{"673528":{"#nid":"673528","#data":{"type":"event","title":"PhD Defense by Mingshu Li","body":[{"value":"\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003EPh.D. Thesis Defense Announcement\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EEnhanced Construction Cost Estimation of Highway Projects using Emerging Statistical and Machine Learning Techniques\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003EBy\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EMingshu Li\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003EAdvisor(s)\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EDr. Baabak Ashuri (CEE\/BC)\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003ECommittee Members:\u003C\/span\u003E\u003C\/strong\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EDr. Baabak Ashuri (CEE\/BC), Dr. Patricia L. Mokhtarian: (CEE), Dr. Eric Marks (CEE),\u0026nbsp; Dr. Polo Chau (CSE), Dr. Minsoo Baek (CM, KSU)\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003EDate \u0026amp; Time\u003C\/span\u003E\u003C\/strong\u003E\u003Cspan\u003E: March 29th, 3:30 pm\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003ELocation: \u003C\/span\u003E\u003C\/strong\u003E\u003Cspan\u003EVirtual \u003Ca href=\u0022https:\/\/teams.microsoft.com\/l\/meetup-join\/19%3ameeting_OWNhYzA5MTUtMmJkOS00MDUzLTkwZDMtYjBmYjA2ZmYxM2U3%40thread.v2\/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%2211c7e901-bf32-4624-aa0f-d3b286555d3d%22%7d\u0022\u003Ehttps:\/\/teams.microsoft.com\/l\/meetup-join\/19%3ameeting_OWNhYzA5MTUtMmJkOS00MDUzLTkwZDMtYjBmYjA2ZmYxM2U3%40thread.v2\/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%2211c7e901-bf32-4624-aa0f-d3b286555d3d%22%7d\u003C\/a\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EAbstract\u003Cbr \/\u003E\r\nSeveral state departments of transporta\udbc0\udd9fon (state DOTs) have encountered significant\u003Cbr \/\u003E\r\nchallenges in accurately es\udbc0\udd9fma\udbc0\udd9fng costs for their highway projects, o\udbc0\udd4cen resul\udbc0\udd9fng in\u003Cbr \/\u003E\r\ndiscrepancies between the states\u2019 DOT es\udbc0\udd9fmates (owner\u2019s es\udbc0\udd9fmates) and contractors\u2019\u003Cbr \/\u003E\r\nsubmi\udbc0\udda9ed bids. These inaccuracies can lead to cost overrun, scope change, schedule delay,\u003Cbr \/\u003E\r\npostponement, and cancella\udbc0\udd9fon of transporta\udbc0\udd9fon projects, which are problema\udbc0\udd9fc for both\u003Cbr \/\u003E\r\nowner organiza\udbc0\udd9fons and highway contractors. There is a cri\udbc0\udd9fcal need to enhance the quality of\u003Cbr \/\u003E\r\nconstruc\udbc0\udd9fon cost es\udbc0\udd9fmates to efficiently allocate public funds and increase confidence in\u003Cbr \/\u003E\r\nengineer\u2019s es\udbc0\udd9fmates. Addressing this need, the overarching objec\udbc0\udd9fve of this research is to\u003Cbr \/\u003E\r\nadvance construc\udbc0\udd9fon cost es\udbc0\udd9fma\udbc0\udd9fon for highway projects through the applica\udbc0\udd9fon of emerging\u003Cbr \/\u003E\r\nsta\udbc0\udd9fs\udbc0\udd9fcal modeling and machine learning techniques, examining cost es\udbc0\udd9fma\udbc0\udd9fon at varying\u003Cbr \/\u003E\r\nlevels of granularity for a comprehensive analysis.\u003Cbr \/\u003E\r\nThe study first adopts a temporal perspec\udbc0\udd9fve at the monthly level, inves\udbc0\udd9fga\udbc0\udd9fng risk factors that\u003Cbr \/\u003E\r\naffect the accuracy of the owner\u2019s es\udbc0\udd9fmate. This level of analysis allows for the examina\udbc0\udd9fon of\u003Cbr \/\u003E\r\nseveral variables represen\udbc0\udd9fng the local highway construc\udbc0\udd9fon market, overall construc\udbc0\udd9fon\u0026nbsp;market, macroeconomic condi\udbc0\udd9fons, and the energy market to iden\udbc0\udd9ffy leading indicators of the\u003Cbr \/\u003E\r\nra\udbc0\udd9fo of low bid to owner\u2019s es\udbc0\udd9fmate. Appropriate \udbc0\udd9fme-series models, such as ARIMAX, will be\u003Cbr \/\u003E\r\napplied to forecast this ra\udbc0\udd9fo using iden\udbc0\udd9ffied leading indicators. This macro-level analysis offers\u003Cbr \/\u003E\r\nfounda\udbc0\udd9fonal insights into market trends and economic factors influencing cost es\udbc0\udd9fma\udbc0\udd9fons,\u003Cbr \/\u003E\r\nse\udbc0\uddabng the stage for more detailed inves\udbc0\udd9fga\udbc0\udd9fons.