{"674569":{"#nid":"674569","#data":{"type":"event","title":"PhD Defense by Frederick Chung","body":[{"value":"\u003Cp\u003E\u003Cem\u003EAnnounced 9 days in advance of the defense with approval from CEE Graduate Committee and Graduate Education Thesis Office.\u003C\/em\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ESchool of Civil and Environmental Engineering\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EPh.D. Thesis Defense Announcement\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EApplication\u0026nbsp;of\u0026nbsp;Data\u0026nbsp;Analytics\u0026nbsp;and\u0026nbsp;Machine\u0026nbsp;Learning\u0026nbsp;Methods\u0026nbsp;to\u0026nbsp;Enhance\u0026nbsp;Decision-Making\u0026nbsp;in\u0026nbsp;Right-Of-Way\u0026nbsp;Acquisition\u0026nbsp;Process\u0026nbsp;and\u0026nbsp;Transportation\u0026nbsp;Asset\u0026nbsp;Management\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EBy\u003C\/strong\u003E\u0026nbsp;Frederick\u0026nbsp;Chung\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAdvisor:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr.\u0026nbsp;Baabak\u0026nbsp;Ashuri\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee Members:\u003C\/strong\u003E\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;Dr.\u0026nbsp;Susan\u0026nbsp;Burns\u0026nbsp;(CEE),\u0026nbsp;Dr.\u0026nbsp;Eric\u0026nbsp;Mark\u0026nbsp;(CEE),\u0026nbsp;Dr.\u0026nbsp;Xiuwei\u0026nbsp;Zhang\u0026nbsp;(CSE),\u0026nbsp;and\u0026nbsp;Dr.\u0026nbsp;Minsoo\u0026nbsp;Baek\u0026nbsp;(KSU)\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EDate and Time:\u003C\/strong\u003E\u0026nbsp; May\u0026nbsp;16th,\u0026nbsp;2024,\u0026nbsp;12:00 PM\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ELocation: \u003C\/strong\u003EMicrosoft\u0026nbsp;Teams\u0026nbsp;(Meeting\u0026nbsp;ID:\u0026nbsp;215\u0026nbsp;398\u0026nbsp;934\u0026nbsp;673;\u0026nbsp;Passcode:\u0026nbsp;28KyNZ)\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EWith\u0026nbsp;the\u0026nbsp;increasing\u0026nbsp;project\u0026nbsp;complexity\u0026nbsp;and\u0026nbsp;evolving\u0026nbsp;conditions\u0026nbsp;surrounding\u0026nbsp;the\u0026nbsp;uncertain\u0026nbsp;project\u0026nbsp;environment,\u0026nbsp;transportation\u0026nbsp;agencies\u0026nbsp;face\u0026nbsp;a\u0026nbsp;great\u0026nbsp;challenge\u0026nbsp;in\u0026nbsp;making\u0026nbsp;optimal\u0026nbsp;decisions\u0026nbsp;in\u0026nbsp;project\u0026nbsp;management.\u0026nbsp;Budgets\u0026nbsp;allocated\u0026nbsp;for\u0026nbsp;transportation\u0026nbsp;projects\u0026nbsp;remain\u0026nbsp;constrained,\u0026nbsp;thereby\u0026nbsp;creating\u0026nbsp;a\u0026nbsp;pressing\u0026nbsp;need\u0026nbsp;for\u0026nbsp;judicious\u0026nbsp;decision-making\u0026nbsp;to\u0026nbsp;ensure\u0026nbsp;the\u0026nbsp;efficient\u0026nbsp;utilization\u0026nbsp;of\u0026nbsp;funds.