Data‐driven post‐earthquake rapid structural safety assessment
Bibliographic record
Abstract
Summary Earthquake‐prone cities are exposed to important societal and financial losses. An important part of these losses stems from the inability to use structures as shelters or for generating economic activity after the event of an earthquake. The inability to use structures is not only due to collapse or damage; it is also due to the lack of knowledge about their safety state, which prohibits their normal use. Because a diagnosis is required for thousands of structures, city‐scale safety assessment requires solutions that are economically sustainable and scalable. Data‐driven algorithms supported by sensing technologies have the potential to solve this challenge. Several ambient vibration monitoring studies of buildings, before and after earthquakes, have shown that the extent of damage in a building is correlated with a decrease in the natural frequency. However, the observed worldwide data may not be representative of specific cities due to factors such as construction type, quality, material, and age. In this paper, we propose a framework that is able to progressively learn the relationship between frequency shift and damage state as a small number of buildings in a city are inspected after an earthquake, and to use that information to predict the safety state of uninspected but monitored buildings. The capacity of the proposed framework to learn and perform prognosis is validated by applying the methodology to a city with 1000 buildings having simulated frequency shifts and damage states. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".