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	ascaad2021_065
id	ascaad2021_065
authors	Fraschini, Matteo; Julian Raxworthy
year	2021
title	Territories Made by Measure: The Parametric as a Way of Teaching Urban Design Theory
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 494-506
summary	Design tools like Grasshopper are often used to either generate novel forms, to automate certain design processes or to incorporate scientific factors. However, any Grasshopper definition has certain assumptions about design and space built into it from its earliest genesis, when the initial algorithm is set out. Correspondingly, implicit theoretical positions are built into definitions, and therefore its results. Approaching parametric design as a question of architectural, landscape architectural or urban design theory allows the breaking down of traditional boundaries between the technical and the historical or theoretical, and the way parametric design, and urban design history & theory, can be conveyed in the teaching environment. Once the boundaries between software and history & theory are transgressed, Grasshopper can be a way of testing the principles embedded in historical designs and thus these two disciplines can be joined. In urban design, there is an inherent clash between an ideal model and existing urban geography or morphology, and also between formal (qualitative) and numerical (quantitative) aspects. If a model provides a necessary vision for future development, an existing topography then results from the continuous human and natural modifications of a territory. To explore this hypothesis, the “Urban Design Representation” subject in the Master of Urban Design program at the University of Cape Town taught in 2017 & 2018 was approached “parametrically” from these two opposite, albeit convergent, starting points: the conceptual/rational versus the physical/empiric representations of a territory. In this framework, Grasshopper was used to represent typical standards and parameters of modern urban planning (for example, Floor/Area Ratio, height and distance between buildings, site coverage, etc), and a typological approach was adopted to study and “decode” the relationship between public and private space, between the street, the block and topography, between solids and voids. This methodology permits a cross-comparison of different urban design models and the immediate evaluation of their formal outputs derived from parametric data.
series	ASCAAD
email
last changed	2021/08/09 13:13

_id	caadria2018_033
id	caadria2018_033
authors	Bai, Nan and Huang, Weixin
year	2018
title	Quantitative Analysis on Architects Using Culturomics - Pattern Study of Prizker Winners Based on Google N-gram Data
doi	https://doi.org/10.52842/conf.caadria.2018.2.257
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 257-266
summary	Quantitative studies using the corpus Google Ngram, namely Culturomics, have been analyzing the implicit patterns of culture changes. Being the top-standard prize in the field of Architecture since 1979, the Pritzker Prize has been increasingly diversified in the recent years. This study intends to reveal the implicit pattern of Pritzker Winners using the method of Culturomics, based on the corpus of Google Ngram to reveal the relationship of the sign of their fame and the fact of prize-winning. 48 architects including 32 awarded and 16 promising are analyzed in the printed corpus of English language between 1900 and 2008. Multiple regression models and multiple imputation methods are used during the data processing. Self-Organizing Map is used to define clusters among the awarded and promising architects. Six main clusters are detected, forming a 3×2 network of fame patterns. Most promising architects can be told from the clustering, according to their similarity to the more typical prize winners. The method of Culturomics could expand the sight of architecture study, giving more possibilities to reveal the implicit patterns of the existing empirical world.
keywords	Culturomics; Google Ngram; Pritzker Prize; Fame Pattern; Self-Organizing Map
series	CAADRIA
email
last changed	2022/06/07 07:54

