Machine learning algorithms applied to engineering seismology and earthquake engineering

Chanda, Sarit and Somala, S N (2021) Machine learning algorithms applied to engineering seismology and earthquake engineering. PhD thesis, Indian Institute of Technology Hyderabad..

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Machine learning algorithms are used in this thesis to predict earthquake parameters for simulated and recorded events. First, the seismological engineering problem of location and magnitude is addressed using machine learning algorithms on synthetic seismograms. Only a single station/ Single component seismogram is used to estimate the earthquake location and the magnitude. The data-driven machine learning techniques are useful to extract the information from the seismograms. The Ground Motion Prediction Equations (GMPEs) like machine learning models have been developed for the different seismically active regions. Ground motion parameters such as intensity, peak ground motion, and earthquake duration are addressed using recorded data for various tectonic regimes worldwide. Ensemble learning algorithms based on majority voting have also been tried out in the thesis and for each seismically active region considered, the best performing algorithm is identified.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Thesis (PhD)
Uncontrolled Keywords: Random forest, XG Boost, PGA, Duration, Intensity
Subjects: Computer science
Civil Engineering > Earthquake Engineering
Divisions: Department of Civil Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 21 Jun 2021 06:39
Last Modified: 21 Jun 2021 06:39
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