Machine learning in earthquake engineering: A review on recent progress and future trends in seismic performance evaluation and design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Applying machine learning (ML) in earthquake engineering has introduced new opportunities for better predicting, evaluating, and mitigating structural damage under seismic hazards . The rapid advancement of ML in earthquake engineering necessitates a thorough understanding of its potential and limitations to guide future research and practical applications effectively. This literature review focuses on the recent advancements of ML in structural seismic performance evaluation and design optimization. This paper comprehensively explores recent trends and innovations for each area, highlights ongoing challenges, and suggests future directions involving emerging technologies. Key findings reveal significant progress in ML methodologies. Still, challenges related to the accurate prediction of nonlinear hysteretic responses, the need for improved generalizability of ML models, the scarcity of high-quality data, effective feature selection techniques, and regional scale investigations remain. Moreover, the future research needs and strategies for addressing these challenges are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 it