[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127613-en":3,"doc-seo-127613-105":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},127613,549768064778,"Finn","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","BIM Based Machine Learning Framework for Healthcare Facilities - Master Thesis","Traditional construction design for healthcare facilities relies on manual data collection, stakeholder requirements, and designer experience across design, collaboration, construction, and operation phases, which limits the number of alternatives assessed and increases the risk of human error. This thesis proposes combining machine learning with building information modelling to optimize hospital layouts using real clinic data and electronic health records. The approach links 2D optimized coordinates to automated 3D BIM model creation, enabling reduced preliminary effort, shorter phases, and labor savings.","BIM Based Machine Learning Framework for Healthcare Facilities  \nDocument:  \nReport  \nAutor:  \nBjørn Harald Høgmo  \nDirector /Co-director:  \nNuria Forcada Matheu/ Hamidreza Alavi  \nDegree:  \nMaster in Technology and Engineering Management  \nExamination session:  \nAutumn, 2022-2023  \nMA TE NAL THESIS  \nAbstract  \nEnglish:  \nTraditional design approach for construction projects spans over several steps, from design phase, collaboration phase, construct phase and operation and management phase. Each phase consists of its own methodology and intermediate steps or intervals. Preliminary stages of a project involve data collection and specification of the requirements, among several points, and within a healthcare facility the specifications are given by stakeholders and the users of the facility. To create good projects, the quality of the specifications relies heavily on the experience of the representants, while the interpretation of the specificationsand creation of design rely on the experience of the designers. Such a manual creation of data collection, specifications and design are prone to human errors , only assessing a few alternatives and what worked earlier design approach.  \nContrary, machine learning (ML) and Building information modelling (BIM) software could assess thousands of alternatives and modified according to different optimization parameters. Therefore, the creation of a preliminary design model based on actual clinic data and their electronic health records (EHR) , optimization of layout based on EHR and patients’movement , which again could be connected to a automate creation of BIM model. Each of the separate areas, creating a machine learning on one side and atomate create BIM on the other hand has a potential benefit to generate savings, reduce length of preliminary phase and reduce labor hours.  \nThis thesis suggests a methodology for combining ML algorithms for optimization in hospital layout problems (HLP) design with the automate creation of 3D BIM model. Such a methodology is shown possible, and the report concludes such an approach is feasible. The methodology steps use of machine learning algorithm to create optimized layout solutions with coordinates in the 2-dimensional (2D) plane, where a visual programming Automa create a 3D BIM model for use in the preliminary phase.  \nSpanish:  \nEl enfoque de diseño tradicional para proyectos de construcción abarca varios pasos, desde la fase de diseño, la fase de colaboración, la fase de construcción y la fase de operación y gestión. Cada fase consta de su propia metodología y pasos intermedios ointervalos. Las etapas preliminares de un proyecto implican la recopilación de datos y laespecificación de los requisitos, entre varios puntos, y dentro de un centro de salud, las especificaciones las dan las partes interesadas y los usuarios del centro. Para crear buenos proyectos, la calidad de las especificaciones depende en gran medida de la experiencia delos representantes, mientras que la interpretación de las especificaciones y la creación del diseño dependen de la experiencia de los diseñadores. Tal creación manual de recopilaciónde datos, especificaciones y diseño es propensa a errores humanos, ya que solo evalúa algunas alternativas y lo que funcionó con el enfoque de diseño anterior.  \nPor el contrario, el software de aprendizaje automático (ML) y modelado de información de construcción (BIM) podría evaluar miles de alternativas y modificarse de acuerdo con diferentes parámetros de optimización. Por lo tanto, la creación de un modelo de diseñopreliminar basado en datos clínicos reales y sus registros de salud electrónicos (EHR), laoptimización del diseño basado en EHR y el movimiento de los pacientes, que nuevamentepodría conectarse a una creación automática del modelo BIM. Cada una de las áreas separadas, la creación de un aprendizaje automático por un lado y la creación automáticade BIM por otro lado, tiene un beneficio potencial para generar ahorros, r","cbCaioWughWSIJFa","https://ap.wps.com/l/cbCaioWughWSIJFa","pdf",1194424,1,49,"English","en",105,"# Introduction\n## Aim\n## Scope\n# Abstract\n# Table of Contents\n# Lists","[{\"question\":\"What problem does the thesis address in traditional healthcare facility design?\",\"answer\":\"Traditional design depends heavily on manual data collection and interpretation of stakeholder requirements, which is prone to human errors and typically evaluates only a limited set of layout alternatives.\"},{\"question\":\"How do machine learning and BIM work together in the proposed method?\",\"answer\":\"Machine learning creates optimized hospital layout solutions using coordinates in a 2D plane, while a visual programming tool automates the generation of a 3D BIM model for the preliminary phase.\"},{\"question\":\"What data sources are used to optimize the hospital layout?\",\"answer\":\"The methodology uses real clinic data and electronic health records (EHR), including patient movement information, to drive layout optimization.\"}]","BIM Based Machine Learning Framework for Healthcare Facilities - 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