Akademik Veri Yönetim Sistemi
Soil Dynamics and Earthquake Engineering, cilt.174, 2023 (SCI-Expanded)
Cumulative absolute velocity (CAV) is a powerful intensity measure for quantifying potential earthquake damage to structures. Machine learning (ML) methods can provide more accurate and reliable predictions of cumulative absolute velocity due to handling nonlinear relationships, adaptability to changing conditions, automation, efficiency, and the potential for real-time predictions. This study aims to identify machine learning regressions with the highest accuracy for CAV prediction. Several supervised machine learning algorithms were applied and comprehensively compared for performance and accuracy in CAV prediction using the recently compiled Turkish strong-motion database. Support Vector Machine, Linear Regression, Random Forest, Artificial Neural Network, Bayesian Ridge Regression, and Gradient Boosting algorithms were evaluated and compared with traditional Ground Motion Models (GMMs). Two new datasets including 24,667 strong-motion recordings from Turkiye along with global strong-motion recordings are used to build machine learning models. The first dataset contains all recordings of events with 3.5≤MomentMagnitude(Mw)≤7.6, while the second dataset contains only recordings with Mw≥5.5. Moreover, feature selection and outlier detection were performed as preprocessing steps to choose the best of seven CAV estimator parameters in order to boost the ML model performance. To measure the performance of ML methods five evaluation metrics were utilized which are mean square error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), and correlation coefficient (R). Comparative assessment of the machine learning algorithms suggests that models trained by Mw≥5.5 dataset are quite successful in CAV prediction compared to predictive models trained by the 3.5≤Mw≤7.6 dataset. The result proves that the Gradient Boosting models significantly outperform the other machine learning algorithms in terms of R and RMSE. Machine learning techniques are more successful with user-selected estimators representing the key components that make up earthquake recordings. Finally, the machine learning-based CAV prediction models for Turkiye are compared with available CAV GMMs, and it is observed that the machine learning-based models can predict CAV as successfully as GMMs if there are a sufficient number of recordings for training machine learning algorithms.