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	ijac201816407
id	ijac201816407
authors	Mahankali, Ranjeeth; Brian R. Johnson and Alex T. Anderson
year	2018
title	Deep learning in design workflows: The elusive design pixel
source	International Journal of Architectural Computing vol. 16 - no. 4, 328-340
summary	The recent wave of developments and research in the field of deep learning and artificial intelligence is causing the border between the intuitive and deterministic domains to be redrawn, especially in computer vision and natural language processing. As designers frequently invoke vision and language in the context of design, this article takes a step back to ask if deep learning’s capabilities might be applied to design workflows, especially in architecture. In addition to addressing this general question, the article discusses one of several prototypes, BIMToVec, developed to examine the use of deep learning in design. It employs techniques like those used in natural language processing to interpret building information models. The article also proposes a homogeneous data format, provisionally called a design pixel, which can store design information as spatial-semantic maps. This would make designers’ intuitive thoughts more accessible to deep learning algorithms while also allowing designers to communicate abstractly with design software.
keywords	Associative logic, creative processes, deep learning, embedding vectors, BIMToVec, homogeneous design data format, design pixel, idea persistence
series	journal
email
last changed	2019/08/07 14:04

_id	ijac201816406
id	ijac201816406
authors	As, Imdat; Siddharth Pal and Prithwish Basu
year	2018
title	Artificial intelligence in architecture: Generating conceptual design via deep learning
source	International Journal of Architectural Computing vol. 16 - no. 4, 306-327
summary	Artificial intelligence, and in particular machine learning, is a fast-emerging field. Research on artificial intelligence focuses mainly on image-, text- and voice-based applications, leading to breakthrough developments in self-driving cars, voice recognition algorithms and recommendation systems. In this article, we present the research of an alternative graph- based machine learning system that deals with three-dimensional space, which is more structured and combinatorial than images, text or voice. Specifically, we present a function-driven deep learning approach to generate conceptual design. We trained and used deep neural networks to evaluate existing designs encoded as graphs, extract significant building blocks as subgraphs and merge them into new compositions. Finally, we explored the application of generative adversarial networks to generate entirely new and unique designs.
keywords	Architectural design, conceptual design, deep learning, artificial intelligence, generative design
series	journal
email
last changed	2019/08/07 14:04

_id	acadia20_382
id	acadia20_382
authors	Hosmer, Tyson; Tigas, Panagiotis; Reeves, David; He, Ziming
year	2020
title	Spatial Assembly with Self-Play Reinforcement Learning
doi	https://doi.org/10.52842/conf.acadia.2020.1.382
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. 382-393.
summary	We present a framework to generate intelligent spatial assemblies from sets of digitally encoded spatial parts designed by the architect with embedded principles of prefabrication, assembly awareness, and reconfigurability. The methodology includes a bespoke constraint-solving algorithm for autonomously assembling 3D geometries into larger spatial compositions for the built environment. A series of graph-based analysis methods are applied to each assembly to extract performance metrics related to architectural space-making goals, including structural stability, material density, spatial segmentation, connectivity, and spatial distribution. Together with the constraint-based assembly algorithm and analysis methods, we have integrated a novel application of deep reinforcement (RL) learning for training the models to improve at matching the multiperformance goals established by the user through self-play. RL is applied to improve the selection and sequencing of parts while considering local and global objectives. The user’s design intent is embedded through the design of partial units of 3D space with embedded fabrication principles and their relational constraints over how they connect to each other and the quantifiable goals to drive the distribution of effective features. The methodology has been developed over three years through three case study projects called ArchiGo (2017–2018), NoMAS (2018–2019), and IRSILA (2019-2020). Each demonstrates the potential for buildings with reconfigurable and adaptive life cycles.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2018_126
id	caadria2018_126
authors	Khean, Nariddh, Kim, Lucas, Martinez, Jorge, Doherty, Ben, Fabbri, Alessandra, Gardner, Nicole and Haeusler, M. Hank
year	2018
title	The Introspection of Deep Neural Networks - Towards Illuminating the Black Box - Training Architects Machine Learning via Grasshopper Definitions
doi	https://doi.org/10.52842/conf.caadria.2018.2.237
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. 237-246
summary	Machine learning is yet to make a significant impact in the field of architecture and design. However, with the combination of artificial neural networks, a biologically inspired machine learning paradigm, and deep learning, a hierarchical subsystem of machine learning, the predictive capabilities of machine learning processes could prove a valuable tool for designers. Yet, the inherent knowledge gap between the fields of architecture and computer science has meant the complexity of machine learning, and thus its potential value and applications in the design of the built environment remain little understood. To bridge this knowledge gap, this paper describes the development of a learning tool directed at architects and designers to better understand the inner workings of machine learning. Within the parametric modelling environment of Grasshopper, this research develops a framework to express the mathematic and programmatic operations of neural networks in a visual scripting language. This offers a way to segment and parametrise each neural network operation into a basic expression. Unpacking the complexities of machine learning in an intermediary software environment such as Grasshopper intends to foster the broader adoption of artificial intelligence in architecture.
keywords	machine learning; neural network; action research; supervised learning; education
series	CAADRIA
email
last changed	2022/06/07 07:52

