Date of Award:

8-2024

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Civil and Environmental Engineering

Committee Chair(s)

Brady Cox

Committee

Brady Cox

Committee

Marv Halling

Committee

James Bay

Committee

Tony Lowry

Committee

John Rice

Abstract

This dissertation introduces novel techniques for estimating the soil small-strain shear modulus (Gmax) and damping ratio (D), crucial for modeling soil behavior in various geotechnical engineering problems. For Gmax estimation, a machine learning approach is proposed, capable of generating two-dimensional (2D) images of the subsurface shear wave velocity, which is directly related to Gmax. The dissertation also presents a method for estimating frequency dependent attenuation coefficients from ambient vibrations collected using 2D arrays of seismic sensors deployed across the ground surface. These attenuation coefficients can then be used in an inversion process to estimate D. The developed techniques for Gmax and D estimation have undergone rigorous validation and testing through synthetic simulations and field experiments, demonstrating their effectiveness. Furthermore, the dissertation presents a comprehensive dataset collected using cutting-edge seismic sensing technologies, including distributed acoustic sensing, three-component seismometers, and a large mobile shaker truck. This dataset has been archived and made publicly available, aiding researchers worldwide in developing and testing new non-invasive imaging techniques. Finally, the dissertation concludes with a review and comparison of recent advancements in non-invasive subsurface imaging techniques and their application at the same site.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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