Date of Award:

12-2026

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Civil and Environmental Engineering

Committee Chair(s)

Mohsen Zaker Esteghamati

Committee

Mohsen Zaker Esteghamati

Committee

Marv Halling

Committee

Brady Cox

Abstract

Earthquake engineers rely on accurate recordings of ground shaking to design safe and resilient buildings. However, sorting through thousands of these recordings to find the reliable ones and throwing out those ruined by sensor errors or background noise is traditionally done by hand. Because modern seismic networks collect massive amounts of earthquake data every day, this manual checking process is much too slow. To fix this, researchers are turning to artificial intelligence to automatically check the quality of these recordings.

While artificial intelligence offers a fast solution, there are limitations. These computer models can become massive and expensive to run, and they sometimes struggle to evaluate earthquakes in new geographic areas. This research addresses these challenges by creating a faster and more reliable automated system.

First, the study compared two different types of artificial intelligence to confirm that the computer was actually identifying real physical features of the earthquake, rather than just guessing based on mathematical patterns. Second, the research compressed these massive computer models into much smaller, efficient versions. These streamlined models not only ran faster but also performed slightly better when analyzing data from new locations.

Ultimately, this research provides the tools to automatically and accurately sort earthquake data. By speeding up this process and ensuring high-quality data, structural engineers can better understand earthquake hazards and design safer infrastructure for society.

Download

Share

COinS

Copyright for this work is retained by the student. If you have any questions regarding the inclusion of this work in the Digital Commons, please email us at .