Date of Award


Degree Name

Master of Science


Civil Engineering

First Advisor

Tezcan, Jale


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.




This thesis is only available for download to the SIUC community. Current SIUC affiliates may also access this paper off campus by searching Dissertations & Theses @ Southern Illinois University Carbondale from ProQuest. Others should contact the interlibrary loan department of your local library or contact ProQuest's Dissertation Express service.