_id	caadria2018_314
id	caadria2018_314
authors	Kim, Jin Sung, Song, Jae Yeol and Lee, Jin Kook
year	2018
title	Approach to the Extraction of Design Features of Interior Design Elements Using Image Recognition Technique
doi	https://doi.org/10.52842/conf.caadria.2018.2.287
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. 287-296
summary	This paper aims to propose deep learning-based approach to the auto-recognition of their design features of interior design elements using given digital images. The recently image recognition technique using convolutional neural networks has shown great success in the various field of research and industry. The open-source frameworks and pre-trained image recognition models supporting image recognition task enable us to easily retrain the models to apply them on any domain. This paper describes how to apply such techniques on interior design process and depicts some demonstration results in that approaches. Furniture that is one of the most common interior design elements has sub-feature including implicit design features, such as style, shape, function as well as explicit properties, such as component, materials, and size. This paper shows to retrain the model to extract some of the features for efficiently managing and utilizing such design information. The target element is chair and the target design features are limited to functional features, materials, seating capacity and design style. Total 3933 chair images dataset and 6 retrained image recognition models were utilized for retraining. Through the combination of those multiple models, inference demonstration also has been described.
keywords	Deep learning; Image recognition; Interior design elements; Design feature; Chair
series	CAADRIA
email
last changed	2022/06/07 07:52

_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	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	ecaade2018_w12
id	ecaade2018_w12
authors	Rahbar, Morteza
year	2018
title	Application of Artificial Intelligence in Architectural Generative Design
doi	https://doi.org/10.52842/conf.ecaade.2018.1.071
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. 71-72
summary	In this workshop, data-driven models will be discussed and how they could change the way architects think, design and analyse. Both supervised and unsupervised learning models will be discussed and different projects will be referred as examples. Deep learning models are the third part of the workshop and more specifically, Generative Adversarial Networks will be mentioned in more detail. The GAN's open a new field of generative models in design which is based on data-driven process and we will go into detail with GANs, their branches and how we could test a sample architecture generative problem with GANs.
keywords	Artificial Intelligence; Machine Learning; Generative Design; Knowledge based Design; GAN
series	eCAADe
email
last changed	2022/06/07 08:00

_id	caadria2018_303
id	caadria2018_303
authors	Song, Jae Yeol, Kim, Jin Sung, Kim, Hayan, Choi, Jungsik and Lee, Jin Kook
year	2018
title	Approach to Capturing Design Requirements from the Existing Architectural Documents Using Natural Language Processing Technique
doi	https://doi.org/10.52842/conf.caadria.2018.2.247
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. 247-254
summary	This paper describes an approach to utilizing natural language processing (NLP) to capture design requirements from the natural language-based architectural documents. In various design stage of the architectural process, there are several different kinds of documents describing requirements for buildings. Capturing the design requirements from those documents is based on extracting information of objects, their properties, and relations. Until recently, interpreting and extracting that information from documents are almost done by a manual process. To intelligently automate the conventional process, the computer has to understand the semantics of natural languages. In this regards, this paper suggests an approach to utilizing NLP for semantic analysis which enables the computer to understand the semantics of the given text data. The proposed approach has following steps: 1) extract noun words which mostly represent objects and property data in Korean Building Act; 2) analyze the semantic relations between words, using NLP and deep learning; 3) Based on domain database, translate the noun words in objects and properties data and find out their relations.
keywords	NLP (Natural Language Processing); Deep learning; Design requirements; Korean Building Act; Semantic analysis
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	acadia18_186
id	acadia18_186
authors	Yin, Hao; Guo, Zhe; Zhao, Yao; Yuan, Philip F.
year	2018
title	Behavior Visualization System Based on UWB Positioning Technology
doi	https://doi.org/10.52842/conf.acadia.2018.186
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. 186-195
summary	This paper takes behavioral performance as a starting point and uses ultra-wideband (UWB) positioning technology and visualization methods to accurately collect and present in-place behavioral data so as to explore the behavioral characteristics of space users. In this process, we learned the observation, quantification, and presentation of behavioral data from the evolution of behavioral research. Secondly, after a comparative analysis of four types of indoor positioning technologies, we selected UWB-positioning technology and the JavaScript programming language as the development tools for a behavior visualization system. Next, we independently developed the behavior visualization system, which required a deep understanding of the working principle of UWB technology and the visualization method of the JavaScript programming language. Finally, the system was applied to an actual space, collecting and presenting users’ behavioral characteristics and habits in order to verify the applicability of the system in the field of behavioral research.
keywords	full paper, design tools, ai + machine learning, big data, behavioral performance + simulation
series	ACADIA
type	paper
email
last changed	2022/06/07 07:57