_id	acadia18_166
id	acadia18_166
authors	Kvochick, Tyler
year	2018
title	Sneaky Spatial Segmentation. Reading Architectural Drawings with Deep Neural Networks and Without Labeling Data
doi	https://doi.org/10.52842/conf.acadia.2018.166
source	ACADIA // 2018: Recalibration. On imprecisionand infidelity. [Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-17729-7] Mexico City, Mexico 18-20 October, 2018, pp. 166-175
summary	Currently, it is nearly impossible for an artificial neural network to generalize a task from very few examples. Humans, however, excel at this. For instance, it is not necessary for a designer to see thousands or millions of unique examples of how to place a given drawing symbol in a way that meets the economic, aesthetic, and performative goals of the project. In fact, the goals can be (and usually are) communicated abstractly in natural language. Machine learning (ML) models, however, do need numerous examples. The methods that we explore here are an attempt to circumvent this in order to make ML models more immediately useful. In this work, we present progress on the application of contemporary ML techniques to the design process in the architecture, engineering, and construction (AEC) industry. We introduce a technique to partially circumvent the data hungriness of neural networks, which is a significant impediment to their application outside of the ML research community. We also show results on the applicability of this technique to real-world drawings and present research that addresses how some fundamental attributes of drawings as images affect the way they are interpreted in deep neural networks. Our primary contribution is a technique to train a neural network to segment real-world architectural drawings after using only generated pseudodrawings.
keywords	full paper, representation + perception, computation, ai & machine learning
series	ACADIA
type	paper
email
last changed	2022/06/07 07:51

_id	acadia18_146
id	acadia18_146
authors	Rossi, Gabriella; Nicholas, Paul
year	2018
title	Re/Learning the Wheel. Methods to Utilize Neural Networks as Design Tools for Doubly Curved Metal Surfaces
doi	https://doi.org/10.52842/conf.acadia.2018.146
source	ACADIA // 2018: Recalibration. On imprecisionand infidelity. [Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-17729-7] Mexico City, Mexico 18-20 October, 2018, pp. 146-155
summary	This paper introduces concepts and computational methodologies for utilizing neural networks as design tools for architecture and demonstrates their application in the making of doubly curved metal surfaces using a contemporary version of the English Wheel. The research adopts an interdisciplinary approach to develop a novel method to model complex geometric features using computational models that originate from the field of computer vision. The paper contextualizes the approach with respect to the current state of the art of the usage of artificial neural networks both in architecture and beyond. It illustrates the cyber physical system that is at the core of this research, with a focus on the employed neural network–based computational method. Finally, the paper discusses the repercussions of these design tools on the contemporary design paradigm.
keywords	full paper, ai & machine learning, digital craft, robotic production, computation
series	ACADIA
type	paper
email
last changed	2022/06/07 07:56

_id	caadria2018_322
id	caadria2018_322
authors	Lu, Hangxin, Gu, Jiaxi, Li, Jin, Lu, Yao, Müller, Johannes, Wei, Wenwen and Schmitt, Gerhard
year	2018
title	Evaluating Urban Design Ideas from Citizens from Crowdsourcing and Participatory Design
doi	https://doi.org/10.52842/conf.caadria.2018.2.297
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 297-306
summary	Participatory planning aims at engaging multiple stakeholders including citizens in various stages of planning projects. Adopting participatory design approach in the early stage of planning project facilitates the ideation process of citizens. We have implemented a participatory design study during the 2017 Beijing Design Week and have conducted an interactive design project called "Design your perfect Dashilar: You Place it!". Participants including local residents and visitors were asked to redesign the Yangmeizhu street, a historical street located in Dashilar area by rearranging the buildings of residential, commercial, administration, and cultural functionalities. Apart from using digital design tools, questionnaires, interviews, and sensor network were applied to collect personal preferences data. Computational approaches were used to extract features from designs and personal preferences. In this paper, we illustrate the implementation of the participatory design and the possible applications by combining with crowdsourcing. Participatory design data and citizens profiles with personal preferences were analysed and their correlations were computed. By using crowdsourcing and participatory design, this study shows that the digitalization of participatory design with data science perspective can indicate the implicit requirements, needs and design ideas of citizens.
keywords	Participatory design; Crowdsourcing; Human computation; Citizen Design Science; Human Computer Interaction
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	ecaade2018_292
id	ecaade2018_292
authors	Dennemark, Martin, Aicher, Andreas, Schneider, Sven and Hailu, Tesfaye
year	2018
title	Generative Hydrology Network Analysis - A parametric approach to water infrastructure based urban planning
doi	https://doi.org/10.52842/conf.ecaade.2018.2.327
source	Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 2, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 327-334
summary	Urban water systems need to be dimensioned well to be economical and distribute water in a good quality to all consumers. Their pipe sizes are dependent on demand and location of consuming nodes. Within uncertain development of cities, planning sustainable hydraulic networks is challenging. This paper explores, how the definition of urban design parameters can be supported using parametric urban design models and computational water network analysis. For the latter we developed new components for Grasshopper based on the open accessible water analysis tool EPANET. In two example cases we demonstrate potential applications of this tool for water-sensitive planning of emerging cities to find optimal positions for water sources or pipe diameters. In subsequent research, this could be used to derive probability-based recommendations for the dimensioning of a water network within uncertain growth.
keywords	water infrastructure; urban planning; parametric design; uncertainty; emerging cities
series	eCAADe
email
last changed	2022/06/07 07:55

