Pub Date : 2022-10-20DOI: 10.47330/dcio.2022.relw8964
Ruben Borsje
{"title":"Bacton Digital Beach Twin: a Digital Twin for natural assets","authors":"Ruben Borsje","doi":"10.47330/dcio.2022.relw8964","DOIUrl":"https://doi.org/10.47330/dcio.2022.relw8964","url":null,"abstract":"","PeriodicalId":129906,"journal":{"name":"Design Computation Input/Output 2022","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130658182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-20DOI: 10.47330/dcio.2022.nwtj1254
Michal Gryko, David Andres Leon
The conception of a new design or building is arguably the most creative stage of a project and one that can be most influenced by inspiration from the world around us. AI algorithms are being increasing implemented to generate inspirational and creative images, however the extent in which this can be further used to create workable designs is always in question. This paper explores how these algorithms can go beyond creating provoking images to be implemented in a wholesome design workflow that allows non-technical users to configure and output rationalised organic forms rapidly for concept development.
{"title":"Prototyping the Organic: AI in design work-flows for complex forms inspired by nature","authors":"Michal Gryko, David Andres Leon","doi":"10.47330/dcio.2022.nwtj1254","DOIUrl":"https://doi.org/10.47330/dcio.2022.nwtj1254","url":null,"abstract":"The conception of a new design or building is arguably the most creative stage of a project and one that can be most influenced by inspiration from the world around us. AI algorithms are being increasing implemented to generate inspirational and creative images, however the extent in which this can be further used to create workable designs is always in question. This paper explores how these algorithms can go beyond creating provoking images to be implemented in a wholesome design workflow that allows non-technical users to configure and output rationalised organic forms rapidly for concept development.","PeriodicalId":129906,"journal":{"name":"Design Computation Input/Output 2022","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130665250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-20DOI: 10.47330/dcio.2022.pepg4740
Octavian Gheorghiu
The workshop will explore the workflow of preparing and transferring data from CAD packages to the Unity Game engine. A 3d model of a house will be prepared for the mix reality experiences, going through the process of optimising the model geometry, adding textures and optimising the output required for mobile devices. In the game engine, we will prepare a diorama model that can be shared either as an augmented reality experience or as a virtual reality experience. We will be exploring how to add time base effects and create a user interface for the mix reality experiences.
{"title":"Mix Reality, Data and Experiences","authors":"Octavian Gheorghiu","doi":"10.47330/dcio.2022.pepg4740","DOIUrl":"https://doi.org/10.47330/dcio.2022.pepg4740","url":null,"abstract":"The workshop will explore the workflow of preparing and transferring data from CAD packages to the Unity Game engine. A 3d model of a house will be prepared for the mix reality experiences, going through the process of optimising the model geometry, adding textures and optimising the output required for mobile devices. In the game engine, we will prepare a diorama model that can be shared either as an augmented reality experience or as a virtual reality experience. We will be exploring how to add time base effects and create a user interface for the mix reality experiences.","PeriodicalId":129906,"journal":{"name":"Design Computation Input/Output 2022","volume":"67 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113988126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-20DOI: 10.47330/dcio.2022.flxi8620
Guanzhou Ji, Ting-Ju Ou, A. Sawyer
This paper introduces a novel computational method for mapping indoor luminance values to the facade of an open workplace to improve its daylight performance. 180-degree fisheye renderings from different locations, view positions, and times of the year are created. These renderings are then transformed from two-dimensional (2D) images into three-dimensional (3D) hemispheres. High luminance values are filtered and projected from the hemisphere to the facade design. This framework will highlight the areas of the facade that allow too much light penetration into the interior environment. This study introduces a flexible framework that allows for an occupant-centric lighting analysis to compute multiple design parameters and synthesize results based on luminance values mapped on the facade design for localized performance optimization to improve facade performance.
