CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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_id acadia20_120
id acadia20_120
authors Barsan-Pipu, Claudiu; Sleiman, Nathalie; Moldovan, Theodor
year 2020
title Affective Computing for Generating Virtual Procedural Environments Using Game Technologies
doi https://doi.org/10.52842/conf.acadia.2020.2.120
source ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 120-129.
summary Architects have long sought to create spaces that can relate to or even induce specific emotional conditions in their users, such as states of relaxation or engagement. Dynamic or calming qualities were given to these spaces by controlling form, perspective, lighting, color, and materiality. The actual impact of these complex design decisions has been challenging to assess, from both quantitative and qualitative standpoints, because neural empathic responses, defined in this paper by feature indexes (FIs) and mind indexes (MIs), are highly subjective experiences. Recent advances in the fields of virtual procedural environments (VPEs) and virtual reality (VR), supported by powerful game engine (GE) technologies, provide computational designers with a new set of design instruments that, when combined with brain-computing interfacing (BCI) and eye-tracking (E-T) hardware, can be used to assess complex empathic reactions. As the COVID-19 health crisis showed, virtual social interaction becomes increasingly relevant, and the social catalytic potential of VPEs can open new design possibilities. The research presented in this paper introduces the cyber-physical design of such an affective computing system. It focuses on how relevant empathic data can be acquired in real time by exposing subjects within a dynamic VR-based VPE and assessing their emotional responses while controlling the actual generative parameters via a live feedback loop. A combination of VR, BCI, and E-T solutions integrated within a GE is proposed and discussed. By using a VPE inside a BCI system that can be accurately correlated with E-T, this paper proposes to identify potential morphological and lighting factors that either alone or combined can have an empathic effect expressed by the relevant responses of the MIs.
series ACADIA
type paper
email
last changed 2023/10/22 12:06

_id cdrf2022_209
id cdrf2022_209
authors Yecheng Zhang, Qimin Zhang, Yuxuan Zhao, Yunjie Deng, Feiyang Liu, Hao Zheng
year 2022
title Artificial Intelligence Prediction of Urban Spatial Risk Factors from an Epidemic Perspective
doi https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_18
source Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary From the epidemiological perspective, previous research methods of COVID-19 are generally based on classical statistical analysis. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. We take the Spatio-temporal data of people infected with new coronary pneumonia before February 28 in Wuhan in 2020 as the research object. We use kriging spatial interpolation technology and core density estimation technology to establish the epidemic heat distribution on fine grid units. We further examine the distribution of nine main spatial risk factors, including agencies, hospitals, park squares, sports fields, banks, hotels, Etc., which are tested for the significant positive correlation with the heat distribution of the epidemic. The weights of the spatial risk factors are used for training Generative Adversarial Network models, which predict the heat distribution of the outbreak in a given area. According to the trained model, optimizing the relevant environment design in urban areas to control risk factors effectively prevents and manages the epidemic from dispersing. The input image of the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area.
series cdrf
email
last changed 2024/05/29 14:02

_id sigradi2020_991
id sigradi2020_991
authors Gomez, Paula; Hadi, Khatereh; Kemenova, Olga; Swarts, Matthew
year 2020
title Spatiotemporal Modeling of COVID-19 Spread in Built Environments
source SIGraDi 2020 [Proceedings of the 24th Conference of the Iberoamerican Society of Digital Graphics - ISSN: 2318-6968] Online Conference 18 - 20 November 2020, pp. 991-996
summary This research proposes a Spatiotemporal Modeling approach to understand the role of architecture, specifically the built environment, in the COVID-19 pandemic. The model integrates spatial and temporal parameters to calculate the probability of spread of and exposure to SARS-CoV-2 virus (responsible of COVID-19 disease) due to the combination of four aspects: Spatial configuration, organizational schedules, people’s behavior, and virus characteristics. Spatiotemporal Modeling builds upon the current models of building analytics for architecture combined with predictive models of COVID-19 spread. While most of the current research on COVID-19 spread focuses on mathematical models at regional scales and the CDC guidelines emphasizing on human behavior, our research focuses on the role of buildings in this pandemic, as the intermediate mechanism where human and social activities occur. The goal is to understand the most significant parameters that influence the virus spread within built environments, including human-to-human, fomite (surface-to-human), and airborne ways of transmission, with the purpose of providing a comprehensive parametric model that may help identify the most influential design and organizational decisions for controlling the pandemic. The proof-of-concept study is a healthcare facility.
keywords Spatiotemporal modeling, Agent-based simulation, COVID-19, Virus spread, Built environments, Human behavior, Social distancing
series SIGraDi
email
last changed 2021/07/16 11:53

