Cumincad : CUMINCAD Papers : Search Results

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	sigradi2020_149
id	sigradi2020_149
authors	Canestrino, Giuseppe; Laura, Greco; Spada, Francesco; Lucente, Roberta
year	2020
title	Generating architectural plan with evolutionary multiobjective optimization algorithms: a benchmark case with an existent construction system
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. 149-156
summary	In architectural design, evolutionary multiobjective optimization algorithms (EMOA) have found use in numerous practical applications in which qualitative and quantitative aspects can be transformed into fitness functions to be optimized. This paper shows that they can be used in an architectural plan design process that starts from a more traditional approach. The benchmark case uses a novel construction system, called Ac.Ca. Building, with a vast architectural and technological database, arleady validated, to generate architectural plan for a residential towerbuilding with a parametric approach and EMOA. The proposed framework differs from past research because uses spatial units with high level of architectural and tecnological definition.
keywords	Architectural design, Parametric architecture, Performance-driven design, architectural layout, evolutionary multiobjective optimization
series	SIGraDi
email
last changed	2021/07/16 11:48

_id	caadria2020_008
id	caadria2020_008
authors	Wang, Likai, Chen, Kian Wee, Janssen, Patrick and Ji, Guohua
year	2020
title	Enabling Optimisation-based Exploration for Building Massing Design - A Coding-free Evolutionary Building Massing Design Toolkit in Rhino-Grasshopper
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. 255-264
doi	https://doi.org/10.52842/conf.caadria.2020.1.255
summary	This paper presents an evolutionary design toolkit for performance-based building massing design optimisation. The toolkit is aimed to assist architects in exploring a wide range of building massing design alternatives guided by various performance objectives, thereby encouraging architects to incorporate evolutionary design optimisation for enriching design ideation at the outset of the design process. The toolkit is implemented in the Rhino-Grasshopper environment and includes components of a diversity-guided evolutionary algorithm and two pre-defined parametric models capable of generating a wide range of massing designs. The evolutionary algorithm can yield diverse design variants from the optimisation process and present more informative results with higher design differentiation. The pre-defined parametric models require minimal customisation from the architects. By using the toolkit, architects can readily explore high-performing building design with performance-based design optimisation with ease, and the coding-free optimisation workflow also streamlines the design process.
keywords	evolutionary design; building massing design; performance-based design; design process; design exploration
series	CAADRIA
email
last changed	2022/06/07 07:58

_id	caadria2020_258
id	caadria2020_258
authors	Beatricia, Beatricia, Indraprastha, Aswin and Koerniawan, M. Donny
year	2020
title	Revisiting Packing Algorithm - A Strategy for Optimum Net-to Gross Office Design
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. 405-414
doi	https://doi.org/10.52842/conf.caadria.2020.1.405
summary	Net-to-gross efficiency is defined as the ratio of net area to a gross area of a building. Net-to-gross efficiency will determine the quantity of leasable building area. On the other side, the effectiveness of the spatial distribution of a floor plan design must follow the value of net-to-gross efficiency. Therefore in the context of office design, there are two challenges need to be improved: 1) to get an optimum value of efficiency, architects need to assign the amount and size of the office units which can be effectively arranged, and 2) to fulfill high net-to-gross efficiency value that usually set out at minimal 85%. This paper aims to apply the packing algorithm as a strategy to achieve optimum net-to-gross efficiency and generating spatial configuration of office units that fit with the result. Our study experimented with series of models and simulations consisting of three stages that start from finding net-to-gross efficiency, defining office unit profiles based on preferable office space units, and applying the packing algorithm to get an optimum office net-to-gross efficiency. Computational processes using physics engine and optimization solvers have been utilized to generate design layouts that have minimal spatial residues, hence increasing the net-to-gross ratio.
keywords	net-to-gross efficiency; packing algorithm; modular office area; area optimization;
series	CAADRIA
email
last changed	2022/06/07 07:54