_id	acadia18_156
id	acadia18_156
authors	Huang, Weixin; Zheng, Hao
year	2018
title	Architectural Drawings Recognition and Generation through Machine Learning
doi	https://doi.org/10.52842/conf.acadia.2018.156
source	ACADIA // 2018: Recalibration. On imprecisionand infidelity. [Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-17729-7] Mexico City, Mexico 18-20 October, 2018, pp. 156-165
summary	With the development of information technology, the ideas of programming and mass calculation were introduced into the design field, resulting in the growth of computer- aided design. With the idea of designing by data, we began to manipulate data directly, and interpret data through design works. Machine Learning as a decision making tool has been widely used in many fields. It can be used to analyze large amounts of data and predict future changes. Generative Adversarial Network (GAN) is a model framework in machine learning. It’s specially designed to learn and generate output data with similar or identical characteristics. Pix2pixHD is a modified version of GAN that learns image data in pairs and generates new images based on the input. The author applied pix2pixHD in recognizing and generating architectural drawings, marking rooms with different colors and then generating apartment plans through two convolutional neural networks. Next, in order to understand how these networks work, the author analyzed their framework, and provided an explanation of the three working principles of the networks, convolution layer, residual network layer and deconvolution layer. Lastly, in order to visualize the networks in architectural drawings, the author derived data from different layer and different training epochs, and visualized the findings as gray scale images. It was found that the features of the architectural plan drawings have been gradually learned and stored as parameters in the networks. As the networks get deeper and the training epoch increases, the features in the graph become more concise and clearer. This phenomenon may be inspiring in understanding the designing behavior of humans.
keywords	full paper, design study, generative design, ai + machine learning, ai & machine learning
series	ACADIA
type	paper
email
last changed	2022/06/07 07:49

_id	caadria2018_083
id	caadria2018_083
authors	Luo, Dan, Wang, Jinsong and Xu, Weiguo
year	2018
title	Robotic Automatic Generation of Performance Model for Non-Uniform Linear Material via Deep Learning
doi	https://doi.org/10.52842/conf.caadria.2018.1.039
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 1, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 39-48
summary	In the following research, a systematic approach is developed to generate an experiment-based performance model that computes and customizes properties of non-uniform linear materials to accommodate the form of designated curve under bending and natural force. In this case, the test subject is an elastomer strip of non-uniform sections. A novel solution is provided to obtain sufficient training data required for deep learning with an automatic material testing mechanism combining robotic arm automation and image recognition. The collected training data are fed into a deep combination of neural networks to generate a material performance model. Unlike most traditional performance models that are only able to simulate the final form from the properties and initial conditions of the given materials, the trained neural network offers a two-way performance model that is also able to compute appropriate material properties of non-uniform materials from target curves. This network achieves complex forms with minimal and effective programmed materials with complicated nonlinear properties and behaving under natural forces.
keywords	Material performance model; Deep Learning; Robotic automation; Material computation; Neural network
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	ecaade2018_298
id	ecaade2018_298
authors	Rossi, Gabriella and Nicholas, Paul
year	2018
title	Modelling A Complex Fabrication System - New design tools for doubly curved metal surfaces fabricated using the English Wheel
doi	https://doi.org/10.52842/conf.ecaade.2018.1.811
source	Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 1, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 811-820
summary	Standard industrialization and numeration models fail to translate the richness and complexity of traditional crafts into the making of the architectural elements, which excludes them from the industry. This paper introduces a new way of modelling a complex craft fabrication method, namely the English Wheel, that is based on the creation of a cyber-physical system. The cyber-physical system connects a robotic arm and an artificial neural network. The robot arm controls the movement of a metal sheet through the English wheel to achieve desired geometries according to toolpaths and predicted deformations specified by the neural network. The method is demonstrated through the making of 1:1 design probes of doubly curved metal surfaces.
keywords	Digital craft; metal forming; doubly curved surfaces; robotic fabrication; neural networks; cyber-physical system
series	eCAADe
email
last changed	2022/06/07 07:56

