In this thesis, a support vector machine (SVM) is used to develop a model to predict Arias Intensity. Arias Intensity is a measure of the strength of ground motions that considers both the amplitude and the duration of ground motions. In this research, a subset of the database from the “Next Generation and the duration of Ground-Motion Attenuation Models” project was used as the training data. The data includes 3525 ground motion records from 175 earthquakes. This research provides the assessment of historical earthquakes using arias intensity data. Support vector machine uses a Kernel function to transform the data into a high dimensional space where relationships between the variables can be efficiently described using simpler models. In this research, after testing several kernel functions, a Gaussian Kernel was selected for the predictive model. The resulting model uses magnitude, epicentral distance, and the shear wave velocity as the predictor of Arias Intensity.
| Identifer | oai:union.ndltd.org:siu.edu/oai:opensiuc.lib.siu.edu:theses-3991 |
| Date | 01 August 2022 |
| Creators | Adhikari, Nation |
| Publisher | OpenSIUC |
| Source Sets | Southern Illinois University Carbondale |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses |
Page generated in 0.0033 seconds