_id	acadia20_208p
id	acadia20_208p
authors	Bernier-Lavigne, Samuel
year	2020
title	Object-Field
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. 208-213
summary	This project aims to continue the correlative study between two fundamental entities of digital architecture: the object and the field. Following periods of experimentations on the ""field"" (materialization of flows of data through animation), the ""field of objects"" (parametricism), the ""object"" (OOO), we investigate the last possible interaction remaining: the ""object-field,"" by merging the formal characteristics of the object with the structural flow of its internal field. This investigation is achieved by exploring the high-resolution features of 3d printing in the design of autonomous architectural objects expressing materiality through topological optimization. The objects are generated by an iterative process of volumetric reduction, resulting in an ensemble of monoliths. Four of them are selected and analyzed through topological optimization in order to extract their internal fields. Next, a series of high-resolution algorithmic systems translate the structural information into 3d printed materiality. Of the four object-fields, one materializes, close to identical, the result of the optimization, giving the keystone to understanding the others. The second one expresses the structural flow through a 1mm voxel system, informed by the optimization, having the effect of stiffening the structure where it is needed and thus generating a new topography on the object. The last two explore the blur that this high-resolution can paradoxically create, with complete integration of the optimal structure in a transparent monolith. This is achieved by a vertex displacement algorithm, and the dissolution of the formal data of the monolith and the structural flows, through the mereological assembly of simple linear elements. For each object-field, a series of drawings was developed using specific algorithmic procedures derived from the peculiarities of their complex geometry. The drawings aim to catalyze coherence throughout the project, where similarities, hitherto kept apart by the multiple materialities, begin to dialogue.
series	ACADIA
type	project
email
last changed	2021/10/26 08:08

_id	cdrf2019_103
id	cdrf2019_103
authors	Runjia Tian
year	2020
title	Suggestive Site Planning with Conditional GAN and Urban GIS Data
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_10
summary	In architecture, landscape architecture, and urban design, site planning refers to the organizational process of site layout. A fundamental step for site planning is the design of building layout across the site. This process is hard to automate due to its multi-modal nature: it takes multiple constraints such as street block shape, orientation, program, density, and plantation. The paper proposes a prototypical and extensive framework to generate building footprints as masterplan references for architects, landscape architects, and urban designers by learning from the existing built environment with Artificial Neural Networks. Pix2PixHD Conditional Generative Adversarial Neural Network is used to learn the mapping from a site boundary geometry represented with a pixelized image to that of an image containing building footprint color-coded to various programs. A dataset containing necessary information is collected from open source GIS (Geographic Information System) portals from the city of Boston, wrangled with geospatial analysis libraries in python, trained with the TensorFlow framework. The result is visualized in Rhinoceros and Grasshopper, for generating site plans interactively.
series	cdrf
email
last changed	2022/09/29 07:51

