens, whose structural characteristics and levels of damage by the 1999 Athens earthquake are known.In particular, 93 buildings collapsed, 201 sustained severe damage, 69 moderate and 93 buildings sustained light damage.By the methods' implementation, eight different scores have been determined for each building, according to the scoring systems of the applied methods.The results of those applications are used to evaluate the methods' reliability in identifying potentially seismically hazardous reinforced concrete buildings.The obtained results indicate that the implementation of the Greek method results in the most reasonable connection between damage severity and structural scores for all levels of damage, while the Greek method is represented to be the most efficient in terms of both predicting the damage level and leading to the reliable formation of a high-priority set of buildings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".