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	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
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
doi	https://doi.org/10.52842/conf.caadria.2018.2.247
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	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
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
doi	https://doi.org/10.52842/conf.caadria.2018.2.257
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_176
id	acadia18_176
authors	Bidgoli, Ardavan; Veloso,Pedro
year	2018
title	DeepCloud. The Application of a Data-driven, Generative Model in Design
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. 176-185
doi	https://doi.org/10.52842/conf.acadia.2018.176
summary	Generative systems have a significant potential to synthesize innovative design alternatives. Still, most of the common systems that have been adopted in design require the designer to explicitly define the specifications of the procedures and in some cases the design space. In contrast, a generative system could potentially learn both aspects through processing a database of existing solutions without the supervision of the designer. To explore this possibility, we review recent advancements of generative models in machine learning and current applications of learning techniques in design. Then, we describe the development of a data-driven generative system titled DeepCloud. It combines an autoencoder architecture for point clouds with a web-based interface and analog input devices to provide an intuitive experience for data-driven generation of design alternatives. We delineate the implementation of two prototypes of DeepCloud, their contributions, and potentials for generative design.
keywords	full paper, design tools software computing + gaming, ai & machine learning, generative design, autoencoders
series	ACADIA
type	paper
email
last changed	2022/06/07 07:52

_id	ecaade2018_393
id	ecaade2018_393
authors	Serrano Salazar, Salvador, Carrasco Hortal, José, Morales Menárguez, Francesc and Gutiérrez Salazar, Juan Pablo
year	2018
title	Cooperative Trees by Adding Inosculated and Discrete Definitions to a DLA Design
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. 103-112
doi	https://doi.org/10.52842/conf.ecaade.2018.2.103
summary	This paper presents a method to generate free-form branched structures from a small number of different constructive elements, based on the postulates of discrete or combinatorial design. The research is based on the study of fractal growth as a generator of complex tree-like structures, using references from other scientific approaches in which the possibilities of the DLA (diffusion-limited aggregation) model have been explored. The proposed method uses the Grasshopper visual programming language, and incorporates new topological strategies to improve the performance or robustness of the system through tree-tree (inosculation) and tree-soil (aerial roots) cooperations. The simulation demonstrates the effectiveness of the proposed method and its potential for the construction of structures with complex geometries from a discrete set of knots and bars and bioinspired strategies. The paper includes a review of the chosen design principles, the developed methodology and a recent physical test in Medellín (Colombia).
keywords	DLA, discrete design, inosculation, branching structures, virtual-real models
series	eCAADe
email
last changed	2022/06/07 07:57

_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	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	sigradi2018_1329
id	sigradi2018_1329
authors	Campos Fialho, Beatriz; A. Costa, Heliara; Logsdon, Louise; Minto Fabrício, Márcio
year	2018
title	CAD and BIM tools in Teaching of Graphic Representation for Engineering
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. 961-968
summary	BIM technology has represented an advance and a break of the design process’ paradigm, impacting both academia and construction market. Reporting a didactic experience in the Civil Engineering graduation, this article aims to understand the teaching and learning process of graphic representation, by using CAD and BIM tools. The research included Literature Review and Empirical Study, whose data collection was based on the application of questionnaires, practical exercises and theoretical test with the students. As a contribution, we highline the complementary nature of the tools and the potentialities of BIM for teaching graphic representation.
keywords	Graphic Representation; CAD System Education; CAE System Education. BIM
series	SIGRADI
email
last changed	2021/03/28 19:58

