Development of a site and motion proxy-based site amplification model for shallow bedrock profiles using machine learning
Notice bibliographique
Résumé
Accurate prediction of site amplification is crucial for seismic hazard assessment, particularly at shallow bedrock sites where limited data can constrain modeling efforts. Traditional regression-based models often fail to capture complex nonlinear interactions inherent in seismic ground response. This study aims to develop proxy-based linear and nonlinear site amplification models that provide reliable predictions using machine learning (ML) techniques, enabling practical applications in regional ground motion modeling. The outputs of a series of one-dimensional site response analyses were used for training. Three ML algorithms were used: random forest (RF), extreme gradient boosting (XGB), and deep neural network (DNN). The models incorporated four site proxies and two motion proxies to predict site amplification, and their performance was evaluated against both a conventional regression-based model and a rigorous ML model utilizing full shear-wave velocity profiles and input motion spectra. When identical proxies were used, the differences between the regression and ML-based models were not pronounced. However, when the ML model was trained simultaneously with the site and motion proxies for both linear and nonlinear components, the prediction performance was significantly enhanced. This revealed that the traditional two-track approach of the site-proxy-dependent linear component and motion-proxy-conditioned nonlinear component is ineffective. A pairing scheme for site and motion proxies is recommended to achieve the most accurate predictions. Among the three ML methods, the RF algorithm exhibited the weakest performance. The XGB and DNN algorithms’ prediction accuracies were superior to the RF algorithm. The XGB and DNN outperformed each other when predicting the linear and nonlinear components, respectively. The proposed ML models achieved coefficient of determination (R 2 ) values up to 0.97 with root mean square error (RMSE) as low as 0.04 for linear components, and R 2 up to 0.92 with RMSE as low as 0.06 for nonlinear components, demonstrating significant improvements over conventional regression-based models. Compared with a rigorous ML model, the proxy-based models exhibited agreeable predictions with far less information, illustrating the benefit of adopting the ML algorithms for improved adaptability and predictive capability. The constraint imposed on the site type, considering only profiles with a bedrock depth of less than 30 m, may have resulted in the strong performance of the proxy-based model.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».