_id acadia20_340
id acadia20_340
authors Soana, Valentina; Stedman, Harvey; Darekar, Durgesh; M. Pawar, Vijay; Stuart-Smith, Robert
year 2020
title ELAbot
doi https://doi.org/10.52842/conf.acadia.2020.1.340
source ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 340-349.
summary This paper presents the design, control system, and elastic behavior of ELAbot: a robotic bending active textile hybrid (BATH) structure that can self-form and transform. In BATH structures, equilibrium emerges from interaction between tensile (form active) and elastically bent (bending active) elements (Ahlquist and Menges 2013; Lienhard et al. 2012). The integration of a BATH structure with a robotic actuation system that controls global deformations enables the structure to self-deploy and achieve multiple three-dimensional states. Continuous elastic material actuation is embedded within an adaptive cyber-physical network, creating a novel robotic architectural system capable of behaving autonomously. State-of-the-art BATH research demonstrates their structural efficiency, aesthetic qualities, and potential for use in innovative architectural structures (Suzuki and Knippers 2018). Due to the lack of appropriate motor-control strategies that exert dynamic loading deformations safely over time, research in this field has focused predominantly on static structures. Given the complexity of controlling the material behavior of nonlinear kinetic elastic systems at an architectural scale, this research focuses on the development of a cyber-physical design framework where physical elastic behavior is integrated into a computational design process, allowing the control of large deformations. This enables the system to respond to conditions that could be difficult to predict in advance and to adapt to multiple circumstances. Within this framework, control values are computed through continuous negotiation between exteroceptive and interoceptive information, and user/designer interaction.
series ACADIA
type paper
email
last changed 2023/10/22 12:06

_id acadia20_130p
id acadia20_130p
authors Swingle, Tyler; Zampini, Davide; Clifford, Brandon
year 2020
title Patty & Jan
source ACADIA 2020: Distributed Proximities / Volume II: Projects [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95253-6]. Online and Global. 24-30 October 2020. edited by M. Yablonina, A. Marcus, S. Doyle, M. del Campo, V. Ago, B. Slocum. 130-135
summary The construction of architecture relies on an orchestra of moving parts and components throughout the process. These components are designed for the primary loads of the ultimate resting positions, but must also accommodate for secondary loads that occur during the assembly process. Safety, budget, and timing are the most influential factors in conducting the orchestra of architectural construction and typically set the tempo. Patty & Jan explore the curious and playful possibilities of secondary loads such as movement, momentum, and impact. This impractical assembly is not intended to negate practical considerations, but to elevate the field of construction above problem-solving. Patty & Jan builds upon previous research into moving massive masonry elements with little energy by controlling the center of mass (CoM) via physical computation and innovative concrete technologies such as proprietary chemical admixtures and special lightweight additions to entrain air as well as impart high fluidity. The resulting densities of the two concrete mixtures range from one-third the density to double the density of conventional concrete. Patty & Jan contributes to this ongoing research by incorporating the fourth dimension into the assembly process. Patty & Jan are a partnership. They have a reciprocal relationship with one another that ensures one cannot assemble without the other. Beginning with Patty and Jan at a pre-determined distance apart, a weighted tool is removed from Patty to alter the CoM and create a righting moment. Rotating along the riding surface, Patty over rotates to collide with Jan and strikes a resounding echo. The controlled impact triggers Jan first to rotate backward, rebound off its braking surface, and then counter-rotate towards Patty. The two meet along their assembly surfaces in the middle and slip effortlessly into their final assembled position. The resulting performance of Patty & Jan is an embedded intelligence of a theatrical assembly between two massive concrete masonry units (MCMU) through their momentum. Patty & Jan demonstrate the ability to predict the inherent movements and autonomous assemblies of MCMUs. It extends the potential of assembly methods to be social generators such as spectacles or performances. This research is a foundation for thinking about more extensive and more complex construction choreographies that engage material as well as human bodies in the building of architecture.
series ACADIA
type project
email
last changed 2021/10/26 08:03

