{"664753":{"#nid":"664753","#data":{"type":"event","title":"Ph.D. Proposal Oral Exam - Taeyong Shin","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u0026nbsp; \u003C\/strong\u003E\u003Cem\u003EMachine Learning-Based Structural Health Monitoring of Concrete Structures using Acoustic Signals\u003C\/em\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee:\u0026nbsp; \u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Ying Zhang, Advisor\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Moore, Chair\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Ma\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract: \u003C\/strong\u003EThe objective of the proposed research is to develop a generalized data-driven approach for the assessment of damage in concrete structures while minimizing the need for labeled data. The preliminary research includes a damage detection model which utilized an autoencoder for anomaly detection and a meta-learning approach which demonstrated the feasibility of a few-shot learning model for damage severity assessment of concrete structures. The proposed research focuses on combining probabilistic learning with meta-learning to provide a prediction of damage severity while taking into consideration the uncertainty of measurements.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Machine Learning-Based Structural Health Monitoring of Concrete Structures using Acoustic Signals"}],"uid":"28475","created_gmt":"2023-01-13 21:52:48","changed_gmt":"2023-01-13 21:52:48","author":"Daniela Staiculescu","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2023-01-18T10:00:00-05:00","event_time_end":"2023-01-18T12:00:00-05:00","event_time_end_last":"2023-01-18T12:00:00-05:00","gmt_time_start":"2023-01-18 15:00:00","gmt_time_end":"2023-01-18 17:00:00","gmt_time_end_last":"2023-01-18 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"434371","name":"ECE Ph.D. Proposal Oral Exams"}],"categories":[],"keywords":[{"id":"102851","name":"Phd proposal"},{"id":"1808","name":"graduate students"}],"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":""}}}