_id	cdrf2023_526
id	cdrf2023_526
authors	Eric Peterson, Bhavleen Kaur
year	2023
title	Printing Compound-Curved Sandwich Structures with Robotic Multi-Bias Additive Manufacturing
doi	https://doi.org/https://doi.org/10.1007/978-981-99-8405-3_44
source	Proceedings of the 2023 DigitalFUTURES The 5st International Conference on Computational Design and Robotic Fabrication (CDRF 2023)
summary	A research team at Florida International University Robotics and Digital Fabrication Lab has developed a novel method for 3d-printing curved open grid core sandwich structures using a thermoplastic extruder mounted on a robotic arm. This print-on-print additive manufacturing (AM) method relies on the 3d modeling software Rhinoceros and its parametric software plugin Grasshopper with Kuka-Parametric Robotic Control (Kuka-PRC) to convert NURBS surfaces into multi-bias additive manufacturing (MBAM) toolpaths. While several high-profile projects including the University of Stuttgart ICD/ITKE Research Pavilions 2014–15 and 2016–17, ETH-Digital Building Technologies project Levis Ergon Chair 2018, and 3D printed chair using Robotic Hybrid Manufacturing at Institute of Advanced Architecture of Catalonia (IAAC) 2019, have previously demonstrated the feasibility of 3d printing with either MBAM or sandwich structures, this method for printing Compound-Curved Sandwich Structures with Robotic MBAM combines these methods offering the possibility to significantly reduce the weight of spanning or cantilevered surfaces by incorporating the structural logic of open grid-core sandwiches with MBAM toolpath printing. Often built with fiber reinforced plastics (FRP), sandwich structures are a common solution for thin wall construction of compound curved surfaces that require a high strength-to-weight ratio with applications including aerospace, wind energy, marine, automotive, transportation infrastructure, architecture, furniture, and sports equipment manufacturing. Typical practices for producing sandwich structures are labor intensive, involving a multi-stage process including (1) the design and fabrication of a mould, (2) the application of a surface substrate such as FRP, (3) the manual application of a light-weight grid-core material, and (4) application of a second surface substrate to complete the sandwich. There are several shortcomings to this moulded manufacturing method that affect both the formal outcome and the manufacturing process: moulds are often costly and labor intensive to build, formal geometric freedom is limited by the minimum draft angles required for successful removal from the mould, and customization and refinement of product lines can be limited by the need for moulds. While the most common material for this construction method is FRP, our proof-of-concept experiments relied on low-cost thermoplastic using a specially configured pellet extruder. While the method proved feasible for small representative examples there remain significant challenges to the successful deployment of this manufacturing method at larger scales that can only be addressed with additional research. The digital workflow includes the following steps: (1) Create a 3D digital model of the base surface in Rhino, (2) Generate toolpaths for laminar printing in Grasshopper by converting surfaces into lists of oriented points, (3) Generate the structural grid-core using the same process, (4) Orient the robot to align in the direction of the substructure geometric planes, (5) Print the grid core using MBAM toolpaths, (6) Repeat step 1 and 2 for printing the outer surface with appropriate adjustments to the extruder orientation. During the design and printing process, we encountered several challenges including selecting geometry suitable for testing, extruder orientation, calibration of the hot end and extrusion/movement speeds, and deviation between the computer model and the physical object on the build platen. Physical models varied from their digital counterparts by several millimeters due to material deformation in the extrusion and cooling process. Real-time deviation verification studies will likely improve the workflow in future studies.
series	cdrf
email
last changed	2024/05/29 14:04