_id caadria2020_306
id caadria2020_306
authors Akizuki, Yuta, Bernhard, Mathias, Kakooee, Reza, Kladeftira, Marirena and Dillenburger, Benjamin
year 2020
title Generative Modelling with Design Constraints - Reinforcement Learning for Object Generation
doi https://doi.org/10.52842/conf.caadria.2020.1.445
source D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 445-454
summary Generative design has been explored to produce unprecedented geometries, nevertheless design constraints are, in most cases, second-graded in the computational process. In this paper, reinforcement learning is deployed in order to explore the potential of generative design satisfying design objectives. The aim is to overcome the three issues identified in the state of the art: topological inconsistency, less variations in style and unpredictability in design. The goal of this paper is to develop a machine learning framework, which works as an intellectual design interpreter capable of codifying an input geometry to form a new geometry. Experiments demonstrate that the proposed method can generate a family of tables of unique aesthetics, satisfying topological consistency under given constraints.
keywords generative design; computational design; data-driven design; reinforcement learning; machine learning
series CAADRIA
email
last changed 2022/06/07 07:54

_id acadia20_228
id acadia20_228
authors Alawadhi, Mohammad; Yan, Wei
year 2020
title BIM Hyperreality
doi https://doi.org/10.52842/conf.acadia.2020.1.228
source ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 228-236.
summary Deep learning is expected to offer new opportunities and a new paradigm for the field of architecture. One such opportunity is teaching neural networks to visually understand architectural elements from the built environment. However, the availability of large training datasets is one of the biggest limitations of neural networks. Also, the vast majority of training data for visual recognition tasks is annotated by humans. In order to resolve this bottleneck, we present a concept of a hybrid system—using both building information modeling (BIM) and hyperrealistic (photorealistic) rendering—to synthesize datasets for training a neural network for building object recognition in photos. For generating our training dataset, BIMrAI, we used an existing BIM model and a corresponding photorealistically rendered model of the same building. We created methods for using renderings to train a deep learning model, trained a generative adversarial network (GAN) model using these methods, and tested the output model on real-world photos. For the specific case study presented in this paper, our results show that a neural network trained with synthetic data (i.e., photorealistic renderings and BIM-based semantic labels) can be used to identify building objects from photos without using photos in the training data. Future work can enhance the presented methods using available BIM models and renderings for more generalized mapping and description of photographed built environments.
series ACADIA
type paper
email
last changed 2023/10/22 12:06

_id cdrf2019_68
id cdrf2019_68
authors Pierre Cutellic
year 2020
title Growing Shapes with a Generalised Model from Neural Correlates of Visual Discrimination
doi https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_7
source Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary This paper focuses on the application of visual Event-Related Potentials (ERP) in better generalisations for design and architectural modelling. It makes use of previously built techniques and trained models on EEG signals of a singular individual and observes the robustness of advanced classification models to initiate the development of presentation and classification techniques for enriched visual environments by developing an iterative and generative design process of growing shapes. The pursued interest is to observe if visual ERP as correlates of visual discrimination can hold in structurally similar, but semantically different, experiments and support the discrimination of meaningful design solutions. Following bayesian terms, we will coin this endeavour a Design Belief and elaborate a method to explore and exploit such features decoded from human visual cognition.
series cdrf
email
last changed 2022/09/29 07:51

