Post-Earthquake Forensic Examination of Two Unreinforced Masonry Buildings via Discontinuum-Based Analysis
Bibliographic record
Abstract
Post-earthquake investigations show that unreinforced masonry (URM) buildings may exhibit diverse failure mechanisms depending on the construction morphology and the connection detailing between their structural components. Advanced computational models are necessary to consider the influence of these aspects. However, realistically reproducing the post-collapse state of an existing URM building is challenging when limited data is available on the aforementioned features. To address this challenge, a framework for exploring the seismic behavior of URM buildings is presented. The current investigation presents two case study buildings located in Türkiye's Hatay province: the Mithatpaşa Primary School in Iskenderun and the Liwan Boutique Hotel in Antakya, both of which suffered partial collapses during the recent Kahramanmaraş Earthquakes in 2023. Discrete block models of the two case study buildings are generated based on geometrical information obtained from various pre- and post-collapse vision-based data sources. An automatic block generation algorithm is proposed to replicate periodic and nonperiodic masonry wall patterns. Next, the generated discrete block media are analyzed using discontinuum-based structural analysis to predict the seismic response of the structures. Comparisons between the preliminary pushover analysis results and collapse observations inform further analyses, and lead to an exploration of how construction morphology and connection detailing may have contributed to the partial collapse of the buildings. It is demonstrated that this iterative approach, supported by forensic site evidence and reverse engineering analysis, provides new insight into the influence of key factors that contribute to collapse. This information can help safeguard similar structures and inform the development of effective retrofitting solutions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".