\u0026nbsp;Given\u0026nbsp;the\u0026nbsp;requirement\u0026nbsp;to\u0026nbsp;address\u0026nbsp;public\u0026nbsp;needs\u0026nbsp;of\u0026nbsp;infrastructure\u0026nbsp;while\u0026nbsp;working\u0026nbsp;within\u0026nbsp;limited\u0026nbsp;budgets,\u0026nbsp;there\u0026nbsp;is\u0026nbsp;a\u0026nbsp;significant\u0026nbsp;necessity\u0026nbsp;to\u0026nbsp;enhance\u0026nbsp;decision-making\u0026nbsp;by\u0026nbsp;exploring\u0026nbsp;application\u0026nbsp;of\u0026nbsp;advanced\u0026nbsp;data\u0026nbsp;analytics\u0026nbsp;and\u0026nbsp;machine\u0026nbsp;learning\u0026nbsp;methodologies.\u0026nbsp;The\u0026nbsp;development\u0026nbsp;of\u0026nbsp;digital\u0026nbsp;tools\u0026nbsp;and\u0026nbsp;platforms\u0026nbsp;has\u0026nbsp;facilitated\u0026nbsp;the\u0026nbsp;collection\u0026nbsp;and\u0026nbsp;storage\u0026nbsp;of\u0026nbsp;vast\u0026nbsp;amount\u0026nbsp;of\u0026nbsp;data\u0026nbsp;pertaining\u0026nbsp;to\u0026nbsp;the\u0026nbsp;performance\u0026nbsp;of\u0026nbsp;infrastructure\u0026nbsp;systems\u0026nbsp;and\u0026nbsp;the\u0026nbsp;history\u0026nbsp;of\u0026nbsp;various\u0026nbsp;projects.\u0026nbsp;The\u0026nbsp;availability\u0026nbsp;of\u0026nbsp;information\u0026nbsp;is\u0026nbsp;opening\u0026nbsp;unique\u0026nbsp;opportunities\u0026nbsp;to\u0026nbsp;analyze\u0026nbsp;past\u0026nbsp;performance,\u0026nbsp;identify\u0026nbsp;patterns,\u0026nbsp;and\u0026nbsp;gain\u0026nbsp;insights\u0026nbsp;that\u0026nbsp;can\u0026nbsp;be\u0026nbsp;used\u0026nbsp;to\u0026nbsp;inform\u0026nbsp;future\u0026nbsp;planning.\u0026nbsp;This\u0026nbsp;research\u0026nbsp;explores\u0026nbsp;implementation\u0026nbsp;of\u0026nbsp;data\u0026nbsp;analytics\u0026nbsp;and\u0026nbsp;machine\u0026nbsp;learning\u0026nbsp;techniques\u0026nbsp;to\u0026nbsp;enhance\u0026nbsp;two\u0026nbsp;crucial\u0026nbsp;project\u0026nbsp;management\u0026nbsp;tasks,\u0026nbsp;Right-Of-Way\u0026nbsp;(ROW)\u0026nbsp;acquisition\u0026nbsp;process\u0026nbsp;and\u0026nbsp;transportation\u0026nbsp;asset\u0026nbsp;management.\u0026nbsp;The\u0026nbsp;first\u0026nbsp;chapter\u0026nbsp;of\u0026nbsp;this\u0026nbsp;research\u0026nbsp;contributes\u0026nbsp;to\u0026nbsp;the\u0026nbsp;state\u0026nbsp;of\u0026nbsp;knowledge\u0026nbsp;in\u0026nbsp;estimating\u0026nbsp;ROW\u0026nbsp;acquisition\u0026nbsp;timeline\u0026nbsp;through\u0026nbsp;developing\u0026nbsp;a\u0026nbsp;novel\u0026nbsp;machine\u0026nbsp;learning\u0026nbsp;model\u0026nbsp;to\u0026nbsp;accurately\u0026nbsp;estimate\u0026nbsp;ROW\u0026nbsp;acquisition\u0026nbsp;timelines,\u0026nbsp;and\u0026nbsp;identifying\u0026nbsp;drivers\u0026nbsp;(i.e.,\u0026nbsp;risk\u0026nbsp;factors)\u0026nbsp;of\u0026nbsp;ROW\u0026nbsp;acquisition\u0026nbsp;durations.