CLASSIFICATION OF RESIDENTIAL BUILDING STOCK IN SERBIA
Bibliographic record
Abstract
Developing a classification system (taxonomy) for buildings is a critical step for seismic risk assessment studies. Such a system can be used to characterize a building portfolio within urban/rural settlements or building stock for the entire country. Serbia is located in a region characterized by a moderate seismic hazard. In the last century, 10 earthquakes of magnitude 5.0 and higher occurred in Serbia, the strongest (M 6.0) in 1922. The strongest earthquake in the 21st century (Mw 5.5), with an epicenter close to Kraljevo, occurred in November 2010 and caused significant damage to residential buildings. In 2019, members of the Serbian Association for Earthquake Engineering (SUZI-SAEE) contributed to the SERA project and its goal to develop a seismic risk model for Europe. A taxonomy of residential buildings in Serbia was developed based on previous national and regional building stock studies. The proposed taxonomy includes the Lateral Load-Resisting System (LLRS) (e.g., wall, frame, dual wall-frame system) and material of the LLRS (e.g., masonry, concrete, wood) as the main attributes. The type of floor diaphragm (rigid or flexible) has been specified only for masonry typologies with unreinforced masonry walls, while building height and date of construction have been implicitly considered. According to the proposed taxonomy, there are 9 residential building typologies in Serbia; out of those, 5 typologies are related to masonry structures, 3 are related to RC structures, and one is related to wood structures. This paper describes the proposed taxonomy and outlines the characteristic features of different building typologies and their relevance for estimating seismic vulnerability and risk. A comparison of the proposed taxonomy for Serbia and published taxonomies for Croatia is also presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".