_id	ecaade2018_315
id	ecaade2018_315
authors	Koehler, Daniel, Abo Saleh, Sheghaf, Li, Hua, Ye, Chuwei, Zhou, Yaonaijia and Navasaityte, Rasa
year	2018
title	Mereologies - Combinatorial Design and the Description of Urban Form.
doi	https://doi.org/10.52842/conf.ecaade.2018.2.085
source	Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 2, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 85-94
summary	This paper discusses the ability to apply machine learning to the combinatorial design-assembly at the scale of a building to urban form. Connecting the historical lines of discrete automata in computer science and formal studies in architecture this research contributes to the field of additive material assemblies, aggregative architecture and their possible upscaling to urban design. The following case studies are a preparation to apply deep-learning on the computational descriptions of urban form. Departing from the game Go as a testbed for the development of deep-learning applications, an equivalent platform can be designed for architectural assembly. By this, the form of a building is defined via the overlap between separate building parts. Building on part-relations, this research uses mereology as a term for a set of recursive assembly strategies, integrated into the design aspects of the building parts. The models developed by research by design are formally described and tested under a digital simulation environment. The shown case study shows the process of how to transform geometrical elements to architectural parts based merely on their compositional aspects either in horizontal or three-dimensional arrangements.
keywords	Urban Form; Discrete Automata ; Combinatorics; Part-Relations; Mereology; Aggregative Architecture
series	eCAADe
email
last changed	2022/06/07 07:51

_id	acadia19_392
id	acadia19_392
authors	Steinfeld, Kyle
year	2019
title	GAN Loci
doi	https://doi.org/10.52842/conf.acadia.2019.392
source	ACADIA 19:UBIQUITY AND AUTONOMY [Proceedings of the 39th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-59179-7] (The University of Texas at Austin School of Architecture, Austin, Texas 21-26 October, 2019) pp. 392-403
summary	This project applies techniques in machine learning, specifically generative adversarial networks (or GANs), to produce synthetic images intended to capture the predominant visual properties of urban places. We propose that imaging cities in this manner represents the first computational approach to documenting the Genius Loci of a city (Norberg-Schulz, 1980), which is understood to include those forms, textures, colors, and qualities of light that exemplify a particular urban location and that set it apart from similar places. Presented here are methods for the collection of urban image data, for the necessary processing and formatting of this data, and for the training of two known computational statistical models (StyleGAN (Karras et al., 2018) and Pix2Pix (Isola et al., 2016)) that identify visual patterns distinct to a given site and that reproduce these patterns to generate new images. These methods have been applied to image nine distinct urban contexts across six cities in the US and Europe, the results of which are presented here. While the product of this work is not a tool for the design of cities or building forms, but rather a method for the synthetic imaging of existing places, we nevertheless seek to situate the work in terms of computer-assisted design (CAD). In this regard, the project is demonstrative of a new approach to CAD tools. In contrast with existing tools that seek to capture the explicit intention of their user (Aish, Glynn, Sheil 2017), in applying computational statistical methods to the production of images that speak to the implicit qualities that constitute a place, this project demonstrates the unique advantages offered by such methods in capturing and expressing the tacit.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:56

