{"681456":{"#nid":"681456","#data":{"type":"event","title":"PhD Defense by Chenying Liu","body":[{"value":"\u003Cp\u003ESchool of Civil and Environmental Engineering\u003C\/p\u003E\u003Cp\u003EPh.D. Thesis Defense Announcement\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETowards the Next Generation of Data-driven and Physics-informed Procedures for the Seismic Performance Assessment of Geosystems\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EBy\u003Cstrong\u003E\u0026nbsp;Chenying Liu\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EAdvisor:\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDr. Jorge Macedo\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003ECommittee Members:\u003Cstrong\u003E\u0026nbsp; Dr. David Frost (CEE), Dr. Zhigang Peng (EAS), Dr. Mahdi Roozbahani (CSE), Dr. Norman Abrahamson (CEE, UC Berkeley), Dr. Jack Baker (CEE, Stanford)\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EDate and Time:\u003Cstrong\u003E\u0026nbsp; April 11, 2025.\u0026nbsp; 9:00AM - 12:00 PM\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003ELocation:\u0026nbsp;SEB122 \u0026nbsp;\/\u003Cstrong\u003E\u0026nbsp;\u003C\/strong\u003E\u003Ca href=\u0022https:\/\/gatech.zoom.us\/j\/4205093739\u0022\u003E\u003Cstrong\u003Ehttps:\/\/gatech.zoom.us\/j\/4205093739\u003C\/strong\u003E\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003EABSTRACT\u003Cbr\u003EPerformance-based engineering (PBE) is widely recognized as the leading framework for the rigorous assessment of engineering systems subjected to extreme events. Over the past 60 years, earthquake engineering has evolved from deterministic approaches\u2014which evaluate only a limited number of assumed-to-be-conservative scenarios\u2014toward performance-based methods that explicitly account for uncertainty. While many recent advances in earthquake engineering have been driven by PBE, important challenges remain.\u003Cbr\u003EEmerging data-driven methods present a unique opportunity to advance performance-based earthquake engineering, particularly for geotechnical systems. However, their successful application requires the integration of physical principles, as design scenarios frequently involve extreme events\u2014such as the \u201cMaximum Credible Earthquake\u201d specified in seismic codes\u2014where extrapolation beyond available data is often necessary.\u003Cbr\u003EThis study contributes to the development of next-generation data-driven and physics-informed approaches in geotechnical earthquake engineering. It introduces new conditional ground motion models that enable hazard-consistent estimation of ground motion intensity measures, aligning more closely with performance-based assessment frameworks. In addition, it proposes new nonergodic ground motion models that capture repeatable effects of earthquake sources, recording stations, and wave propagation paths, leading to improved uncertainty quantification for regions like California and Turkey. The work also investigates the implications of the nonergodic paradigm\u2014traditionally applied in seismic demand estimation\u2014for broader seismic risk assessment, including regional liquefaction hazard and the resilience of distributed energy resource systems in specific areas of California.\u003Cbr\u003EFurther, the study develops machine learning\u2013 and deep learning\u2013based semi-empirical models to evaluate the seismic performance of slope systems in subduction zones and buildings in urban areas underlain by liquefiable soils. These models demonstrate more robust median predictions and reduced uncertainty compared to traditional approaches. The study also explores the potential of physics-informed neural networks as efficient surrogates for traditional numerical methods, which, despite decades of development, still face practical and computational limitations. The study concludes with a perspective on the future of performance-based engineering in the context of emerging computational and data-driven paradigms\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ETowards the Next Generation of Data-driven and Physics-informed Procedures for the Seismic Performance Assessment of Geosystems\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Towards the Next Generation of Data-driven and Physics-informed Procedures for the Seismic Performance Assessment of Geosystems"}],"uid":"27707","created_gmt":"2025-03-31 14:16:56","changed_gmt":"2025-03-31 14:17:28","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-04-11T09:00:00-04:00","event_time_end":"2025-04-11T11:00:00-04:00","event_time_end_last":"2025-04-11T11:00:00-04:00","gmt_time_start":"2025-04-11 13:00:00","gmt_time_end":"2025-04-11 15:00:00","gmt_time_end_last":"2025-04-11 15:00:00","rrule":null,"timezone":"America\/New_York"},"location":"SEB122  ","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}