{"title":"View-Based Luminance Mapping in Open Workplace","authors":"Guanzhou Ji, Ting-Ju Ou, A. Sawyer","doi":"10.47330/dcio.2022.flxi8620","DOIUrl":"https://doi.org/10.47330/dcio.2022.flxi8620","url":null,"abstract":"This paper introduces a novel computational method for mapping indoor luminance values to the facade of an open workplace to improve its daylight performance. 180-degree fisheye renderings from different locations, view positions, and times of the year are created. These renderings are then transformed from two-dimensional (2D) images into three-dimensional (3D) hemispheres. High luminance values are filtered and projected from the hemisphere to the facade design. This framework will highlight the areas of the facade that allow too much light penetration into the interior environment. This study introduces a flexible framework that allows for an occupant-centric lighting analysis to compute multiple design parameters and synthesize results based on luminance values mapped on the facade design for localized performance optimization to improve facade performance.","PeriodicalId":129906,"journal":{"name":"Design Computation Input/Output 2022","volume":"107 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120849362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-20DOI: 10.47330/dcio.2022.otju4338
Kean Walmsley
{"title":"Research into Digital Twins for AEC","authors":"Kean Walmsley","doi":"10.47330/dcio.2022.otju4338","DOIUrl":"https://doi.org/10.47330/dcio.2022.otju4338","url":null,"abstract":"","PeriodicalId":129906,"journal":{"name":"Design Computation Input/Output 2022","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128960827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-20DOI: 10.47330/dcio.2022.axbl8798
J. Tompkin
Neural fields are a new (and old!) approach to solving problems over spacetime via first-order optimization of a neural network. Over the past three years, combining neural fields with classic computer graphics approaches have allowed us to make significant advances in solving computer vision problems like scene reconstruction. I will present recent work that can reconstruct indoor scenes for photorealistic interactive exploration using new scalable hybrid neural field representations. This has applications where any real-world place needs to be digitized, especially for visualization purposes.
{"title":"Neural Fields for Scalable Scene Reconstruction","authors":"J. Tompkin","doi":"10.47330/dcio.2022.axbl8798","DOIUrl":"https://doi.org/10.47330/dcio.2022.axbl8798","url":null,"abstract":"Neural fields are a new (and old!) approach to solving problems over spacetime via first-order optimization of a neural network. Over the past three years, combining neural fields with classic computer graphics approaches have allowed us to make significant advances in solving computer vision problems like scene reconstruction. I will present recent work that can reconstruct indoor scenes for photorealistic interactive exploration using new scalable hybrid neural field representations. This has applications where any real-world place needs to be digitized, especially for visualization purposes.","PeriodicalId":129906,"journal":{"name":"Design Computation Input/Output 2022","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124857867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-20DOI: 10.47330/dcio.2022.iqbe2166
S. Yousif, Daniel Bolojan
"The incorporation of deep learning models into architectural design poses challenges, despite their potential to inform new design processes. One of these issues is the oversimplification of the design problem when employing a discrete, single AI model to tackle a multifaceted, complex design activity. Importantly, the investigation of DL-driven systems requires the identification of components (parts) and relationships between these constituents of this new design workflow. The need to investigate a functional AI-driven design workflow structure with design intentions encoded and incorporated into a human-supervised process is an additional important issue identified by this research. How can specific levels of agency be identified and made explicit in the context of interacting with learning systems? This study investigates a novel human-AI collaborative workflow that combines machine and designer creativity within a comprehensive framework. The focus was on developing a design system, a ""prototype"" with interconnected AI and agent-based models (ABM) to address multiple architectural systems at various design levels (design tasks, design phases) while enacting the designer's varying degrees of agency. Curation of datasets, network types, and connection strategies are the design intentions when working with AI models. In developing a new design workflow, we employ systems theory and the need to deconstruct the design process into its component parts. Design is considered an ""exploration activity"" because it involves the modification and evolution of both the problem goals (design requirements) and the methods and means used to achieve the goals. The investigation centered on prototyping feasible workflows with the following objectives: (1) establishing successful interconnectivities