_id	ijac201816304
id	ijac201816304
authors	Miao, Yufan; Reinhard Koenig, Katja Knecht, Kateryna Konieva, Peter Buš and Mei-Chih Chang
year	2018
title	Computational urban design prototyping: Interactive planning synthesis methods—a case study in Cape Town
source	International Journal of Architectural Computing vol. 16 - no. 3, 212-226
summary	This article is motivated by the fact that in Cape Town, South Africa, approximately 7.5 million people live in informal settlements and focuses on potential upgrading strategies for such sites. To this end, we developed a computational method for rapid urban design prototyping. The corresponding planning tool generates urban layouts including street network, blocks, parcels and buildings based on an urban designer’s specific requirements. It can be used to scale and replicate a developed urban planning concept to fit different sites. To facilitate the layout generation process computationally, we developed a new data structure to represent street networks, land parcellation, and the relationship between the two. We also introduced a nested parcellation strategy to reduce the number of irregular shapes generated due to algorithmic limitations. Network analysis methods are applied to control the distribution of buildings in the communities so that preferred neighborhood relationships can be considered in the design process. Finally, we demonstrate how to compare designs based on various urban analysis measures and discuss the limitations that arise when we apply our method in practice, especially when dealing with more complex urban design scenarios.
keywords	Procedural modeling, spatial synthesis, generative design, urban planning
series	journal
email
last changed	2019/08/07 14:03

_id	caadria2018_243
id	caadria2018_243
authors	Yin, Shi and Xiao, Yiqiang
year	2018
title	Research on the Impact of Traditional Urban Geometry on Outdoor Thermal Environment - Case Study of Neighbourhoods with Arcade Street in South China
doi	https://doi.org/10.52842/conf.caadria.2018.2.503
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 503-512
summary	With the deterioration of urban environment gradually in these decades, the demand for improving the outdoor thermal environment is increasing. The traditional architecture and urban planning contain abundant climate responding strategy, while current studies about it are still insufficient. Furthermore, many researches had profound results on how different urban design parameters would impact outdoor thermal comfort, but only a few of them could achieve an effective transformation into a practical scenario. Thus, this paper attempts to present the impact of different traditional urban form, which is extracted from different neighborhoods with arcade street in south China, on the outdoor thermal environment, through field measurements and climatic simulation with Envi-met. Moreover, these different complex urban forms were transferred into a simplified form with uniform character and simulating based on the same boundary condition. Comparing the SVF (Sky View Factor) and PET (Physiological Equivalent Temperature) of each point, the organic urban form would lead better thermal environment than others on the main road. On the other hand, the SVF of a point is not the only one aspect of its PET, which related with the form of urban geometry as well.
keywords	Climate Responsive Urban Design; Traditional Arcade-Street Neighborhood; Urban Geometry; Outdoor Thermal Comfort
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	caadria2018_158
id	caadria2018_158
authors	Koh, Immanuel
year	2018
title	Learning Design Trends from Social Networks - Data Mining, Analysis & Visualization of Grasshopper® Online User Community
doi	https://doi.org/10.52842/conf.caadria.2018.2.277
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 277-286
summary	The paper has demonstrated that the increasingly online relationship between designers and their digital tools can be quantitatively represented, described and analyzed through the data-mining of design-domain specific and tool-based social network (i.e. Grasshopper3D). It explores design trends' correlations based on network user groups' size, users' demographics, nodes' degree centrality and discussion threads' popularity.
keywords	Social Networks; Design Trends; Big Data; Parametric Design Tools; Data Visualization
series	CAADRIA
email
last changed	2022/06/07 07:51

_id	caadria2018_293
id	caadria2018_293
authors	Lee, Jisun and Lee, Hyunsoo
year	2018
title	The Visible and Invisible Network of a Self-Organizing Town - Agent-Based Simulation for Investigating Urban Development Process
doi	https://doi.org/10.52842/conf.caadria.2018.2.411
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 411-420
summary	This study applies self-organization as a methodology to understand the complex process of city networks caused by interactions between spatial structures and individual behaviors. The agent-based simulations have been conducted to investigate the visible and invisible networks understanding the self-organized aspects of city development processes. To develop optimal future networks providing connectivity and accessibility this study investigates spatial network configurations from internal individual behavior and movement. As results, it was found that the spatial configurations of the agent movement trails match to the current district boundaries and the similar network patterns were seen in various control values of agent behavior settings. This study contributes to searching out the hierarchy of network structures which is an important factor for re-planning of the way system.
keywords	Agent-based simulation; network analysis ; self organization ; urban development process ; Physarum polycephalum
series	CAADRIA
email
last changed	2022/06/07 07:52