_id	caadria2018_278
id	caadria2018_278
authors	Caetano, In?s, Ilunga, Guilherme, Belém, Catarina, Aguiar, Rita, Feist, Sofia, Bastos, Francisco and Leit?o, António
year	2018
title	Case Studies on the Integration of Algorithmic Design Processes in Traditional Design Workflows
doi	https://doi.org/10.52842/conf.caadria.2018.1.111
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. 111-120
summary	Algorithmic design processes have enormous potential for architecture. Even though some large design offices have already incorporated such processes in their workflow, so far, these have not been seriously considered by the large majority of traditional small-scale studios. Nevertheless, as the integration of algorithmic techniques inside architectural studios does not require mastering programming skills, but rather taking advantage of a collaborative design process, small design studios are therefore able of using such strategies within their workflow. This paper discusses a series of challenges presented by one of these studios, where we had to integrate algorithmic design processes with the studio's traditional workflow.
keywords	Collaborative design; Algorithmic design; Design strategies; Design workflow processes
series	CAADRIA
email
last changed	2022/06/07 07:54

_id	ecaaderis2018_118
id	ecaaderis2018_118
authors	Symeonidou, Ioanna
year	2018
title	Revisiting ancient Greek technology with digital media
source	Odysseas Kontovourkis (ed.), Sustainable Computational Workflows [6th eCAADe Regional International Workshop Proceedings / ISBN 9789491207143], Department of Architecture, University of Cyprus, Nicosia, Cyprus, 24-25 May 2018, pp. 21-26
keywords	This paper presents design research on ancient Greek technological achievements and their reinterpretation in the contemporary world. The research involved the study of ancient greek technology and its respective digital representation and led to a semester-long design studio within the product design curriculum at the International Hellenic University. The students would study the morphology of ancient Greek technological devices through their 3D reconstruction and prototyping. The paper will present the results of the aforementioned design studio, commenting on the methodology, the learning theories adopted and the learning outcomes.
series	eCAADe
email
last changed	2018/05/29 14:33

_id	caadria2018_000
id	caadria2018_000
authors	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.)
year	2018
title	CAADRIA 2018: Learning, Prototyping and Adapting, Volume 1
doi	https://doi.org/10.52842/conf.caadria.2018.1
source	Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 1, Tsinghua University, Beijing, China, 17-19 May 2018, 578 p.
summary	Rapidly evolving technologies are increasingly shaping our societies as well as our understanding of the discipline of architecture. Computational developments in fields such as machine learning and data mining enable the creation of learning networks that involve architects alongside algorithms in developing new understanding. Such networks are increasingly able to observe current social conditions, plan, decide, act on changing scenarios, learn from the consequences of their actions, and recognize patterns out of complex activity networks. While digital technologies have already enabled architecture to transcend static physical boxes, new challenges of the present and visions for the future continue to call for both innovative responses integrating emerging technologies into experimental architectural practice and their critical reflection. In this process, the capability of adapting to complex social and environmental challenges through learning, prototyping and verifying solution proposals in the context of rapidly shifting realities has become a core challenge to the architecture discipline. Supported by advancing technologies, architects and researchers are creating new frameworks for digital workflows that engage with new challenges in a variety of ways. Learning networks that recognize patterns from massive data, rapid prototyping systems that flexibly iterate innovative physical solutions, and adaptive design methods all contribute to a flexible and networked digital architecture that is able to learn from both past and present to evolve towards a promising vision of the future.
series	CAADRIA
last changed	2022/06/07 07:49

