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...
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| Tipo de documento: | dissertação |
| Data de publicação: | 2023 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositório: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglês |
| OAI Identifier: | oai:upcommons.upc.edu:2117/385148 |
| Acesso em linha: | https://hdl.handle.net/2117/385148 |
| Access Level: | Acceso aberto |
| Palavra-chave: | 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 |
| Resumo: | 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 |
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