_id caadria2020_054
id caadria2020_054
authors Shen, Jiaqi, Liu, Chuan, Ren, Yue and Zheng, Hao
year 2020
title Machine Learning Assisted Urban Filling
doi https://doi.org/10.52842/conf.caadria.2020.2.679
source D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 679-688
summary When drawing urban scale plans, designers should always define the position and the shape of each building. This process usually costs much time in the early design stage when the condition of a city has not been finally determined. Thus the designers spend a lot of time working forward and backward drawing sketches for different characteristics of cities. Meanwhile, machine learning, as a decision-making tool, has been widely used in many fields. Generative Adversarial Network (GAN) is a model frame in machine learning, specially designed to learn and generate image data. Therefore, this research aims to apply GAN in creating urban design plans, helping designers automatically generate the predicted details of buildings configuration with a given condition of cities. Through the machine learning of image pairs, the result shows the relationship between the site conditions (roads, green lands, and rivers) and the configuration of buildings. This automatic design tool can help release the heavy load of urban designers in the early design stage, quickly providing a preview of design solutions for urban design tasks. The analysis of different machine learning models trained by the data from different cities inspires urban designers with design strategies and features in distinct conditions.
keywords Artificial Intelligence; Urban Design; Generative Adversarial Networks; Machine Learning
series CAADRIA
email
last changed 2022/06/07 07:56

_id ecaade2020_486
id ecaade2020_486
authors Teng, Teng, Jia, Mian and Sabin, Jenny
year 2020
title Scutoid Brick - The Designing of Epithelial cell inspired-brick in Masonry shell System
doi https://doi.org/10.52842/conf.ecaade.2020.1.563
source Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 563-572
summary This paper focuses on the design of individual bricks in a masonry shell system that are inspired and informed by the reorganization of epithelial cells within tissues. Starting from a newly discovered shape called "Scutoid", we first investigated how epithelial cells within living animals are packed three dimensionally within tissues. We focused on the living mechanisms within these cells that facilitate tissue curvature in the creatures' organs, skin, and blood vessels. By utilizing this generative geometric approach, we created a series of parametric generators and modeling kits to represent this mechanism and process. We then explored the potential for adopting this mechanism into larger-scale settings. Meanwhile, we discovered that the deformation of individual epithelial cells during the bending process generates an intriguing triangular connection along the bending direction. We managed to translate this unique feature to the architectural scale as a joint system for connecting bricks in a masonry shell structure. Based on the above findings, we designed and fabricated a set of models for the masonry shell structure that are generated from scutoid bricks and this unique joint. The geometrical characteristics of scutoid bricks allows the packing of four bricks with just two joints. The work that we have generated thus far contributes to solving issues of shell design and fabrication from the perspective of individual units. The result of the shell structure model demonstrates that applying the epithelial cell inspired-block masonry system is a feasible approach for the construction of shell structures.
keywords Epithelial cell; Scutoid; Bio-inspired Design; Generative Design; Masonry shell
series eCAADe
email
last changed 2022/06/07 07:58

_id ecaade2020_093
id ecaade2020_093
authors Veloso, Pedro and Krishnamurti, Ramesh
year 2020
title An Academy of Spatial Agents - Generating spatial configurations with deep reinforcement learning
doi https://doi.org/10.52842/conf.ecaade.2020.2.191
source Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 191-200
summary Agent-based models rely on decentralized decision making instantiated in the interactions between agents and the environment. In the context of generative design, agent-based models can enable decentralized geometric modelling, provide partial information about the generative process, and enable fine-grained interaction. However, the existing agent-based models originate from non-architectural problems and it is not straight-forward to adapt them for spatial design. To address this, we introduce a method to create custom spatial agents that can satisfy architectural requirements and support fine-grained interaction using multi-agent deep reinforcement learning (MADRL). We focus on a proof of concept where agents control spatial partitions and interact in an environment (represented as a grid) to satisfy custom goals (shape, area, adjacency, etc.). This approach uses double deep Q-network (DDQN) combined with a dynamic convolutional neural-network (DCNN). We report an experiment where trained agents generalize their knowledge to different settings, consistently explore good spatial configurations, and quickly recover from perturbations in the action selection.
keywords space planning; agent-based model; interactive generative systems; artificial intelligence; multi-agent deep reinforcement learning
series eCAADe
email
last changed 2022/06/07 07:58

