[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120461-en":3,"doc-seo-120461-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120461,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","BIM Clash Report Analysis Using Machine Learning Algorithms","Clash detection is presented as a key BIM application, yet clash resolution remains manual, slow, and limited by incomplete use of BIM information. This thesis addresses gaps in prior work by using machine learning to improve model coordination decisions and by identifying the attribute combinations needed to anticipate clash significance accurately. The research investigates categorizing clashes through image recognition and numerical data, achieving precision above 80% with CNN-based approaches and supervised YOLOv8-driven image recognition. A predictive clash-significance model enables more efficient coordination meetings.","BIM Clash Report Analysis Using Machine Learning Algorithms  \nby  \nIbironke Regina Adegun  \nA thesis submitted in partial fulfillment of the requirements for the degree  \nof  \nMaster of Science  \nin  \nConstruction Engineering and Management  \nDepartment of Civil and Environmental Engineering  \nUniversity of Alberta  \n© Ibironke Regina Adegun, 2024  \nABSTRACT  \nClash detection has been argued as one of the most beneficial BIM (Building Information Modelling) applications. However, the clash resolution process is still manually conducted and time-consuming, and BIM information is not fully utilized to facilitate automatic clash resolution.  \nPrevious research employed machine learning and data mining methodologies to examine model coordination information and enhance the process of decision-making. Nevertheless, there are still deficiencies. Moreover, no prior study has pinpointed the attribute combination required for the precise anticipation of clash significance.  \nThis research explores machine learning through two main avenues: firstly, categorizing clashes by image recognition and numerical data. Applying a Convolutional Neural Network multilayer (CNN) algorithm to different combinations of clashes achieved a precision of over 80% . The image clash recognition algorithm was also developed using YOLO v8’s supervised CNN algorithm.  \nBy forecasting clash significance with a high level of accuracy and recognizing the essential characteristics, this research makes a valuable contribution to the field of study within BIM and model coordination. Previous research had overlooked the collection of clashes across all disciplines and the identification of critical attribute combinations that result in accurate predictions. Furthermore, the development of a predictive model for clash significance presents new possibilities for professionals in the industry to enhance the efficiency of model coordination meetings by considering the disciplines, elements, and volumes ofthe clashes.  \nPREFACE  \nThis thesis is an original work by Ibironke Regina Adegun completed under the supervision of Dr. Mohamed Al-Hussein.  \nThis is an academic work that can be used by third parties, as long as internationally accepted rules and good practices are respected, particularly in what concerns author rights and related matters.  \nACKNOWLEDGMENTS  \nFirstly, I would like to give all glory to the almighty God for making my dreams come true by being a part of the construction management Master’s program at the University of Alberta. I am also thankful to my parents for their emotional and motivational support not just for this dissertation but all my life.  \nThank you to my supervisor Dr. Mohamed Al-Hussein for the access, availability, expert guidance, and resources provided throughout the course of this dissertation.  \nThank you to my co-supervisor Dr Ahmed Bouferguene for the timely advice and insight into the dissertation topic. I would like to extend my gratitude to Hamida Mokhtari and, Jonathan Tomalty.  \nFinally, thank you to Charles, my siblings and friends for cheering me on and supporting me in many ways.  \nTABLE OF CONTENTS  \nABSTRACT ..................................................................................................................... ii  \nPREFACE ....................................................................................................................... iii  \nACKNOWLEDGMENTS...............................................................................................iv  \nTABLE OF CONTENTS.................................................................................................v  \nLIST OF FIGURES ........................................................................................................ix  \nLIST OF TABLES ..........................................................................................................xi  \nLIST OF ABBREVIATIONS....................................................................","cbCaid9iHn6yEmCo","https://ap.wps.com/l/cbCaid9iHn6yEmCo","pdf",6293068,1,125,"English","en",105,"# Chapter 1 Introduction\n## Background and Motivation\n## Research objectives\n## Thesis organization\n# Chapter 2 Literature Review\n## BIM Coordination\n## Issues facing BIM Coordination\n## Clash Detection Evolution\n## Frameworks and Tools Promoting Clash Avoidance\n## Review of Causes of Clashes and Clash Avoidance Strategies\n## Design conflict resolution\n## Knowledge Gaps in BIM Coordination\n## Clash Detection\n## Machine Learning\n## ML Applications in Construction\n## Evolution of Machine Learning\n## Shallow learning","[{\"question\":\"Why is clash detection important in BIM, and what limitation remains?\",\"answer\":\"Clash detection is valuable for BIM model coordination, but clash resolution is still largely manual and time-consuming. BIM information is also not fully leveraged for automatic clash resolution.\"},{\"question\":\"How does the thesis use machine learning to handle BIM clashes?\",\"answer\":\"It explores two avenues: categorizing clashes using image recognition and numerical data. A CNN-based approach and a supervised YOLOv8-driven image recognition method are applied to different clash combinations.\"},{\"question\":\"What contribution does the thesis claim regarding clash significance prediction?\",\"answer\":\"It forecasts clash significance with high accuracy by recognizing essential characteristics and attribute combinations. It also aims to support professionals by improving the efficiency of model coordination meetings across disciplines, elements, and volumes.\"}]","BIM Clash Report Analysis Using Machine Learning Algorithms | PDF",1785730216,315,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"bim-clash-report-analysis-using-machine-learning-algorithms","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/bim-clash-report-analysis-using-machine-learning-algorithms/120461/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is clash detection important in BIM, and what limitation remains?","Question",{"text":75,"@type":76},"Clash detection is valuable for BIM model coordination, but clash resolution is still largely manual and time-consuming. BIM information is also not fully leveraged for automatic clash resolution.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the thesis use machine learning to handle BIM clashes?",{"text":80,"@type":76},"It explores two avenues: categorizing clashes using image recognition and numerical data. A CNN-based approach and a supervised YOLOv8-driven image recognition method are applied to different clash combinations.",{"name":82,"@type":73,"acceptedAnswer":83},"What contribution does the thesis claim regarding clash significance prediction?",{"text":84,"@type":76},"It forecasts clash significance with high accuracy by recognizing essential characteristics and attribute combinations. It also aims to support professionals by improving the efficiency of model coordination meetings across disciplines, elements, and volumes.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]