BIM based Machine Learning framework for healthcare facilities
Traditional 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 col...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/385148 |
| Acceso en línea: | https://hdl.handle.net/2117/385148 |
| Access Level: | acceso abierto |
| Palabra clave: | Health facilities -- Design and construction Building information modeling Particle Swarm Optimization PSO Hospital layout problem HLP BIM Dynamo Building information modelling Equipaments sanitaris -- Disseny i construcció Modelatge d'informació de construcció Àrees temàtiques de la UPC::Edificació |
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BIM based Machine Learning framework for healthcare facilities |
| title |
BIM based Machine Learning framework for healthcare facilities |
| spellingShingle |
BIM based Machine Learning framework for healthcare facilities Hogmo, Bjorn Health facilities -- Design and construction Building information modeling Particle Swarm Optimization PSO Hospital layout problem HLP BIM Dynamo Building information modelling Equipaments sanitaris -- Disseny i construcció Modelatge d'informació de construcció Àrees temàtiques de la UPC::Edificació |
| title_short |
BIM based Machine Learning framework for healthcare facilities |
| title_full |
BIM based Machine Learning framework for healthcare facilities |
| title_fullStr |
BIM based Machine Learning framework for healthcare facilities |
| title_full_unstemmed |
BIM based Machine Learning framework for healthcare facilities |
| title_sort |
BIM based Machine Learning framework for healthcare facilities |
| dc.creator.none.fl_str_mv |
Hogmo, Bjorn |
| author |
Hogmo, Bjorn |
| author_facet |
Hogmo, Bjorn |
| author_role |
author |
| dc.contributor.none.fl_str_mv |
Forcada Matheu, Núria |
| dc.subject.none.fl_str_mv |
Health facilities -- Design and construction Building information modeling Particle Swarm Optimization PSO Hospital layout problem HLP BIM Dynamo Building information modelling Equipaments sanitaris -- Disseny i construcció Modelatge d'informació de construcció Àrees temàtiques de la UPC::Edificació |
| topic |
Health facilities -- Design and construction Building information modeling Particle Swarm Optimization PSO Hospital layout problem HLP BIM Dynamo Building information modelling Equipaments sanitaris -- Disseny i construcció Modelatge d'informació de construcció Àrees temàtiques de la UPC::Edificació |
| description |
Traditional 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 specifications and 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. Contrary, 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. This 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 |
| publishDate |
2023 |
| dc.date.none.fl_str_mv |
2023 2023-02-01 2023 2023-03-17 |
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master thesis http://purl.org/coar/resource_type/c_bdcc NA http://purl.org/coar/version/c_be7fb7dd8ff6fe43 |
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info:eu-repo/semantics/masterThesis |
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masterThesis |
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https://hdl.handle.net/2117/385148 |
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https://hdl.handle.net/2117/385148 |
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Inglés eng |
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Inglés |
| language |
eng |
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open access http://purl.org/coar/access_right/c_abf2 |
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info:eu-repo/semantics/openAccess |
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open access http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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application/pdf application/pdf application/pdf |
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Universitat Politècnica de Catalunya |
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Universitat Politècnica de Catalunya |
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reponame:UPCommons. Portal del coneixement obert de la UPC instname:Universitat Politècnica de Catalunya (UPC) |
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Universitat Politècnica de Catalunya (UPC) |
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UPCommons. Portal del coneixement obert de la UPC |
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BIM based Machine Learning framework for healthcare facilitiesHogmo, BjornHealth facilities -- Design and constructionBuilding information modelingParticle Swarm OptimizationPSOHospital layout problemHLPBIMDynamoBuilding information modellingEquipaments sanitaris -- Disseny i construccióModelatge d'informació de construccióÀrees temàtiques de la UPC::EdificacióTraditional 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 specifications and 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. Contrary, 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. This 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 phaseEl 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 o intervalos. Las etapas preliminares de un proyecto implican la recopilación de datos y la especificación 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 de los representantes, mientras que la interpretación de las especificaciones y la creación del diseño dependen de la experiencia de los diseñadores. Tal creación manual de recopilación de 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. Por 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 diseño preliminar basado en datos clínicos reales y sus registros de salud electrónicos (EHR), la optimización del diseño basado en EHR y el movimiento de los pacientes, que nuevamente podría 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 automática de BIM por otro lado, tiene un beneficio potencial para generar ahorros, reducir la duración de la fase preliminar y reducir las horas de trabajo. Esta tesis sugiere una metodología para combinar algoritmos ML para la optimización en el diseño de problemas de diseño de hospitales (HLP) con la creación automática de modelos BIM 3D. Tal metodología se muestra posible, y el informe concluye que tal enfoque es factible. Los pasos de la metodología utilizan el algoritmo de aprendizaje automático para crear soluciones de diseño optimizadas con coordenadas en el plano bidimensional (2D), donde una programación visual Automa crea un modelo BIM 3D para usar en la fase preliminarUniversitat Politècnica de CatalunyaForcada Matheu, Núria20232023-02-0120232023-03-17master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfapplication/pdfapplication/pdfhttps://hdl.handle.net/2117/385148reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3851482026-05-27T15:37:01Z |
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15,300724 |