_id ecaade2020_007
id ecaade2020_007
authors Yu, De
year 2020
title Reprogramming Urban Block by Machine Creativity - How to use neural networks as generative tools to design space
doi https://doi.org/10.52842/conf.ecaade.2020.1.249
source Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 249-258
summary The democratization of design requires balancing all sorts of factors in space design. However, the traditional way to organize spatial relationships cannot deal with such complex design objectives. Can one find another form of creativity rather the human brain to design space? As Margaret Boden mentioned, "computers and creativity make interesting partners with respect to two different projects." This paper addresses whether machine creativity in the form of neural networks could be considered as a powerful generative tool to reprogram urban block in order to meet multi-users' needs. It tested this theory in a specific block model called Agri-tecture, a new architectural form combing farming with the urban built environment. Specifically, the machine empowered by Generative Adversarial Network designed spatial layouts based on learning the existing cases. Nevertheless, since the machine can hardly avoid errors, architects need to intervene and verify the machine's work. Thus, a synergy between human creativity and machine creativity is called for.
keywords machine creativity; Generative Adversarial Network; spatial layout; creativity combination; Agri-tecture
series eCAADe
email
last changed 2022/06/07 07:57

_id acadia20_110
id acadia20_110
authors Zhang, Mengni; Dewey, Clara; Kalantari, Saleh
year 2020
title Dynamic Anthropometric Modeling Interface
doi https://doi.org/10.52842/conf.acadia.2020.1.110
source ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 110-119.
summary In this paper, we propose a Kinect-based Dynamic Anthropometric Modeling Interface (DAMI), built in Rhinoceros with Grasshopper for patient room layout optimization and nurse posture evaluations. Anthropometry is an important field that studies human body measurements to help designers improve product ergonomics and reduce negative health consequences such as musculoskeletal disorders (MSDs). Unlike existing anthropometric tools, which rely on generic human body datasets and static posture models, DAMI tracks and records user postures in real time, creating custom 3D body movement models that are typically absent in current space-planning practices. A generic hospital patient room, which contains complex and ergonomically demanding activities for nurses, was selected as an initial testing environment. We will explain the project background, the methods used to develop DAMI, and demonstrate its capabilities. There are two main goals DAMI aims to achieve. First, as a generative tool, it will reconstruct dynamic body point cloud models, which will be used as input for optimizing room layout during a project’s schematic design phase. Second, as an evaluation tool, by encoding and visualizing the Rapid Entire Body Assessment (REBA) scores, DAMI will illustrate the spatiotemporal relationship between nurse postures and the built environment during a project’s construction phase or post occupancy evaluation. We envision a distributed system of Kinect sensors to be embedded in various hospital rooms to help architects, planners, and facility managers improve nurse work experiences through better space planning.
series ACADIA
type paper
email
last changed 2023/10/22 12:06

_id caadria2020_391
id caadria2020_391
authors Caetano, Inês, Garcia, Sara, Pereira, Inês and Leitão, António
year 2020
title Creativity Inspired by Analysis - an algorithmic design system for designing structurally feasible façades
doi https://doi.org/10.52842/conf.caadria.2020.1.599
source D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 599-608
summary Although structural performance has a crucial role in the overall design, its analysis is often postponed to later design stages. This largely occurs because analysis processes are time consuming and require the use of specific models and tools. This problem is then aggravated by the number of design variations that have to be analysed until an acceptable solution is found. However, the implementation of design changes at later stages is limited, as also is their impact on the solution's final performance. Fortunately, with algorithmic design, we can overcome these limitations, as it not only supports complex designs and facilitates design changes, but also automates the production of the specific models and their subsequent analysis and optimization. In this research we focus on buildings façades, proposing an algorithmic design system to support their design, structural analysis, and optimization.
keywords Performance-based Design; Algorithmic Design; Algorithmic Structural Analysis; Algorithmic Optimization; Façade Design
series CAADRIA
email
last changed 2022/06/07 07:54