between multiple DL models within the process to manage architectural systems and layers; (2) emphasizing design agency and embedding intentions within each design task within the process. The proposed prototype was applied to three case studies to demonstrate the framework's potential, evaluate its functionality, and assess the outcomes. The experiment described here followed the format of a three-month project (Figure 1). The framework included the use of DL models for (i) design exploration, (ii) generation, revision, and evaluation, and (iii) project development. To examine different types of DL model connections at a global level, the following strategies were identified and implemented: sequential/(unidirectional); parallel and linear; and branching (design problem is broken down into subtasks defined by separate sets of AI models), branching off, or/and merging into a design solution. Designers are the choreographers of how discrete AI models interact with other discrete AI models and human agents. Using this concept as a guide, we can determine the levels of autonomy that the proposed framework driven by interconnected AI can provide. (a) dataset curatio
{"title":"Interconnectivity of Deep Learning Models in AI-Driven Design Systems","authors":"S. Yousif, Daniel Bolojan","doi":"10.47330/dcio.2022.iqbe2166","DOIUrl":"https://doi.org/10.47330/dcio.2022.iqbe2166","url":null,"abstract":"\"The incorporation of deep learning models into architectural design poses challenges, despite their potential to inform new design processes. One of these issues is the oversimplification of the design problem when employing a discrete, single AI model to tackle a multifaceted, complex design activity. Importantly, the investigation of DL-driven systems requires the identification of components (parts) and relationships between these constituents of this new design workflow. The need to investigate a functional AI-driven design workflow structure with design intentions encoded and incorporated into a human-supervised process is an additional important issue identified by this research. How can specific levels of agency be identified and made explicit in the context of interacting with learning systems? This study investigates a novel human-AI collaborative workflow that combines machine and designer creativity within a comprehensive framework. The focus was on developing a design system, a \"\"prototype\"\" with interconnected AI and agent-based models (ABM) to address multiple architectural systems at various design levels (design tasks, design phases) while enacting the designer's varying degrees of agency. Curation of datasets, network types, and connection strategies are the design intentions when working with AI models. In developing a new design workflow, we employ systems theory and the need to deconstruct the design process into its component parts. Design is considered an \"\"exploration activity\"\" because it involves the modification and evolution of both the problem goals (design requirements) and the methods and means used to achieve the goals. The investigation centered on prototyping feasible workflows with the following objectives: (1) establishing successful interconnectivities between multiple DL models within the process to manage architectural systems and layers; (2) emphasizing design agency and embedding intentions within each design task within the process. The proposed prototype was applied to three case studies to demonstrate the framework's potential, evaluate its functionality, and assess the outcomes. The experiment described here followed the format of a three-month project (Figure 1). The framework included the use of DL models for (i) design exploration, (ii) generation, revision, and evaluation, and (iii) project development. To examine different types of DL model connections at a global level, the following strategies were identified and implemented: sequential/(unidirectional); parallel and linear; and branching (design problem is broken down into subtasks defined by separate sets of AI models), branching off, or/and merging into a design solution. Designers are the choreographers of how discrete AI models interact with other discrete AI models and human agents. Using this concept as a guide, we can determine the levels of autonomy that the proposed framework driven by interconnected AI can provide. (a) dataset curatio","PeriodicalId":129906,"journal":{"name":"Design Computation Input/Output 2022","volume":"275 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115944261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-20DOI: 10.47330/dcio.2022.fynq9140
P. Russell
The presentation discusses a topological approach to the question of location information in increasingly wired buildings and building components. One of the consequences of this approach is to use the thresholds within buildings as not only the position for mathematical descriptions (topologies), but also the physical infrastructure of sensors, actuators and even processors. The presentation describes the processor-cycle and maintenance-cycle advantages of this strategy and presents the challenges in creating a topologically configured intelligent system of systems.