_id	caadria2020_241
id	caadria2020_241
authors	Shireen, Naghmi, Erhan, Halil, Woodbury, Robert and Antle, Alissa N.
year	2020
title	Spatial Metaphors for Multi-Dimensional Design Gallery Interfaces
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. 265-274
doi	https://doi.org/10.52842/conf.caadria.2020.1.265
summary	With increased computing capabilities and large screen displays, the opportunity to support multiple designs in a single interface has recently become practical. Generating a large number of design alternatives is still a challenge but equally is to manage, review, understand and make-sense out of this multi-dimensional design space. Especially, when we consider the human cognitive limitations and the overly crowded informational displays. This research focuses on developing spatial metaphors based on the previous design literature and the findings from a study conducted to understand how to manage large design spaces with thousands of alternatives. We compare the existing design gallery systems used in practice with the spatial metaphors proposed in this paper. The goal is to develop a spatial structuring toolkit for interface designers of such tools.
keywords	Design space exploration; spatial metaphors; multi-dimensional design space; gallery interfaces
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	acadia20_290
id	acadia20_290
authors	Stuart-Smith, Robert; Danahy, Patrick; Revelo La Rotta, Natalia
year	2020
title	Topological and Material Formation
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. 290-299.
doi	https://doi.org/10.52842/conf.acadia.2020.1.290
summary	Extrusion-based additive manufacturing (AM) is gaining traction in the construction industry, offering lower environmental and economic costs through reductions in material and production time. AM designs achieve these reductions by increasing topological and geometric complexity, and through variable material distribution via custom-programmed robot tool paths. Limited approaches are available to develop AM building designs within a topologically free design search or to leverage material affects relative to structural performance. Established methods such as topological structural optimization (TSO) operate primarily within design rationalization, demonstrating less formal or aesthetic diversity than agent-based methods that exhibit behavioral character. While material-extrusion gravitational affects have been explored in AM research using viscous materials such as concrete and ceramics, established methods are not sufficiently integrated into simulation and structural analysis workflows. A novel three-part method is proposed for the design and simulation of extrusion-based AM that includes topoForm, an evolutionary multi-agent software capable of generating diverse topological designs; matForm, an agent-based AM robot tool-path generator that is geometrically agnostic and adapts material effects to local structural and geometric data; and matSim, a material-physics simulation environment that enables high-resolution AM material effects to be simulated and structurally and aesthetically analyzed. The research enables designers to incorporate and simulate material behavior prior to fabrication and produce instructions suitable for industrial robot AM. The approach is demonstrated in the generative design of four AM column-like elements.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	acadia20_238
id	acadia20_238
authors	Zhang, Hang
year	2020
title	Text-to-Form
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. 238-247.
doi	https://doi.org/10.52842/conf.acadia.2020.1.238
summary	Traditionally, architects express their thoughts on the design of 3D architectural forms via perspective renderings and standardized 2D drawings. However, as architectural design is always multidimensional and intricate, it is difficult to make others understand the design intention, concrete form, and even spatial layout through simple language descriptions. Benefiting from the fast development of machine learning, especially natural language processing and convolutional neural networks, this paper proposes a Linguistics-based Architectural Form Generative Model (LAFGM) that could be trained to make 3D architectural form predictions based simply on language input. Several related works exist that focus on learning text-to-image generation, while others have taken a further step by generating simple shapes from the descriptions. However, the text parsing and output of these works still remain either at the 2D stage or confined to a single geometry. On the basis of these works, this paper used both Stanford Scene Graph Parser (Sebastian et al. 2015) and graph convolutional networks (Kipf and Welling 2016) to compile the analytic semantic structure for the input texts, then generated the 3D architectural form expressed by the language descriptions, which is also aided by several optimization algorithms. To a certain extent, the training results approached the 3D form intended in the textual description, not only indicating the tremendous potential of LAFGM from linguistic input to 3D architectural form, but also innovating design expression and communication regarding 3D spatial information.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2020_024
id	caadria2020_024
authors	Zheng, Hao and Ren, Yue
year	2020
title	Architectural Layout Design through Simulated Annealing Algorithm
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. 275-284
doi	https://doi.org/10.52842/conf.caadria.2020.1.275
summary	Simulated Annealing is an artificial intelligence algorithm for finding the optimal solution of a proposition in an ample search space, which is based on the similarity between the physical annealing process of solid materials and the combinatorial optimization problem. In architectural layout design, although architects usually rely on their subjective design concepts to arrange buildings in a site, the judging criteria hidden in their design concepts are understandable. They can be summarized and parameterized as a combination of penalty and reward functions. By defining the functions to evaluate a design plan, then using the simulated annealing algorithm to search the optimal solution, the plan can be optimized and generated automatically. Six penalty and reward functions are proposed with different parameter weights in this article, which become a guideline for architectural layout design, especially for residential area planning. Then the results of several tests are shown, in which the parameter weights are adjusted, and the importance of each function is integrated. Lastly, a recommended weight and "temperature" setting are proposed, and a system of generating architectural layout is invented, which releases architects from building arranging work in an early stage.
keywords	Architectural Layout; Simulated Annealing; Artificial Intelligence; Computational Design
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	ascaad2022_102
id	ascaad2022_102
authors	Turki, Laila; Ben Saci, Abdelkader
year	2022
title	Generative Design for a Sustainable Urban Morphology
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 434-449
summary	The present work concerns the applications of generative design for sustainable urban fabric. This represents an iterative process that involves an algorithm for the generation of solar envelopes to satisfy solar and density constraints. We propose in this paper to explore a meta-universe of human-machine interaction. It aims to design urban forms that offer solar access. This being to minimize heating energy expenditure and provide solar well-being. We propose to study the impact of the solar strategy of building morphosis on energy exposure. It consists of determining the layout and shape of the constructions based on the shading cut-off time. This is a period of desirable solar access. We propose to define it as a balance between the solar irradiation received in winter and that received in summer. We rely on the concept of the solar envelope defined since the 1970s by Knowles and its many derivatives (Koubaa Turki & al., 2020). We propose a parametric model to generate solar envelopes at the scale of an urban block. The generative design makes it possible to create a digital model of the different density solutions by varying the solar access duration. The virtual environment created allows exploring urban morphologies resilient both to urban densification and better use of the context’s resources. The seasonal energy balance, between overexposure in summer and access to the sun in winter, allows reaching high energy and environmental efficiency of the buildings. We have developed an algorithm on Dynamo for the generation of the solar envelope by shading exchange. The program makes it possible to detect the boundaries of the parcels imported from Revit, establish the layout of the building, and generate the solar envelopes for each variation of the shading cut-off time. It also calculates the FAR1 and the FSI2 from the variation of the shading cut-off time for each parcel of the island. We compare the solutions generated according to the urban density coefficients and the solar access duration. Once the optimal solution has been determined, we export the results back into Revit environment to complete the BIM modelling for solar study. This article proposes a method for designing buildings and neighbourhoods in a virtual environment. The latter acts upstream of the design process and can be extended to the different phases of the building life cycle: detailed design, construction, and use.
series	ASCAAD
email
last changed	2024/02/16 13:38

