[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120029-en":3,"doc-seo-120029-105":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},120029,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Quality check of BIM models using machine learning","The complexity of BIM models creates a strong need for accurate, usable data across construction workflows, since missing or incorrect information propagates into construction drawings and drives costly schedule and budget impacts. Artificial intelligence and machine learning offer automated alternatives to manual, rule-based checking for BIM quality assurance. This study applies machine learning to detect anomalies in precast structural wall openings, identifying all openings with wrong embedded information and reducing downstream errors through more reliable construction documentation.","5º CONGRESSO PORTUGUÊS DE BUILDING INFORMATION MODELLING  \nQuality check of BIM models using machine learning  \n[https://doi.org/10.21814/uminho.ed.164.2](https://doi.org/10.21814/uminho.ed.164.2)  \nIraj Esmaeili1, João Poças Martins2, José Miguel Castro3  \n1 PhD Student, Department of Civil Engineering, Faculty of Engineering of the University of Porto, Porto, 0000-0002-4819-0312  \n2 CONSTRUCT-FEUP, BUILT CoLAB, Porto, 0000-0001-9878-3792  \n3 Associate Professor, Department of Civil Engineering, Faculty of Engineering of the University of Porto, Porto, 0000-0001-9732-9969  \nAbstract  \nThe complexity of BIM models challenges the engaged parties to deliver an ac‑ curate model suitable for various purposes. This is especially important during the construction stage, where errors in construction drawings entail considerable cost and time burdens. As a possible solution, artificial intelligence and machine learning (ML) techniques can be deployed to assist BIM parties with the time and resource‑ consuming task of checking the quality of BIM models. This study aims to use ma‑ chine learning techniques to check the quality of BIM models, especially in precast structural wall openings. A machine learning model was used in a BIM model of a project to detect anomalies in openings of precast structural walls, and it was able to detect all the openings with wrong information, which, consequently, would nega‑ tively impact the final delivery of the walls. Considering the applicability of using such an ML model in other projects, the contribution of this study is to reduce the errors in the construction drawings and consequently secure the projects in terms of time and cost burdens due to these errors.  \n30 QUALITY CHECK OF BIM MODELS USING MACHINE LEARNING  \n1. Introduction  \nAs the construction industry moves towards digitalization and adoption of Build‑ ing Information Modelling (BIM), ensuring the quality of BIM models becomes rel‑ evant. Delivering the project within the planned time, budget, and quality is tightly connected to the drawings issued for construction. Since construction drawings are produced from BIM models, missing and incorrect information in BIM models leads to errors in later phases. Therefore, a sound BIM model will produce constructible drawings with fewer errors.  \nMissing and incorrect information can hinder the automation of tasks and jeopard‑ ize the quality of construction output. Due to the large variability of geometries and objects in BIM models, the data embedded in the models cannot be automatically verified by setting explicit rules [1]; therefore, artificial intelligence (AI) and spe‑ cifically machine learning techniques can replace the need for hardcoding the rules. Moreover, rule inference is itself a specific and constrained instantiation of AI [2].  \nThe field of artificial intelligence is a thriving field that has numerous practical ap‑ plications. The ability of AI systems to learn from data alleviated the difficulties encountered by systems that rely on hard-coded knowledge [3]. Machine learning algorithms offer solutions in several areas that need prediction, classification, clus‑ tering, and anomaly detection. Therefore, manual or rule-based data verification for anomaly detection can be replaced by an automated machine learning process.  \nAs BIM models are growing in size and complexity, a human‑performed quality check, even on a specific object class, might be impossible within the strict deadlines of projects. Hence, this study proposes a method for BIM model quality checking for openings where mechanical, electrical, and plumbing (MEP) services pass through them in the walls, floors, and ceilings of buildings. A machine learning model was applied to identify errors and omissions in the data embedded in opening elements.  \nThe structure of this study is organized as follows: first, a background on BIM and machine learning studies is provided in Section 2. Next, in Section 3, the research me","cbCainN52lEypxWJ","https://ap.wps.com/l/cbCainN52lEypxWJ","pdf",1025921,1,10,"English","en",105,"# Introduction\n# Background\n# Research Method\n# Experiments and Results\n# Conclusions and Future Work","[{\"question\":\"Why is quality checking of BIM models important during construction?\",\"answer\":\"Construction drawings are produced from BIM models, so missing or incorrect embedded information leads to errors in later phases. These errors create significant cost and time burdens.\"},{\"question\":\"How does the study use machine learning to improve BIM model quality?\",\"answer\":\"A machine learning model is applied to a BIM model to detect anomalies in openings of precast structural walls. It identifies openings with wrong embedded information that would negatively impact delivery.\"},{\"question\":\"What is the main contribution and expected impact of this approach?\",\"answer\":\"The contribution is reducing errors in construction drawings, thereby securing projects against time and cost impacts caused by those errors. The study also discusses applicability of the ML model to other projects.\"}]","Quality check of BIM models using machine learning | PDF",1785727794,25,{"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},"quality-check-of-bim-models-using-machine-learning","",{"@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/quality-check-of-bim-models-using-machine-learning/120029/",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 quality checking of BIM models important during construction?","Question",{"text":75,"@type":76},"Construction drawings are produced from BIM models, so missing or incorrect embedded information leads to errors in later phases. These errors create significant cost and time burdens.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study use machine learning to improve BIM model quality?",{"text":80,"@type":76},"A machine learning model is applied to a BIM model to detect anomalies in openings of precast structural walls. It identifies openings with wrong embedded information that would negatively impact delivery.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the main contribution and expected impact of this approach?",{"text":84,"@type":76},"The contribution is reducing errors in construction drawings, thereby securing projects against time and cost impacts caused by those errors. 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