{"title":"The Internet of Doors - topologies and doorframe computing","authors":"P. Russell","doi":"10.47330/dcio.2022.fynq9140","DOIUrl":"https://doi.org/10.47330/dcio.2022.fynq9140","url":null,"abstract":"The presentation discusses a topological approach to the question of location information in increasingly wired buildings and building components. One of the consequences of this approach is to use the thresholds within buildings as not only the position for mathematical descriptions (topologies), but also the physical infrastructure of sensors, actuators and even processors. The presentation describes the processor-cycle and maintenance-cycle advantages of this strategy and presents the challenges in creating a topologically configured intelligent system of systems.","PeriodicalId":129906,"journal":{"name":"Design Computation Input/Output 2022","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114477700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-20DOI: 10.47330/dcio.2022.eqad1156
Hangchuan Wei, Yota Adilenido, R. Beckett
In recent years, machine learning (ML) has received significant attention in the field of architectural design. This paper proposes a methodology for integrating ML with computational design to generate building massing based on environment, in this way, gives an outlook on the application of ML in architecture. In the early stages of building design, a great deal of effort is often spent on specifying and designing building massing. In this process, the assessment of the building wind performance plays an important role. Compared to professional computational fluid dynamics (CFD) software, plug-ins based on rhino and grasshopper, such like Butterfly and Eddy3D, can well integrated into computational design process. But even then, these plug-ins are still limited because a lot of computing power and time are required to run the program. This article provides an overview of a generative framework embedded with a ML approach to apply CFD in building design, finally results on a building massing with a balanced wind environment at the early stage of architectural design. This framework innovates the existing CFD simulation in following aspects: 1) ML-based simulation is timesaving, 2) this advantage allows the use of exhaustive enumeration to obtain the optimal solution, 3) this framework provides a good interface with computational design process with images as a medium, 4) therefore it is more flexible and operational. This framework aims to provide an approach to achieve faster and better massing design. To reach this objective, there are three main steps: 1) firstly, a generative adversarial network (GAN) model is trained to get wind simulation results from the input site, 2) then, the possible boundaries of massing in different height are generated for exhaustive enumeration, 3) afterwards, run again the GAN wind simulation for the possible boundaries, 4) and finally an assessment method is put forward to obtain the ideal result for the site.
{"title":"Environmental-driven Massing Based on Machine learning","authors":"Hangchuan Wei, Yota Adilenido, R. Beckett","doi":"10.47330/dcio.2022.eqad1156","DOIUrl":"https://doi.org/10.47330/dcio.2022.eqad1156","url":null,"abstract":"In recent years, machine learning (ML) has received significant attention in the field of architectural design. This paper proposes a methodology for integrating ML with computational design to generate building massing based on environment, in this way, gives an outlook on the application of ML in architecture. In the early stages of building design, a great deal of effort is often spent on specifying and designing building massing. In this process, the assessment of the building wind performance plays an important role. Compared to professional computational fluid dynamics (CFD) software, plug-ins based on rhino and grasshopper, such like Butterfly and Eddy3D, can well integrated into computational design process. But even then, these plug-ins are still limited because a lot of computing power and time are required to run the program. This article provides an overview of a generative framework embedded with a ML approach to apply CFD in building design, finally results on a building massing with a balanced wind environment at the early stage of architectural design. This framework innovates the existing CFD simulation in following aspects: 1) ML-based simulation is timesaving, 2) this advantage allows the use of exhaustive enumeration to obtain the optimal solution, 3) this framework provides a good interface with computational design process with images as a medium, 4) therefore it is more flexible and operational. This framework aims to provide an approach to achieve faster and better massing design. To reach this objective, there are three main steps: 1) firstly, a generative adversarial network (GAN) model is trained to get wind simulation results from the input site, 2) then, the possible boundaries of massing in different height are generated for exhaustive enumeration, 3) afterwards, run again the GAN wind simulation for the possible boundaries, 4) and finally an assessment method is put forward to obtain the ideal result for the site.","PeriodicalId":129906,"journal":{"name":"Design Computation Input/Output 2022","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128073279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：