\u003Cbr \/\u003E\r\nTransi\udbc0\udd9foning to the project level, the research conducts survival analysis to assess the\u003Cbr \/\u003E\r\nrela\udbc0\udd9fonship between several poten\udbc0\udd9fal drivers and the likelihood of inaccurate cost es\udbc0\udd9fma\udbc0\udd9fon.\u003Cbr \/\u003E\r\nBy innova\udbc0\udd9fvely applying concepts and methods from survival analysis to construc\udbc0\udd9fon cost\u003Cbr \/\u003E\r\nes\udbc0\udd9fma\udbc0\udd9fon, this part of the study explores the impact of project-specific, bidder-specific, and\u003Cbr \/\u003E\r\nexternal market characteris\udbc0\udd9fcs on es\udbc0\udd9fma\udbc0\udd9fon accuracy. This project-level analysis provides\u003Cbr \/\u003E\r\ncri\udbc0\udd9fcal insights into the dynamics at play within individual projects, complemen\udbc0\udd9fng the broader\u003Cbr \/\u003E\r\nmarket perspec\udbc0\udd9fve obtained from the temporal analysis.\u003Cbr \/\u003E\r\nFinally, at the most granular pay item level, forecas\udbc0\udd9fng models for early-phase cost es\udbc0\udd9fma\udbc0\udd9fon\u003Cbr \/\u003E\r\nof lump sum pay items (Traffic Control and Grading Complete) are developed using text-mining\u003Cbr \/\u003E\r\nand machine learning techniques. This approach involves retrieving project informa\udbc0\udd9fon\u003Cbr \/\u003E\r\navailable at the early stages of project development through text analysis and examining various\u003Cbr \/\u003E\r\nmachine learning algorithms with iden\udbc0\udd9ffied key predic\udbc0\udd9fve features to select the bestperforming\u003Cbr \/\u003E\r\nmodel. By focusing on specific pay items, this level of analysis directly addresses the\u003Cbr \/\u003E\r\nprac\udbc0\udd9fcal needs of designers and cost es\udbc0\udd9fmators, offering precise tools for early cost es\udbc0\udd9fma\udbc0\udd9fon\u003Cbr \/\u003E\r\nand further enriching the comprehensive understanding gained from the previous analyses.\u003Cbr \/\u003E\r\nThis research contributes to the body of knowledge through: (1) developing appropriate\u003Cbr \/\u003E\r\nmul\udbc0\udd9fvariate \udbc0\udd9fme-series models (i.e., ARIMAX models) to predict the ra\udbc0\udd9fo of low bid to owner\u2019s\u003Cbr \/\u003E\r\nes\udbc0\udd9fmate; (2) crea\udbc0\udd9fng a Cox propor\udbc0\udd9fonal hazards model to explain and predict the likelihood of\u003Cbr \/\u003E\r\ninaccurate cost es\udbc0\udd9fmates; (3) developing machine learning algorithms to accurately es\udbc0\udd9fmate\u003Cbr \/\u003E\r\nprices of lump sum pay item at early stages of project development. It is an\udbc0\udd9fcipated that the\u003Cbr \/\u003E\r\nresearch outcome would help cost es\udbc0\udd9fma\udbc0\udd9fng professionals in transporta\udbc0\udd9fon agencies be\udbc0\udda9er\u003Cbr \/\u003E\r\nunderstand the risk factors and poten\udbc0\udd9fal drivers of the devia\udbc0\udd9fon between owner\u2019s es\udbc0\udd9fmate and\u003Cbr \/\u003E\r\nlow bids, prepare more accurate cost es\udbc0\udd9fmates and develop appropriate risk management\u003Cbr \/\u003E\r\nstrategies for enhanced decision-making. Through its mul\udbc0\udd9f-level analysis, the study provides\u003Cbr \/\u003E\r\nsignificant insights into project planning, budget alloca\udbc0\udd9fon, and construc\udbc0\udd9fon cost management,\u003Cbr \/\u003E\r\nthereby underscoring the cri\udbc0\udd9fcal role of integra\udbc0\udd9fng machine learning and sta\udbc0\udd9fs\udbc0\udd9fcal modeling\u003Cbr \/\u003E\r\ntechniques in enhancing the accuracy and reliability of cost es\udbc0\udd9fma\udbc0\udd9fons for highway projects.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EEnhanced Construction Cost Estimation of Highway Projects using Emerging Statistical and Machine Learning Techniques\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Enhanced Construction Cost Estimation of Highway Projects using Emerging Statistical and Machine Learning Techniques"}],"uid":"27707","created_gmt":"2024-03-14 15:37:58","changed_gmt":"2024-03-14 15:37:57","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-03-29T15:30:00-04:00","event_time_end":"2024-03-29T17:00:00-04:00","event_time_end_last":"2024-03-29T17:00:00-04:00","gmt_time_start":"2024-03-29 19:30:00","gmt_time_end":"2024-03-29 21:00:00","gmt_time_end_last":"2024-03-29 21:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Virtual","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":""}}}