{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/92910"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/92910","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Model-driven visual data analytics for monitoring work-in-progress on construction sites","abstract":"This Dissertation was approved for publication on 2016-06-29 at 16:51.","abstract_html":"This Dissertation was approved for publication on 2016-06-29 at 16:51.","abstract_has_math":false,"creators":["Han, Kook In"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Golparvar-Fard, Mani","Liu, Liang","Hoiem, Derek","El-Rayes, Khaled","Haas, Carl"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-11-10T18:27:35Z","date_published":"2016-11-10T18:27:35Z","updated_at":"2026-07-22T22:26:35Z","subjects":["Construction Progress Monitoring","Computer Vision","Sequencing Knowledge","Building Information Modeling","Material Classification","Big Visual Data"],"languages":["en"],"rights":["Copyright 2016 Kook In Han"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/92910","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Golparvar-Fard, Mani","Liu, Liang","Hoiem, Derek","El-Rayes, Khaled","Haas, Carl"]},{"key":"dc:creator","label":"Author","values":["Han, Kook In"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-11-10T18:27:35Z","2018-11-11T10:15:12Z","2016-06-29","2016-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Construction Progress Monitoring","Computer Vision","Sequencing Knowledge","Building Information Modeling","Material Classification","Big Visual Data"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Kook In Han"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/92910"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This Dissertation was approved for publication on 2016-06-29 at 16:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9719 on 2016-11-10 at 12:19:45","Made available in DSpace on 2016-11-10T18:27:35Z (GMT). No. of bitstreams: 2 HAN-DISSERTATION-2016.pdf: 123199154 bytes, checksum: 3028608400b9c83acfce796d80d2da51 (MD5) LICENSE.txt: 4205 bytes, checksum: 3e993492b23c056aab60a37b60755159 (MD5) Previous issue date: 2016-06-29","Adherence to project schedules and budgets is the most highly valued performance metric among project owners. Despite its significance, most projects struggle to keep track of accurate as-built status. More than 53% of typical construction projects are behind schedule, and more than 66% do not meet their budget requirements. A few major factors accounting for such delays and cost overruns include 1) inconsistency among contractors, subcontractors and owners in terms of how much a construction project is faring at any given date, 2) flawed performance management due to lack of infrequent reporting of actual performance to project teams, and 3) planners' missed connections to most up-to-date construction progress information. To address these inefficiencies and contribute to the National Research Council (NRC)'s goal of improving efficiency in the construction industry, this dissertation proposes a construction progress monitoring framework that streamlines the utilization of existing large collections of site photographs – captured with consumer grade cameras, commodity smartphones as well as Unmanned Aerial Vehicles (UAV) - together with Building Information Modeling (BIM) for automated detection, analysis, and visualization of progress deviations at the operation level. To do so, several computer vision algorithms are developed to automatically create 4D as-built point clouds using the collected images (with or without using BIM as a priori) and to automatically analyze their deviations with as-planned 4D BIM models based on both geometry and visual appearance features. A reasoning mechanism based on formalized sequences of construction activities and inter-dependency of BIM elements is also presented which alleviates problems associated with limited visibility in visual capture processes, as well as lack of details in as-planned representations. To enhance practicality of the proposed framework, a crowdsourcing method is also proposed to enhance the accuracy and completeness of the visual data that is necessary to train the underlying machine learning algorithm used for visual data analytics. The proposed methods are validated using a large range of real-world construction datasets under normal and challenging conditions. The results of applying these computer vision methods show that appearance based recognition of construction materials and their comparison to BIM (with or without using geometry) outperforms the state of the art geometrical based method. Feedbacks from industry practitioners also show that reasoning based methods are acceptable for inferring progress for incomplete site datasets. The framework provides an easy and quick solution for project-level monitoring and provides project teams with a mechanism for better understanding of how a project compares with others in terms of cost, schedule, and labor hours. It also enhanced communication by providing real-time project information, improving onsite decision-making and work-sequencing, and fostering collaborative partnerships.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-08-01","The student, Kook Han, accepted the attached license on 2016-06-29 at 09:29.","The student, Kook Han, submitted this Dissertation for approval on 2016-06-29 at 09:58.","Embargo set by: Seth Robbins for item 95330 Lift date: 2018-11-10T18:28:02Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 95330 on 2018-11-11T10:15:12Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Model-driven visual data analytics for monitoring work-in-progress on construction sites"]}]}],"canonical_facts":{"dc:contributor":["Golparvar-Fard, Mani","Liu, Liang","Hoiem, Derek","El-Rayes, Khaled","Haas, Carl"],"dc:creator":["Han, Kook In"],"dc:date":["2016-11-10T18:27:35Z","2018-11-11T10:15:12Z","2016-06-29","2016-08"],"dc:description":["This Dissertation was approved for publication on 2016-06-29 at 16:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9719 on 2016-11-10 at 12:19:45","Made available in DSpace on 2016-11-10T18:27:35Z (GMT). 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To address these inefficiencies and contribute to the National Research Council (NRC)'s goal of improving efficiency in the construction industry, this dissertation proposes a construction progress monitoring framework that streamlines the utilization of existing large collections of site photographs – captured with consumer grade cameras, commodity smartphones as well as Unmanned Aerial Vehicles (UAV) - together with Building Information Modeling (BIM) for automated detection, analysis, and visualization of progress deviations at the operation level. To do so, several computer vision algorithms are developed to automatically create 4D as-built point clouds using the collected images (with or without using BIM as a priori) and to automatically analyze their deviations with as-planned 4D BIM models based on both geometry and visual appearance features. A reasoning mechanism based on formalized sequences of construction activities and inter-dependency of BIM elements is also presented which alleviates problems associated with limited visibility in visual capture processes, as well as lack of details in as-planned representations. To enhance practicality of the proposed framework, a crowdsourcing method is also proposed to enhance the accuracy and completeness of the visual data that is necessary to train the underlying machine learning algorithm used for visual data analytics. The proposed methods are validated using a large range of real-world construction datasets under normal and challenging conditions. The results of applying these computer vision methods show that appearance based recognition of construction materials and their comparison to BIM (with or without using geometry) outperforms the state of the art geometrical based method. Feedbacks from industry practitioners also show that reasoning based methods are acceptable for inferring progress for incomplete site datasets. The framework provides an easy and quick solution for project-level monitoring and provides project teams with a mechanism for better understanding of how a project compares with others in terms of cost, schedule, and labor hours. It also enhanced communication by providing real-time project information, improving onsite decision-making and work-sequencing, and fostering collaborative partnerships.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-08-01","The student, Kook Han, accepted the attached license on 2016-06-29 at 09:29.","The student, Kook Han, submitted this Dissertation for approval on 2016-06-29 at 09:58.","Embargo set by: Seth Robbins for item 95330 Lift date: 2018-11-10T18:28:02Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 95330 on 2018-11-11T10:15:12Z."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/92910"],"dc:language":["en"],"dc:rights":["Copyright 2016 Kook In Han"],"dc:subject":["Construction Progress Monitoring","Computer Vision","Sequencing Knowledge","Building Information Modeling","Material Classification","Big Visual Data"],"dc:title":["Model-driven visual data analytics for monitoring work-in-progress on construction sites"],"dc:type":["text"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:35Z"}