Machine-learning-based models, methods, and software for intensity, vulnerability, and risk assessment of Central U.S. induced earthquakes

dc.contributor.advisorClayton, Patricia M.
dc.contributor.committeeMemberRathje, Ellen M.
dc.contributor.committeeMemberWilliamson, Eric B.
dc.contributor.committeeMemberZhang, Zhanmin
dc.contributor.committeeMemberFaust, Kasey M.
dc.contributor.committeeMemberZhang, Ming
dc.creatorKhosravikia, Farid
dc.date.accessioned2020-10-02T16:13:29Z
dc.date.available2020-10-02T16:13:29Z
dc.date.created2020-08
dc.date.issued2020-08-14
dc.date.submittedAugust 2020
dc.date.updated2020-10-02T16:13:29Z
dc.description.abstractSince 2009, the Central U.S. has been subjected to a new type of seismic hazard attributed to human activities from the petroleum industry. Since then, there has been an increase in the number of earthquakes in the Central U.S. from an average of 25 per year in 2008 to 365 in 2017. These earthquakes can adversely affect the safety of infrastructure in the region, considering most were designed with minimal to no seismic detailing considerations due to the historically low seismicity in the region. The main objective of this dissertation is threefold: 1) To characterize the seismic demand of these earthquakes by developing region-specific ground motion models. 2) To evaluate the vulnerability of the built environment (in particular, bridge portfolios and residential buildings with masonry façades) to these recent earthquakes by developing fragility functions. 3) To integrate the ground motion and fragility models with other region-specific information to investigate regional consequences (i.e., potential economic loss) on the built environment for future seismic events. This information is now used by the Texas Department of Transportation to inform decision-making in terms of post-earthquake response and planning for future events. For each objective, the present study combines machine learning science with structural and earthquake engineering knowledge into a data-driven, state-of-the-art framework to develop more reliable prediction models compared to the conventional methods in the literature. This dissertation comparatively investigates the advantages of using machine learning techniques instead of conventional methods in developing each model (i.e., ground motion and fragility models). Moreover, this study investigates the seismic characteristics, vulnerability, and risk associated with these earthquakes, compared with those associated with other seismic hazards in the U.S. The comparison includes similar magnitude natural earthquakes in the Western U.S., New Madrid seismic hazards (i.e., the historical seismic hazard of interest in the Central U.S.), and estimates from HAZUS (i.e., the software provided by Federal Emergency Management Agency for disaster risk assessment). As part of this study, open-source application software named ShakeRisk is developed for risk, reliability, and resilience assessment of the built environment to natural hazards. ShakeRisk provides a platform to integrate artificial intelligence, systems engineering, structural and earthquake engineering research fields to simulate civil infrastructure responses at both structural and system scales in a reliable and computationally efficient way. Adopting clean architecture principles and object-oriented programming language in the design of ShakeRisk, it can be readily extended by adding features (i.e., new data sources, models, analyses, and user interfaces) and customizing existing ones without the need to modify existing code.
dc.description.departmentCivil, Architectural, and Environmental Engineering
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/83059
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/10060
dc.language.isoen
dc.subjectInduced seismicity
dc.subjectGround motion models
dc.subjectStructural fragility models
dc.subjectRisk assessment
dc.subjectMachine learning techniques
dc.subjectSoftware development
dc.titleMachine-learning-based models, methods, and software for intensity, vulnerability, and risk assessment of Central U.S. induced earthquakes
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentCivil, Architectural, and Environmental Engineering
thesis.degree.disciplineCivil Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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