_id	caadria2018_001
id	caadria2018_001
authors	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.)
year	2018
title	CAADRIA 2018: Learning, Prototyping and Adapting, Volume 2
doi	https://doi.org/10.52842/conf.caadria.2018.2
source	Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, 610 p.
summary	Rapidly evolving technologies are increasingly shaping our societies as well as our understanding of the discipline of architecture. Computational developments in fields such as machine learning and data mining enable the creation of learning networks that involve architects alongside algorithms in developing new understanding. Such networks are increasingly able to observe current social conditions, plan, decide, act on changing scenarios, learn from the consequences of their actions, and recognize patterns out of complex activity networks. While digital technologies have already enabled architecture to transcend static physical boxes, new challenges of the present and visions for the future continue to call for both innovative responses integrating emerging technologies into experimental architectural practice and their critical reflection. In this process, the capability of adapting to complex social and environmental challenges through learning, prototyping and verifying solution proposals in the context of rapidly shifting realities has become a core challenge to the architecture discipline. Supported by advancing technologies, architects and researchers are creating new frameworks for digital workflows that engage with new challenges in a variety of ways. Learning networks that recognize patterns from massive data, rapid prototyping systems that flexibly iterate innovative physical solutions, and adaptive design methods all contribute to a flexible and networked digital architecture that is able to learn from both past and present to evolve towards a promising vision of the future.
series	CAADRIA
last changed	2022/06/07 07:49

_id	ijac201816202
id	ijac201816202
authors	Tamke, Martin; Paul Nicholas and Mateusz Zwierzycki
year	2018
title	Machine learning for architectural design: Practices and infrastructure
source	International Journal of Architectural Computing vol. 16 - no. 2, 123-143
summary	In this article, we propose that new architectural design practices might be based on machine learning approaches to better leverage data-rich environments and workflows. Through reference to recent architectural research, we describe how the application of machine learning can occur throughout the design and fabrication process, to develop varied relations between design, performance and learning. The impact of machine learning on architectural practices with performance-based design and fabrication is assessed in two cases by the authors. We then summarise what we perceive as current limits to a more widespread application and conclude by providing an outlook and direction for future research for machine learning in architectural design practice.
keywords	Machine learning, robotic fabrication, design-integrated simulation, material behaviour, feedback, Complex Modelling
series	journal
email
last changed	2019/08/07 14:03

_id	acadia23_v3_71
id	acadia23_v3_71
authors	Vassigh, Shahin; Bogosian, Biayna
year	2023
title	Envisioning an Open Knowledge Network (OKN) for AEC Roboticists
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 3: Proceedings of the 43rd Annual Conference for the Association for Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9891764-1-0]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 24-32.
summary	The construction industry faces numerous challenges related to productivity, sustainability, and meeting global demands (Hatoum and Nassereddine 2020; Carra et al. 2018; Barbosa, Woetzel, and Mischke 2017; Bock 2015; Linner 2013). In response, the automation of design and construction has emerged as a promising solution. In the past three decades, researchers and innovators in the Architecture, Engineering, and Construction (AEC) fields have made significant strides in automating various aspects of building construction, utilizing computational design and robotic fabrication processes (Dubor et al. 2019). However, synthesizing innovation in automation encounters several obstacles. First, there is a lack of an established venue for information sharing, making it difficult to build upon the knowledge of peers. First, the absence of a well-established platform for information sharing hinders the ability to effectively capitalize on the knowledge of peers. Consequently, much of the research remains isolated, impeding the rapid dissemination of knowledge within the field (Mahbub 2015). Second, the absence of a standardized and unified process for automating design and construction leads to the individual development of standards, workflows, and terminologies. This lack of standardization presents a significant obstacle to research and learning within the field. Lastly, insufficient training materials hinder the acquisition of skills necessary to effectively utilize automation. Traditional in-person robotics training is resource-intensive, expensive, and designed for specific platforms (Peterson et al. 2021; Thomas 2013).
series	ACADIA
type	field note
email
last changed	2024/04/17 13:59