_id	cdrf2019_179
id	cdrf2019_179
authors	Yuzhe Pan, Jin Qian, and Yingdong Hu
year	2020
title	A Preliminary Study on the Formation of the General Layouts on the Northern Neighborhood Community Based on GauGAN Diversity Output Generator
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_17
summary	Recently, the mainstream gradually has become replacing neighborhood-style communities with high-density residences. The original pleasant scale and enclosed residential spaces have been broken, and the traditional neighborhood relations are going away. This research uses machine learning to train the model to generate a new plan, which is used in today’s residential design. First, in order to obtain a better generation effect, this study extracts the transcendental information of the neighborhood community in north of China, using roads, buildings etc. as morphological representations; GauGAN, compared to the pix2pix and pix2pixHD, used by predecessors, can achieve a clearer and a more diversified output and also fit irregular contours more realistically. ANN model trained by 167 general layout samples of a neighborhood community in north of China from 1950s to 1970s can generate various general layouts in different shapes and scales. The experiment proves that GauGAN is more suitable for general layout generation than pix2pix (pix2pixHD); Distributed training can improve the clarity of the generation and allow later vectorization to be more convenient.
series	cdrf
email
last changed	2022/09/29 07:51

_id	acadia20_584
id	acadia20_584
authors	Brás, Catarina; Castelo-Branco, Renata; Menezes Leitao, António
year	2020
title	Parametric Model Manipulation in Virtual Reality
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. 584-593.
doi	https://doi.org/10.52842/conf.acadia.2020.1.584
summary	Algorithmic design (AD) uses algorithms to describe architectural designs, producing results that are visual by nature and greatly benefit from immersive visualization. Having this in mind, several approaches have been developed that allow architects to access and change their AD programs in virtual reality (VR). However, programming in VR introduces a new level of complexity that hinders creative exploration. Solutions based in visual programming offer limited parameter manipulation and do not scale well, particularly when used in a remote collaboration environment, while those based in textual programming struggle to find adequate interaction mechanisms to efficiently modify existing programs in VR. This research proposes to ease the programming task for architects who wish to develop and experiment with collaborative textual-based AD in VR, by bringing together the user-friendly features of visual programming and the flexibility and scalability of textual programming. We introduce an interface for the most common parametric changes that automatically generates the corresponding code in the AD program, and a hybrid programming solution that allows participants in an immersive collaborative design experience to combine textual programming with this new visual alternative for the parametric manipulation of the design. The proposed workflow aims to foster remote collaborative work in architecture studios, offering professionals of different backgrounds the opportunity to parametrically interact with textual-based AD projects while immersed in them.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2020_347
id	caadria2020_347
authors	Budig, Michael, Heckmann, Oliver, Ng Qi Boon, Amanda, Hudert, Markus, Lork, Clement and Cheah, Lynette
year	2020
title	Data-driven Embodied Carbon Evaluation of Early Building Design Iterations
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. 303-312
doi	https://doi.org/10.52842/conf.caadria.2020.2.303
summary	In the early design phases, Life Cycle Assessment can assist project stakeholders in making informed decisions on choosing structural systems and materials with an awareness of environmental sustainability through their embodied carbon content; yet embodied carbon is difficult to quantify without detailed design information in the early design stages. In response, this paper proposes a novel data-driven tool, prior to the definition of floor plan layouts to perform embodied carbon evaluation of existing building designs based on a Bayesian Neural Network (BNN) regression. The BNN is built from data drawn from existing floor plans of residential buildings, and predicts material volume and embodied carbon from generic design parameters typical in the early design stage. Users will be able to interact with the tool in Grasshopper or as an online resource, input generic design parameters, and obtain comparative visualizations based on the choice of a construction system and its environmental sustainability in a 'shoebox' interface - a simplified three-dimensional representation of a building's primary spatial units generated with the tool.
keywords	Regression; Bayesian Neural Network; High-Rise Residential Buildings
series	CAADRIA
email
last changed	2022/06/07 07:54

