[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119860-en":3,"doc-seo-119860-105":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":20,"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},119860,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning Methods in BIM-Based Applications - A Review","This survey reviews machine learning (ML) methods applied to building and construction industry use cases leveraging building information modeling (BIM). It introduces BIM as a database-like representation of civil engineering data across the full lifecycle, from design through construction to facility management, and highlights how ML can infer implicit knowledge rather than rely solely on scenario-specific querying. The review describes representative ML method categories and application targets, including classification, anomaly detection, and time series analysis, to support prediction, error assessment, and maintenance or renovation planning. The work aims to guide and extend future research by identifying key reference themes and hotspots.","[Vietnam J. Comp. Sci. Downloaded from www.worldscientific.com](Vietnam J. Comp. Sci. Downloaded from www.worldscientific.com)  \nby JAGIELLONIAN UNIVERSITY on 11 except Open Access articles/ 14/23. Re-use and distribution is strictly not permitted, for .  \n OPEN ACCESS  \nVietnam Journal of Computer Science (2023) 1–22  \n\\# The Author(s)  \nDOI: 10.1142/S2196888823300028  \nMachine Learning Methods in BIM-Based Applications | A Review  \nGrażyna Ślusarczyk * and Barbara Strug †  \nInstitute of Applied Computer Science, Jagiellonian University Lojasiewicza 11, 30-059 Krakw, Poland  \n*[grazyna.slusarczyk@uj.edu.pl](grazyna.slusarczyk@uj.edu.pl)  \n†[barbara.strug@uj.edu.pl](barbara.strug@uj.edu.pl)  \nReceived 26 July 2023  \nRevised 30 September 2023  \nAccepted 1 October 2023  \nPublished 4 November 2023  \nThis paper presents a survey of machine learning (ML) methods used in applications dedicated to the building and construction industry. A building information modeling (BIM) model, being a database system for civil engineering data, is presented. A representative selection of methods and applications is described. The aim of this paper is to facilitate the continuation of research e®orts and to encourage bigger participation of database system researchers in the ¯eld of civil engineering.  \nKeywords: BIM data; machine learning; civil engineering modeling.  \n1. Introduction  \nBuilding Information Modeling (BIM) is nowadays widely used in architecture, engineering and construction industry (AEC) . The building and construction industry employs currently about 7% of the world's working-age population and is one of the world economy's largest sectors. It is estimated that about $10 trillion is spent on construction-related goods and services every year. In the last decade, the acceptance and actual use of BIM has increased signi¯cantly within the building community. It has largely contributed to the process of eliminating faults in designs. BIM allows architects and engineers to create 3D simulations of the desired structures which contain signi¯cantly more information on the actual structures than drawings produced using traditional Computer Aided Drafting (CAD) systems. As a result, BIM has become more and more present in the construction industry.  \n† Corresponding author.  \nThis is an Open Access article published by World Scienti¯c Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC BY) License which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n[Vietnam J. Comp. Sci. Downloaded from www.worldscientific.com](Vietnam J. Comp. Sci. Downloaded from www.worldscientific.com)  \nby JAGIELLONIAN UNIVERSITY on 11 except Open Access articles/ 14/23. Re-use and distribution is strictly not permitted, for .  \n􀀁  \n2 G. Slusarczyk & B. Strug  \nBIM technology enables representation of syntactic and semantic building information with respect to the entire life cycle of designed objects, from the design phase, through construction to the facility management (FM) phase. BIM includes information about the elements and spaces within buildings, their constituting elements, their interrelations, properties and performance. The project created in BIM technology can be treated as a database that allows to record both technical information about building elements, and its purpose and history. However, although BIM is information rich, not all knowledge is explicitly stated. It seems that ML approaches suit well to deduce implicit knowledge from BIM models. Contrary to querying approaches used to extract knowledge from building models,1–3 which are tailored to speci¯c scenarios with prede¯ned outcomes, ML methods are able to detect patterns and make predictions.  \nUsing ML and arti¯cial intelligence (AI) in AEC industry is a promising research direction. It has to be noted that while both ML and AI are rapidly developing across many other industries, the ","cbCais1vq3hefzPn","https://ap.wps.com/l/cbCais1vq3hefzPn","pdf",443476,1,22,"English","en",105,"# Introduction\n## BIM in AEC and Knowledge Extraction\n## How ML Fits BIM Models\n# Objectives and Scope\n## Relationship to Prior Conference Work\n## Review Purpose and Research Hotspots","[{\"question\":\"What problem does the paper address in BIM-based research?\",\"answer\":\"It addresses how to extract implicit knowledge from BIM models, where not all knowledge is explicitly stated, by using machine learning methods.\"},{\"question\":\"How does the paper position machine learning compared with querying approaches?\",\"answer\":\"It explains that querying methods are tailored to predefined scenarios, while ML detects patterns and makes predictions from examples.\"},{\"question\":\"Which ML application types are discussed for BIM?\",\"answer\":\"The paper discusses classification, anomaly detection, and time series analysis, mapped to tasks such as delay likelihood estimation, modeling error discovery, and maintenance or renovation planning.\"}]","Machine Learning Methods in BIM-Based Applications - 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