_id acadia20_688
id acadia20_688
authors del Campo, Matias; Carlson, Alexandra; Manninger, Sandra
year 2020
title 3D Graph Convolutional Neural Networks in Architecture Design
doi https://doi.org/10.52842/conf.acadia.2020.1.688
source ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 688-696.
summary The nature of the architectural design process can be described along the lines of the following representational devices: the plan and the model. Plans can be considered one of the oldest methods to represent spatial and aesthetic information in an abstract, 2D space. However, to be used in the design process of 3D architectural solutions, these representations are inherently limited by the loss of rich information that occurs when compressing the three-dimensional world into a two-dimensional representation. During the first Digital Turn (Carpo 2013), the sheer amount and availability of models increased dramatically, as it became viable to create vast amounts of model variations to explore project alternatives among a much larger range of different physical and creative dimensions. 3D models show how the design object appears in real life, and can include a wider array of object information that is more easily understandable by nonexperts, as exemplified in techniques such as building information modeling and parametric modeling. Therefore, the ground condition of this paper considers that the inherent nature of architectural design and sensibility lies in the negotiation of 3D space coupled with the organization of voids and spatial components resulting in spatial sequences based on programmatic relationships, resulting in an assemblage (DeLanda 2016). These conditions constitute objects representing a material culture (the built environment) embedded in a symbolic and aesthetic culture (DeLanda 2016) that is created by the designer and captures their sensibilities.
series ACADIA
type paper
email
last changed 2023/10/22 12:06

_id ijac202220109
id ijac202220109
authors Ortner, F. Peter; Jing Zhi Tay
year 2022
title Resilient by design: Informing pandemic-safe building redesign with computational models of resident congestion
source International Journal of Architectural Computing 2022, Vol. 20 - no. 1, pp. 129–144
summary This paper describes a computational design-support tool created in response to safe-distancing measures enforced during the COVID-19 pandemic. The tool was developed for a specific use case: understanding congestion in crowded migrant worker dormitories that experienced high rates of COVID-19 transmission in 2020. Building from agent-based and network-based computational simulations, the tool presents a hybrid method for simulating building resident movements based on known or pre-determined schedules and likely itineraries. This hybrid method affords the design tool a novel approach to simultaneous exploration of spatial and temporal design scenarios. The paper demonstrates the use of the tool on an anonymised case study of a high-density migrant worker dormitory, comparing results from a baseline configuration against design variations that modify dormitory physical configuration and schedule. Comparisons between the design scenarios provide evidence for reflections on pandemic-resilient design and operation strategies for dor- mitories. A conclusions section considers the extent to which the model and case study results are applicable to other dense institutional buildings and describes the paper’s contributions to general understanding of configurational and operational aspects of resilience in the built environment.
keywords Design for resilience, evidence-based design, design support, agent-based model, schedule-based model, network analysis
series journal
last changed 2024/04/17 14:29

_id ecaade2020_348
id ecaade2020_348
authors Chiujdea, Ruxandra Stefania and Nicholas, Paul
year 2020
title Design and 3D Printing Methodologies for Cellulose-based Composite Materials
doi https://doi.org/10.52842/conf.ecaade.2020.1.547
source Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 547-554
summary A growing awareness of architecture's environmental responsibility is encouraging a shift from an industrial age to an ecological one. This shift emphasises a new era of materiality, characterised by a special focus on bio-polymers. The potential of these materials is to address unsustainable modes of resource consumption, and to rebalance our relationship with the natural. However, bio-polymers also challenge current design and manufacturing practices, which rely on highly manufactured and standardized materials. In this paper, we present material experiments and digital design and fabrication methodologies for cellulose-based composites, to create porous biodegradable panels. Cellulose, the most abundant bio-polymer on Earth, has potential for differentiated architectural applications. A key limit is the critical role of additive fabrication methods for larger scale elements, which are a subject of ongoing research. In this paper, we describe how controlling the interdependent relationship between the additive manufacturing process and the material grading enables the manipulation of the material's performance, and the related control aspects including printing parameters such as speed, nozzle diameter, air flow, etc., as well as tool path trajectory. Our design exploration responds to the emerging fabrication methods to achieve different levels of porosity and depth which define the geometry of a panel.
keywords cellulose-based composite material; additive manufacturing; material grading; digital fabrication; spatial print trajectory; porous panels
series eCAADe
email
last changed 2022/06/07 07:56