_id	caadria2018_018
id	caadria2018_018
authors	Lin, Yuming and Huang, Weixin
year	2018
title	Social Behavior Analysis in Innovation Incubator Based on Wi-Fi Data - A Case Study on Yan Jing Lane Community
doi	https://doi.org/10.52842/conf.caadria.2018.2.197
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 197-206
summary	Innovation incubator is an emerging kind of office space which focuses on promoting social interaction in the space. From the perspective of environmental behavior, the complex relationship between a particular space form and the social interactions is well worth exploring. Based on Wi-Fi positioning data, this paper examined the spatial and temporal behavior in innovation incubators. Using the interdisciplinary social networks analysis, this paper further analyzed the social interactions in this space, mining out social structures such as gathering and community, and analyzing the relationship between these structures and spaces. The result shows that human behavior in innovation incubators has some interesting characteristics, and the social structures are closely linked with the functional area of innovation incubator. This paper provides a new perspective and introduces interdisciplinary approaches to study the social behaviors in a particular space form, which has great potential in future research.
keywords	environmental behavior study; social behavior analysis; innovation incubator; Wi-Fi IPS; social network
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	ecaade2018_420
id	ecaade2018_420
authors	Peters, Brady, Akiyama, Mitchell, Abou Ras, Ous and Lamb, Sean
year	2018
title	Spatial Sonic Network - Designing and prototyping acoustic mirrors for communication
doi	https://doi.org/10.52842/conf.ecaade.2018.1.571
source	Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 1, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 571-580
summary	The Spatial Sonic Network is a proposal for a series of parabolic acoustic mirrors that collect, focus, and translate sound. Computational tools were used extensively throughout the project, to realize algorithmic logic, to integrate acoustic performance into the architectural design process, and to link design models to fabrication machinery. While conceptually straightforward, the design of acoustic mirrors, also known as sound mirrors, raised several challenges in terms of network design, geometry definition, acoustic performance simulation, prototyping, and measuring. The research and results that emerged from these challenges is the focus of this paper.
keywords	Architectural Acoustics; Performance Simulation; Prototyping
series	eCAADe
email
last changed	2022/06/07 08:00

_id	acadia18_108
id	acadia18_108
authors	Sanchez, Jose
year	2018
title	Platforms for Architecture: Imperatives and Opportunities of Designing Online Networks for Design
doi	https://doi.org/10.52842/conf.acadia.2018.108
source	ACADIA // 2018: Recalibration. On imprecisionand infidelity. [Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-17729-7] Mexico City, Mexico 18-20 October, 2018, pp. 108-117
summary	The rise of platforms such as Facebook, YouTube, and Uber, initially celebrated as part of a disruptive new era of the internet, has slowly been reassessed as a problematic and unregulated form of twenty-first-century info-capitalism that contributes to inequality, mistrust, and user polarization. The internet has become a place for content creation, not only consumption, and the content freely created by the network of users has defined a self-organizing system of ad-hoc audiences following echo chambers organized through artificial intelligence, which amplifies previously identified trends. While a large portion of the content created by users seems to be aimed at personal forms of entertainment, a few remarkable projects, such as Wikipedia, have allowed hundreds of users to contribute to a collective goal. While we can observe that the platform model has appeared in diverse disciplines, allowing the creation of content from news articles to music, we have not seen the emergence of a robust design platform intended to proliferate and advance the discipline of architecture. This paper makes the case that video game technology and its audiences have reached a state of technical capability that could allow for architectural platforms to emerge, one in which players could learn, create, and share architectural designs. Such a platform comes with a series of ethical imperatives, questions of value proposition, and liabilities, as well as a high potential to communicate and proliferate architectural knowledge and know-how. Common’hood, currently under development, will be used as a case study to engage the development of an ethical architectural platform that develops a proposition towards authorship, ownership, and collective engagement.
keywords	full paper, platforms, capitalism, network, video game, combinatorics, information theory, entropy, co-ops, platform cooperativism, privacy, encryption
series	ACADIA
type	paper
email
last changed	2022/06/07 07:56

_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

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