_id	artificial_intellicence2019_147
id	artificial_intellicence2019_147
authors	Ding Wen Bao, Xin Yan, Roland Snooks, and Yi Min Xie
year	2020
title	Bioinspired Generative Architectural Design Form-Finding and Advanced Robotic Fabrication Based on Structural Performance
source	Architectural Intelligence Selected Papers from the 1st International Conference on Computational Design and Robotic Fabrication (CDRF 2024)
doi	https://doi.org/https://doi.org/10.1007/978-981-15-6568-7_10
summary	Due to the potential to generate forms with high efficiency and elegant geometry, topology optimization is widely used in architectural and structural designs. This paper presents a working flow of form-finding and robotic fabrication based BESO (Bi-directional Evolutionary Structure Optimization) optimization method. In case there are some other functional requirements or condition limitations, some useful modifications are also implemented in the process. With this kind of working flow, it is convenient to foreknow or control the structural optimization direction before the optimization process. Furthermore, some fabrication details of the optimized model will be discussed because there are also many notable technical points between computational optimization and robotic fabrication.
series	Architectural Intelligence
email
last changed	2022/09/29 07:28

_id	acadia20_464
id	acadia20_464
authors	Elberfeld, Nathaniel; Tessmer, Lavender; Waller, Alexandra
year	2020
title	A Case for Lace
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. 464-473.
doi	https://doi.org/10.52842/conf.acadia.2020.1.464
summary	Textiles and architecture share a long, intertwined history from the earliest enclosures to contemporary high-tech tensile structures. In the Four Elements of Architecture, Gottfried Semper (2010) posited wickerwork and carpet enclosures to be the essential origins of architectural space. More recently, architectural designers are capitalizing on the characteristics of textiles that are difficult or impossible to reproduce with other material systems: textiles are pliable, scalable, and materially efficient. As industrial knitting machines join robotic systems in architecture schools with fabrication- forward agendas, much of the recent developments in textile-based projects make use of knitting. In this paper, we propose an alternative textile technique, lacemaking, for architectural fabrication. We present a method for translating traditional lacemaking techniques to an architectural scale and explore its relative advantages over other textiles. In particular, we introduce bobbin lace and describe its steps both in traditional production and at an architectural scale. We use the unique properties of bobbin lace to form workflows for fabrication and computational analysis. An example of computational analysis demonstrates the ability to optimize lace-based designs towards particular labor objectives. We discuss opportunities for automation and consider the broader implications of understanding a material system relative to the cost of labor to produce designs using it.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	acadia20_74
id	acadia20_74
authors	Bucklin, Oliver; Born, Larissa; Körner, Axel; Suzuki, Seiichi; Vasey, Lauren; T. Gresser, Götz; Knippers, Jan; Menges,
year	2020
title	Embedded Sensing and Control
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. 74-83.
doi	https://doi.org/10.52842/conf.acadia.2020.1.074
summary	This paper investigates an interactive and adaptive control system for kinetic architectural applications with a distributed sensing and actuation network to control modular fiber-reinforced composite components. The aim of the project was to control the actuation of a foldable lightweight structure to generate programmatic changes. A server parses input commands and geometric feedback from embedded sensors and online data to drive physical actuation and generate a digital twin for real-time monitoring. Physical components are origami-like folding plates of glass and carbon-fiber-reinforced plastic, developed in parallel research. Accelerometer data is analyzed to determine component geometry. A component controller drives actuators to maintain or move towards desired positions. Touch sensors embedded within the material allow direct control, and an online user interface provides high-level kinematic goals to the system. A hierarchical control system parses various inputs and determines actuation based on safety protocols and prioritization algorithms. Development includes hardware and software to enable modular expansion. This research demonstrates strategies for embedded networks in interactive kinematic structures and opens the door for deeper investigations such as artificial intelligence in control algorithms, material computation, as well as real-time modeling and simulation of structural systems.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2020_403
id	caadria2020_403
authors	Ghandi, Mona
year	2020
title	Reducing Energy Consumption through Cyber-Physical Adaptive Spaces and Occupants' Biosignals
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. 121-130
doi	https://doi.org/10.52842/conf.caadria.2020.2.121
summary	The field of architecture has long embraced adaptive approaches to address issues of sustainability and efficiency. Building energy consumption accounts for about 40% of the total energy consumption in the U.S. This energy is mainly used for lighting, heating, cooling, and ventilation. Researches show that 30% of that energy is wasted. One of the main reasons for such high energy waste in the commercial (and even private) sectors is a generic assumption about the occupants' preferences. To fill this gap, the objective of this project is to optimize building energy retrofits by creating smart environments that autonomously respond to the occupants' comfort level using affective computing and adaptive systems. This adaptive approach will help optimizing energy consumption without sacrificing occupants' comfort through passive cooling and heating strategy, responding to occupants' preferences detected from their biological and neurological data. Progress towards achieving this goal will make building energy costs more affordable to the benefit of families and businesses and reduce energy waste.
keywords	Human-Computer Interaction; Optimizing Energy Consumption; Sustainability + High Performance Built Environment; Adaptive and Interactive Architecture; Cyber-Physical Spaces, Affective Computing, Occupantsâ€™ Comfort and Well-Being
series	CAADRIA
email
last changed	2022/06/07 07:51

