Use of post-earthquake point cloud data for forensic evaluation of failures in masonry structures
Bibliographic record
Abstract
Abstract Post-earthquake reconnaissance of engineering structures aims to collect the essential data required for forensic investigations of failures. These investigations inform time-critical repair, stabilisation and demolition decisions after an earthquake. Current reconnaissance procedures rely on visual observations and manual surveying, which do not provide adequate data for the forensic analysis of historic masonry structures. This study shows how an alternative form of data, point clouds from laser scanning and photogrammetry, can be used to conduct detailed forensic work. Case studies from the 2023 Turkey earthquakes are used to illustrate how point clouds were employed to 1) quantify the geometry of load-bearing systems, 2) assess construction quality, 3) detect geometric distortions and defects, and 4) provide data to generate and evaluate numerical models. The examples highlight the new insight provided by this alternative form of data. The dataset collected as a part of this study is shared open access to enable further investigations: https://github.com/Yilong-Yang/Shared-Data---BEE-2025 .
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".