Site amplification prediction model of shallow bedrock sites based on machine learning models
Bibliographic record
Abstract
Prediction of the site amplification is of primary importance for a site-specific seismic hazard assessment. A large suite of both empirical and simulation-based site amplification models has been proposed. Because they are conditioned on a few simplified site proxies including time-averaged shear wave velocity up to a depth of 30 m ( V S30 ) and site period ( T G ), they only provide approximate estimates of the site amplification. In this study, site amplification prediction models are developed using two machine learning algorithms , which are random forest (RF) and deep neural network (DNN). A comprehensive database of site response analysis outputs obtained from simulations performed on shallow bedrock profiles is used. Instead of simplified site proxies and ground motion intensity measures, matrix data which include the response spectrum of the input ground motion and shear wave velocity profile . Both machine learning based models provide exceptional prediction accuracies of both the linear and nonlinear amplifications compared with the regression-based model, producing accurate predictions of both binned mean and standard deviation of the site amplification. Among two machine learning techniques , DNN-based model is revealed to produce better predictions.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".