\u0026nbsp;The\u0026nbsp;forecasting\u0026nbsp;model\u0026nbsp;developed\u0026nbsp;in\u0026nbsp;this\u0026nbsp;research\u0026nbsp;achieves\u0026nbsp;a\u0026nbsp;high\u0026nbsp;accuracy\u0026nbsp;to\u0026nbsp;predict\u0026nbsp;ROW\u0026nbsp;durations\u0026nbsp;by\u0026nbsp;explaining\u0026nbsp;74%\u0026nbsp;of\u0026nbsp;the\u0026nbsp;variance\u0026nbsp;in\u0026nbsp;ROW\u0026nbsp;acquisition\u0026nbsp;durations\u0026nbsp;using\u0026nbsp;project\u0026nbsp;features.\u0026nbsp;Moreover,\u0026nbsp;number\u0026nbsp;of\u0026nbsp;parcels,\u0026nbsp;average\u0026nbsp;cost\u0026nbsp;estimation\u0026nbsp;per\u0026nbsp;parcel,\u0026nbsp;length\u0026nbsp;of\u0026nbsp;projects,\u0026nbsp;number\u0026nbsp;of\u0026nbsp;condemnations,\u0026nbsp;number\u0026nbsp;of\u0026nbsp;relocations,\u0026nbsp;and\u0026nbsp;type\u0026nbsp;of\u0026nbsp;work\u0026nbsp;are\u0026nbsp;found\u0026nbsp;to\u0026nbsp;be\u0026nbsp;influential\u0026nbsp;factors\u0026nbsp;as\u0026nbsp;drivers\u0026nbsp;of\u0026nbsp;ROW\u0026nbsp;acquisition\u0026nbsp;duration.\u0026nbsp;The\u0026nbsp;second\u0026nbsp;chapter\u0026nbsp;of\u0026nbsp;this\u0026nbsp;research\u0026nbsp;contributes\u0026nbsp;to\u0026nbsp;the\u0026nbsp;body\u0026nbsp;of\u0026nbsp;knowledge\u0026nbsp;in\u0026nbsp;improving\u0026nbsp;the\u0026nbsp;prediction\u0026nbsp;of\u0026nbsp;pavement\u0026nbsp;condition\u0026nbsp;by\u0026nbsp;developing\u0026nbsp;machine\u0026nbsp;learning\u0026nbsp;models\u0026nbsp;and\u0026nbsp;implementing\u0026nbsp;ensemble\u0026nbsp;methods\u0026nbsp;to\u0026nbsp;enhance\u0026nbsp;predictive\u0026nbsp;performance.\u0026nbsp;This\u0026nbsp;research\u0026nbsp;focuses\u0026nbsp;on\u0026nbsp;developing\u0026nbsp;five\u0026nbsp;machine\u0026nbsp;learning\u0026nbsp;classification\u0026nbsp;models,\u0026nbsp;Random\u0026nbsp;Forest,\u0026nbsp;Gradient\u0026nbsp;Boosting,\u0026nbsp;Support\u0026nbsp;Vector\u0026nbsp;Machine,\u0026nbsp;K-Nearest\u0026nbsp;Neighbor,\u0026nbsp;and\u0026nbsp;Artificial\u0026nbsp;Neural\u0026nbsp;Network,\u0026nbsp;to\u0026nbsp;predict\u0026nbsp;pavement\u0026nbsp;condition\u0026nbsp;levels.\u0026nbsp;To\u0026nbsp;enhance\u0026nbsp;prediction\u0026nbsp;performance,\u0026nbsp;ensemble\u0026nbsp;methods,\u0026nbsp;including\u0026nbsp;voting\u0026nbsp;and\u0026nbsp;stacking,\u0026nbsp;are\u0026nbsp;integrated.