Machine learning-based estimation of seismic structural damage via an accessible web application
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
This paper introduces DIGITERRA, a novel web-based platform that enhances accessibility to seismic damage estimation through machine learning techniques. Trained on 120,000 nonlinear dynamic simulations, DIGITERRA provides accurate structural damage assessments without requiring specialized software or advanced technical expertise. The platform utilizes gradient boosting, a machine learning algorithm selected as the most effective after evaluating several alternatives. Feature selection is based on sensitivity analysis, SHAP analysis, and input from structural engineering experts to optimize both accuracy and accessibility. By allowing users to input basic building parameters and quickly receive damage state estimations, DIGITERRA democratizes access to advanced seismic analysis tools. This research demonstrates how machine learning can bridge the gap between complex engineering analyses and practical applications, empowering both specialists and non-specialists to make informed decisions about structural resilience in seismic-prone regions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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