_id	cdrf2019_159
id	cdrf2019_159
authors	Hang Zhang and Ye Huang
year	2020
title	Machine Learning Aided 2D-3D Architectural Form Finding at High Resolution
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_15
summary	In the past few years, more architects and engineers start thinking about the application of machine learning algorithms in the architectural design field such as building facades generation or floor plans generation, etc. However, due to the relatively slow development of 3D machine learning algorithms, 3D architecture form exploration through machine learning is still a difficult issue for architects. As a result, most of these applications are confined to the level of 2D. Based on the state-of-the-art 2D image generation algorithm, also the method of spatial sequence rules, this article proposes a brand-new strategy of encoding, decoding, and form generation between 2D drawings and 3D models, which we name 2D-3D Form Encoding WorkFlow. This method could provide some innovative design possibilities that generate the latent 3D forms between several different architectural styles. Benefited from the 2D network advantages and the image amplification network nested outside the benchmark network, we have significantly expanded the resolution of training results when compared with the existing form-finding algorithm and related achievements in recent years
series	cdrf
email
last changed	2022/09/29 07:51

_id	acadia20_170p
id	acadia20_170p
authors	Pawlowska, Gosia
year	2020
title	Viscous Catenary
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. 170-175
summary	Viscous Catenary is a free-form architectural glass structure that embeds material logic in a distributed system. Multi-curved panels are joined in a ‘catenary channel glass’ assembly, expressing the inherent behavior of the material at high temperatures. Float glass will typically achieve a level of viscosity at 1200°F (650°C), formed in a kiln by draping or “slumping. This hybrid fabrication process combines low-tech hardware and modern digital technologies. Glass panels were formed in a traditional kiln over a set of interchangeable waterjet-cut steel profiles or a repositionable tooling system. Parametric design in Grasshopper was essential to establish a discrete number of unique formwork elements and subdivide the overall geometry by panel size. In this case, each panel in the system was draped over four steel profiles. The formwork encourages a specific curvature in the glass, most precisely at the locations of folding. These moments of control allow the panels to align at their folds and join in an assembly by splice-lamination. Between the folds, the material remains free to shape itself, responding to its thickness, span, time, and temperature- into an undetermined “viscous catenary.” Selectively programming the geometry allows for a degree of material agency to remain in the system. This method differs from existing curved architectural glass, which would typically be pressed into a fully deterministic mold, leaving no opportunity for emergent morphologies. A pilot installation joined using transparent silicone adhesive achieved a height of 90cm with overlapping 30cm tall panels. Laser 3-d scanning between fabrication and assembly helped evaluate the fit between adjacent panels, identifying locations that required reinforcement. More research is needed to improve tolerances and overcome limitations in the adhesive before scaling up the fabrication system. Viscous Catenary succeeds in questioning the formal and structural potential of matter-driven curved architectural glass assemblies.
series	ACADIA
type	project
email
last changed	2021/10/26 08:03

_id	acadia20_228
id	acadia20_228
authors	Alawadhi, Mohammad; Yan, Wei
year	2020
title	BIM Hyperreality
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.
doi	https://doi.org/10.52842/conf.acadia.2020.1.228
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

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