_id caadria2020_395
id caadria2020_395
authors Loo, Stella Yi Ning, Jayashankar, Dhileep Kumar, Gupta, Sachin and Tracy, Kenneth
year 2020
title Hygro-Compliant: Responsive Architecture with Passively Actuated Compliant Mechanisms
doi https://doi.org/10.52842/conf.caadria.2020.1.223
source D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 223-232
summary Research investigating water-driven passive actuation demonstrates the potential to transform how buildings interact with their environment while avoiding the complications of conventionally powered actuation. Previous experiments evidence the possibilities of bi-layer materials (Reichert, Menges, and Correa 2015; Correa et al. 2015) and mechanical assemblies with discretely connected actuating members (Gupta et al. 2019). By leveraging changes in weather to power actuated building components these projects explore the use of smart biomaterials and responsive building systems. Though promising the implementation of these technologies requires deep engagement into material synthesis and fabrication. This paper presents the design and prototyping of a rain responsive façade system using chitosan hygroscopic films as actuators counterbalanced by programmed compliant mechanisms. Building on previous work into chitosan film assemblies this research focuses on the development of compliant mechanisms as a means of controlling movement without over-complicated rotating parts.
keywords Passive Actuation; Responsive Architecture; Bio-polymers; 4D Structures; Compliant Mechanism
series CAADRIA
email
last changed 2022/06/07 07:52

_id sigradi2020_594
id sigradi2020_594
authors Poustinchi, Ebrahim; Hashemi, Mona; Krivanek, Cory
year 2020
title Physical Interface for Robotic Marionette Camera (RMC): Hardware Controlling Platform for Robotic Videography Motion Design
source SIGraDi 2020 [Proceedings of the 24th Conference of the Iberoamerican Society of Digital Graphics - ISSN: 2318-6968] Online Conference 18 - 20 November 2020, pp. 594-599
summary This project-based research is an investigation on controlling robotic videography/camera through an interactive physical interface. Here referred to as Robotic Marionette Camera (RMC), this research project is enabling designers and videographers to design precise robotic videography scenarios and camera-paths in the physical world with similar qualities to the digital design environments.Using the ideas of a “digital” camera in design software platforms, RMC looks at concepts such as aiming, zooming in and out, panning, orbiting, and other motions/operations borrowed from cinematography, such as tilting, rolling and trucking amongst others.As a physical/hardware interface, RMC enables real-time interaction with an industrial robot arm through a custom-made hardware controller. Using a tangible interface, RMC users can design, edit, and program the robotic videography paths interactively without a need for programming knowledge.
keywords Robotics, Design, Interface Design, Videography, Human-Robot Interaction
series SIGraDi
email
last changed 2021/07/16 11:52

_id caadria2020_443
id caadria2020_443
authors Abuzuraiq, Ahmed M. and Erhan, Halil
year 2020
title The Many Faces of Similarity - A Visual Analytics Approach for Design Space Simplification
doi https://doi.org/10.52842/conf.caadria.2020.1.485
source D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 485-494
summary Generative design methods may involve a complex design space with an overwhelming number of alternatives with their form and design performance data. Existing research addresses this complexity by introducing various techniques for simplification through clustering and dimensionality reduction. In this study, we further analyze the relevant literature on design space simplification and exploration to identify their potentials and gaps. We find that the potentials include: alleviating the choice overload problem, opening up new venues for interrelating design forms and data, creating visual overviews of the design space and introducing ways of creating form-driven queries. Building on that, we present the first prototype of a design analytics dashboard that combines coordinated and interactive visualizations of design forms and performance data along with the result of simplifying the design space through hierarchical clustering.
keywords Visual Analytics; Design Exploration; Dimensionality Reduction; Clustering; Similarity-based Exploration
series CAADRIA
email
last changed 2022/06/07 07:54

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