“…The web-based dashboard environment was not used much in this technology demonstration since the problem formulation and design development was a single person activity involving just the author, as opposed to a multidisciplinary environment in real-life project settings. Centralized relational database – Setting up a centralized cloud-based OORDBMS database that stores data from multiple modeling tools (retrieved as IFC and translated to SQL to be stored in the OORDBMS), metamodels and tradespace analysis tools that works in tandem with the PIDO tools was helpful to automate the data exchange between multiple tools. However, relying on the promising potential of such state-of-the-art data exchange infrastructures indicated in various literature and benchmark testing (Nour, 2009; Lee et al ., 2014; Li et al , 2016; Solihin et al , 2017; Cho et al , 2018; Bock, 2019; Wyszomirski and Gotlib, 2020), the scope of the paper was limited to just demonstrate the setup and use of such infrastructure as one of the components within the overall MDO framework proposed and not on the rigorous and objective evaluation of the infrastructure.Fig. 41.Parallel coordinates plot showing convergence of the tradespace toward the optimal options as the design phase progresses from conceptual to detailed-extended design.Fig.…”
Section: Design Of An Office Buildingmentioning
confidence: 99%
“…Centralized relational database – Setting up a centralized cloud-based OORDBMS database that stores data from multiple modeling tools (retrieved as IFC and translated to SQL to be stored in the OORDBMS), metamodels and tradespace analysis tools that works in tandem with the PIDO tools was helpful to automate the data exchange between multiple tools. However, relying on the promising potential of such state-of-the-art data exchange infrastructures indicated in various literature and benchmark testing (Nour, 2009; Lee et al ., 2014; Li et al , 2016; Solihin et al , 2017; Cho et al , 2018; Bock, 2019; Wyszomirski and Gotlib, 2020), the scope of the paper was limited to just demonstrate the setup and use of such infrastructure as one of the components within the overall MDO framework proposed and not on the rigorous and objective evaluation of the infrastructure.…”
Section: Design Of An Office Buildingmentioning
confidence: 99%
“…This facilitates storing geometrical information of building components along with their properties in an IFC schema in various fields of appropriate tables in the SQL database. Furthermore, benchmark testing of query performance of IFC models using OORDBMS have indicated the benefits of OORDBMS in terms of easy retrieval of references (design variables) from multiple sources (Lee et al, 2014;Li et al, 2016;Cho et al, 2018). The database was hosted in Microsoft® Azure™ (Cloud server).…”
Section: Centralized Relational Database For Interoperability Between...mentioning
Modern day building design projects require multidisciplinary expertise from architects and engineers across various phases of the design (conceptual, preliminary, and detailed) and construction processes. The Architecture Engineering and Construction (AEC) community has recently shifted gears toward leveraging design optimization techniques to make well-informed decisions in the design of buildings. However, most of the building design optimization efforts are either multidisciplinary optimization confined to just a specific design phase (conceptual/preliminary/detailed) or single disciplinary optimization (structural/thermal/daylighting/energy) spanning across multiple phases. Complexity in changing the optimization setup as the design progresses through subsequent phases, interoperability issues between modeling and physics-based analysis tools used at later stages, and the lack of an appropriate level of design detail to get meaningful results from these sophisticated analysis tools are few challenges that limit multi-phase multidisciplinary design optimization (MDO) in the AEC field. This paper proposes a computational building design platform leveraging concurrent engineering techniques such as interactive problem structuring, simulation-based optimization using meta models for energy and daylighting (machine learning based) and tradespace visualization. The proposed multi-phase concurrent MDO framework is demonstrated by using it to design and optimize a sample office building for energy and daylighting objectives across multiple phases. Furthermore, limitations of the proposed framework and future avenues of research are listed.
“…The web-based dashboard environment was not used much in this technology demonstration since the problem formulation and design development was a single person activity involving just the author, as opposed to a multidisciplinary environment in real-life project settings. Centralized relational database – Setting up a centralized cloud-based OORDBMS database that stores data from multiple modeling tools (retrieved as IFC and translated to SQL to be stored in the OORDBMS), metamodels and tradespace analysis tools that works in tandem with the PIDO tools was helpful to automate the data exchange between multiple tools. However, relying on the promising potential of such state-of-the-art data exchange infrastructures indicated in various literature and benchmark testing (Nour, 2009; Lee et al ., 2014; Li et al , 2016; Solihin et al , 2017; Cho et al , 2018; Bock, 2019; Wyszomirski and Gotlib, 2020), the scope of the paper was limited to just demonstrate the setup and use of such infrastructure as one of the components within the overall MDO framework proposed and not on the rigorous and objective evaluation of the infrastructure.Fig. 41.Parallel coordinates plot showing convergence of the tradespace toward the optimal options as the design phase progresses from conceptual to detailed-extended design.Fig.…”
Section: Design Of An Office Buildingmentioning
confidence: 99%
“…Centralized relational database – Setting up a centralized cloud-based OORDBMS database that stores data from multiple modeling tools (retrieved as IFC and translated to SQL to be stored in the OORDBMS), metamodels and tradespace analysis tools that works in tandem with the PIDO tools was helpful to automate the data exchange between multiple tools. However, relying on the promising potential of such state-of-the-art data exchange infrastructures indicated in various literature and benchmark testing (Nour, 2009; Lee et al ., 2014; Li et al , 2016; Solihin et al , 2017; Cho et al , 2018; Bock, 2019; Wyszomirski and Gotlib, 2020), the scope of the paper was limited to just demonstrate the setup and use of such infrastructure as one of the components within the overall MDO framework proposed and not on the rigorous and objective evaluation of the infrastructure.…”
Section: Design Of An Office Buildingmentioning
confidence: 99%
“…This facilitates storing geometrical information of building components along with their properties in an IFC schema in various fields of appropriate tables in the SQL database. Furthermore, benchmark testing of query performance of IFC models using OORDBMS have indicated the benefits of OORDBMS in terms of easy retrieval of references (design variables) from multiple sources (Lee et al, 2014;Li et al, 2016;Cho et al, 2018). The database was hosted in Microsoft® Azure™ (Cloud server).…”
Section: Centralized Relational Database For Interoperability Between...mentioning
Modern day building design projects require multidisciplinary expertise from architects and engineers across various phases of the design (conceptual, preliminary, and detailed) and construction processes. The Architecture Engineering and Construction (AEC) community has recently shifted gears toward leveraging design optimization techniques to make well-informed decisions in the design of buildings. However, most of the building design optimization efforts are either multidisciplinary optimization confined to just a specific design phase (conceptual/preliminary/detailed) or single disciplinary optimization (structural/thermal/daylighting/energy) spanning across multiple phases. Complexity in changing the optimization setup as the design progresses through subsequent phases, interoperability issues between modeling and physics-based analysis tools used at later stages, and the lack of an appropriate level of design detail to get meaningful results from these sophisticated analysis tools are few challenges that limit multi-phase multidisciplinary design optimization (MDO) in the AEC field. This paper proposes a computational building design platform leveraging concurrent engineering techniques such as interactive problem structuring, simulation-based optimization using meta models for energy and daylighting (machine learning based) and tradespace visualization. The proposed multi-phase concurrent MDO framework is demonstrated by using it to design and optimize a sample office building for energy and daylighting objectives across multiple phases. Furthermore, limitations of the proposed framework and future avenues of research are listed.
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