_id	acadia18_216
id	acadia18_216
authors	Ahrens, Chandler; Chamberlain, Roger; Mitchell, Scott; Barnstorff, Adam
year	2018
title	Catoptric Surface
doi	https://doi.org/10.52842/conf.acadia.2018.216
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. 216-225
summary	The Catoptric Surface research project explores methods of reflecting daylight through a building envelope to form an image-based pattern of light on the interior environment. This research investigates the generation of atmospheric effects from daylighting projected onto architectural surfaces within a built environment in an attempt to amplify or reduce spatial perception. The mapping of variable organizations of light onto existing or new surfaces creates a condition where the perception of space does not rely on form alone. This condition creates a visual effect of a formless atmosphere and affects the way people use the space. Often the desired quantity and quality of daylight varies due to factors such as physiological differences due to age or the types of tasks people perform (Lechner 2009). Yet the dominant mode of thought toward the use of daylighting tends to promote a homogeneous environment, in that the resulting lighting level is the same throughout a space. This research project questions the desire for uniform lighting levels in favor of variegated and heterogeneous conditions. The main objective of this research is the production of a unique facade system that is capable of dynamically redirecting daylight to key locations deep within a building. Mirrors in a vertical array are individually adjusted via stepper motors in order to reflect more or less intense daylight into the interior space according to sun position and an image-based map. The image-based approach provides a way to specifically target lighting conditions, atmospheric effects, and the perception of space.
keywords	full paper, non-production robotics, representation + perception, performance + simulation, building technologies
series	ACADIA
type	paper
email
last changed	2022/06/07 07:54

_id	ijac201816103
id	ijac201816103
authors	Alani, Mostafa W.
year	2018
title	Algorithmic investigation of the actual and virtual design space of historic hexagonal-based Islamic patterns
source	International Journal of Architectural Computing vol. 16 - no. 1, 34-57
summary	This research challenges the long-standing paradigm that considers compositional analysis to be the key to researching historical Islamic geometric patterns. Adopting a mathematical description shows that the historical focus on existing forms has left the relevant structural similarities between historical Islamic geometric patterns understudied. The research focused on the hexagonal-based Islamic geometric patterns and found that historical designs correlate to each other beyond just the formal dimension and that deep, morphological connections exist in the structures of historical singularities. Using historical evidence, this article identifies these connections and presents a categorization system that groups designs together based on their “morphogenetic” characteristics.
keywords	Islamic geometric patterns, morphology, computations, digital design, algorithmic thinking
series	journal
email
last changed	2019/08/07 14:03

_id	acadia18_386
id	acadia18_386
authors	Chen, Canhui; Burry, Jane
year	2018
title	(Re)calibrating Construction Simplicity and Design Complexity
doi	https://doi.org/10.52842/conf.acadia.2018.386
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. 386-393
summary	Construction simplicity is crucial to cost control, however design complexity is often necessary in order to meet particular spatial performance criteria. This paper presents a case study of a semi-enclosed meeting pod that has a brief that must contend with the seemingly contradictory conditions of the necessary geometric complexities imperative to improved acoustic performance and cost control in construction. A series of deep oculi are introduced as architectural elements to link the pod interior to the outside environment. Their reveals also introduce sound reflection and scattering, which contribute to the main acoustic goal of improved speech privacy. Represented as a three-dimensional funnel like shape, the reveal to each opening is unique in size, depth and angle. Traditionally, the manufacturing of such bespoke architectural elements in many cases resulted in lengthy and costly manufacturing processes. This paper investigates how the complex oculi shape variations can be manufactured using one universal mold. A workflow using mathematical and computational operations, a standardized fabrication approach and customization through tooling results in a high precision digital process to create particular calculated geometries, recalibrated at each stage to account for the paradoxical inexactitudes and inevitable tolerances.
keywords	work in progress,tolerance, developable surface, form finding, construction simplicity, material behavior
series	ACADIA
type	paper
email
last changed	2022/06/07 07:55

_id	sigradi2018_1619
id	sigradi2018_1619
authors	Agirbas, Asli
year	2018
title	Creating Non-standard Spaces via 3D Modeling and Simulation: A Case Study
source	SIGraDi 2018 [Proceedings of the 22nd Conference of the Iberoamerican Society of Digital Graphics - ISSN: 2318-6968] Brazil, São Carlos 7 - 9 November 2018, pp. 1051-1058
summary	Especially in the film industry, architectural spaces away from Euclidean geometry are brought to foreground. The best environment in which such spaces can be designed, is undoubtedly the 3D modeling environment. In this study, an experimental study was carried out on the creation of alternative spaces with undergraduate architectural students. Via using 3D modeling and various simulation techniques in the Maya software, students created spaces, which were away from the traditional architectural spaces. Thus, in addition to learning the 3D modeling software, architectural students learned to use animation and simulation as a part of design, not just as a presentation tool, and opening up new horizons for non-standard spaces was provided.
keywords	3D Modeling; Simulation; Animation; CAAD; Maya; Non-standard spaces
series	SIGRADI
email
last changed	2021/03/28 19:58

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