\u0026nbsp;Voting\u0026nbsp;ensemble\u0026nbsp;model\u0026nbsp;constructed\u0026nbsp;with\u0026nbsp;the\u0026nbsp;two\u0026nbsp;best-performing\u0026nbsp;individual\u0026nbsp;classification\u0026nbsp;models\u0026nbsp;reaches\u0026nbsp;the\u0026nbsp;highest\u0026nbsp;accuracy\u0026nbsp;rate\u0026nbsp;at\u0026nbsp;83%.\u0026nbsp;Although\u0026nbsp;the\u0026nbsp;performance\u0026nbsp;of\u0026nbsp;individual\u0026nbsp;models\u0026nbsp;fluctuates,\u0026nbsp;ensemble\u0026nbsp;models\u0026nbsp;consistently\u0026nbsp;produce\u0026nbsp;top-tier\u0026nbsp;performance\u0026nbsp;irrespective\u0026nbsp;of\u0026nbsp;the\u0026nbsp;data\u0026nbsp;sampling\u0026nbsp;variations.\u0026nbsp;Therefore,\u0026nbsp;ensemble\u0026nbsp;methods\u0026nbsp;are\u0026nbsp;recommended\u0026nbsp;in\u0026nbsp;developing\u0026nbsp;pavement\u0026nbsp;condition\u0026nbsp;prediction\u0026nbsp;models\u0026nbsp;to\u0026nbsp;enhance\u0026nbsp;accuracy\u0026nbsp;and\u0026nbsp;achieve\u0026nbsp;more\u0026nbsp;consistent\u0026nbsp;quality\u0026nbsp;of\u0026nbsp;predictions.\u0026nbsp;The\u0026nbsp;findings\u0026nbsp;of\u0026nbsp;this\u0026nbsp;research\u0026nbsp;will\u0026nbsp;provide\u0026nbsp;transportation\u0026nbsp;agencies\u0026nbsp;with\u0026nbsp;insights\u0026nbsp;on\u0026nbsp;how\u0026nbsp;to\u0026nbsp;improve\u0026nbsp;practices\u0026nbsp;in\u0026nbsp;scheduling\u0026nbsp;ROW\u0026nbsp;acquisition\u0026nbsp;process\u0026nbsp;and\u0026nbsp;improving\u0026nbsp;pavement\u0026nbsp;condition\u0026nbsp;forecasting\u0026nbsp;practices\u0026nbsp;to\u0026nbsp;enhance\u0026nbsp;their\u0026nbsp;maintenance\u0026nbsp;planning\u0026nbsp;and\u0026nbsp;cost\u0026nbsp;savings.\u003Cbr \/\u003E\r\n\u0026nbsp;\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EApplication\u0026nbsp;of\u0026nbsp;Data\u0026nbsp;Analytics\u0026nbsp;and\u0026nbsp;Machine\u0026nbsp;Learning\u0026nbsp;Methods\u0026nbsp;to\u0026nbsp;Enhance\u0026nbsp;Decision-Making\u0026nbsp;in\u0026nbsp;Right-Of-Way\u0026nbsp;Acquisition\u0026nbsp;Process\u0026nbsp;and\u0026nbsp;Transportation\u0026nbsp;Asset\u0026nbsp;Management\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Application of Data Analytics and Machine Learning Methods to Enhance Decision-Making in Right-Of-Way Acquisition Process and Transportation Asset Management"}],"uid":"27707","created_gmt":"2024-05-07 17:54:58","changed_gmt":"2024-05-07 17:56:03","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-05-16T12:00:00-04:00","event_time_end":"2024-05-16T14:00:00-04:00","event_time_end_last":"2024-05-16T14:00:00-04:00","gmt_time_start":"2024-05-16 16:00:00","gmt_time_end":"2024-05-16 18:00:00","gmt_time_end_last":"2024-05-16 18:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Microsoft Teams ","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":""}}}