Investigating the Reliability of Sidewalk Survey Parameters on the Seismic Risk Assessment of RC Buildings Considering the 2023 Kahramanmaraş Earthquakes
Bibliographic record
Abstract
Identification of the buildings that are most vulnerable to significant damages following a major earthquake is a critical issue in risk mitigation processes. Rapid visual screening (RVS) methods are commonly used to obtain rapid and accurate assessment results especially in large urban regions. Accuracy of these results fundamentally depends on method-based sidewalk survey parameters and a regionwise building performance level threshold. In this regard, the tragic 2023 Kahramanmaraş earthquakes provided valuable data for investigating the success of the existing RVS methods and the correlation of survey parameters with performance scores by considering pre- and postearthquake conditions. This paper presents two fundamental cases: (1) the ability of RVS methods in prioritization of seismic risk level of buildings through damage observations and (2) findings on the effects and reliability of sidewalk survey parameters on the estimation of seismic risk levels of existing buildings in a quantitative manner. For this purpose, 1,015 buildings were considered for comparison of the predicted vulnerability levels with the actual damages from site observations. Pearson’s correlation coefficients are used as dependency pointers to reveal the correlation levels between the survey parameters and the performance indicator. Results indicated that RYTEIE in Türkiye and FEMA P-154 in the United States appear to be more reliable in prioritizing the seismic risk level of buildings based on regionwise performance thresholds compared to prioritization assessed via Canadian RVS method among the considered RVS methods. It is derived from the current study that the RYTEIE method is capable of predicting risky buildings within the range of 54% to 86% accuracy using the proposed performance score range from 35 to 75 for the selected building stock. Moreover, the performance score evaluated via FEMA P-154 methodology is highly correlated with design year of a building.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".