_id	sigradi2023_375
id	sigradi2023_375
authors	Consalter Diniz, Maria Luisa, Polverini Boeing, Lais, dos Santos Carvalho, Wendel and Bertola Duarte, Rovenir
year	2023
title	Natural Language Processing, Sentiment Analysis, and Urban Studies: A Systematic Review
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 1761–1772
summary	This paper discusses the potential of using data from social media and location data platforms to create cartographies that enhance our understanding of urban dynamics. Natural Language Processing (NLP) and sentiment analysis are highlighted as essential tools for comprehending and categorizing this data. The study conducted a systematic review of NLP and sentiment analysis applications in urban studies, covering 27 peer-reviewed journals and conference papers published between 2018 and 2023. The research classified applications into six categories: urban livability, governance and management, user and landscape perception, land use and zoning, public health, and transportation and mobility. Most studies primarily relied on data from social media platforms like Twitter and location data sources such as Google Maps and Trip Advisor. Challenges include dealing with irrelevant or misleading information in publicly available data and limited accuracy when analyzing sentiments of non-English-speaking populations.
keywords	Natural language processing, Sentiment analysis, Urban studies, Digital cartographies, Systematic review.
series	SIGraDi
email
last changed	2024/03/08 14:09

_id	ecaade2018_399
id	ecaade2018_399
authors	Cutellic, Pierre
year	2018
title	UCHRON - An Event-Based Generative Design Software Implementing Fast Discriminative Cognitive Responses from Visual ERP BCI
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. 131-138
doi	https://doi.org/10.52842/conf.ecaade.2018.2.131
summary	This research aims at investigating BCI technologies in the broad scope of CAAD applications exploiting early visual cognition in computational design. More precisely, this paper will describe the investigation of key BCI and ML components for the implementation and development of a software supporting this research : Uchron. It will be organised as follows. Firstly, it will introduce the pursued interest and contribution that visual-ERP EEG based BCI application for Generative Design may provide through a synthetic review of precedents and BCI technology. Secondly, selected BCI components will be described and a methodology will be presented to provide an appropriate framework for a CAAD software approach. This section main focus is on the processing component of the BCI. It distinguishes two key aspects of discrimination and generation in its design and proposes a new model based on GAN for modulated adversarial design. Emphasis will be made on the explicit use of inference loops integrating fast human cognitive responses and its individual capitalisation through time in order to reflect towards the generation of design and architectural features.
keywords	Human Computer Interaction; Neurodesign; Generative Design; Design Computing and Cognition; Machine Learning
series	eCAADe
email
last changed	2022/06/07 07:56

_id	ecaade2018_392
id	ecaade2018_392
authors	Gargaro, Silvia, Cigola, Michela, Gallozzi, Arturo and Fioravanti, Antonio
year	2018
title	Cultural Heritage Knowledge Context - A model based on Collaborative Cultural approach
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. 205-214
doi	https://doi.org/10.52842/conf.ecaade.2018.2.205
summary	Cultural Heritage is a wide concept. It's what remains of the past generations Cultural Heritage includes tangible culture (such as buildings, monuments, landscapes, books, works of art and artifacts), intangible culture (such as folklore, music, traditions, language and knowledge) and natural heritage (including culturally significant landscapes, and biodiversity). A good preservation, restauration and valorization of Cultural Heritage embraces tangible and intangible culture, actually not evaluated in an holistic way.Cultural Heritage is not only an historical memory of the past, but the mirror of an anthropological reality that characterizes our personal and collective identity within a cultural context. The question is: How can we take into account these thought categories? The model proposed would be an used methodology to analyze the model for data acquisition, processing, modeling and implementation of knowledge on culture and social context through ontologies. The purpose of the research is to analyze the relationship between Cultural Context and Cultural Heritage.The contribution proposes an original approach to Cultural Heritage based on a social and cultural approach, transforming the user as an actor for the acquisition of raw data and cultural knowledge, applying the model to the Archaeological Complex of Casinum, in South Latium.
keywords	Cultural Heritage; Context Knowledge; Intangible Knowledge; Ontologies; Human Behavior Constraints
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
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
doi	https://doi.org/10.52842/conf.acadia.2018.166
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	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
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
doi	https://doi.org/10.52842/conf.caadria.2018.1.039
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	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
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
doi	https://doi.org/10.52842/conf.acadia.2018.186
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_245
id	caadria2018_245
authors	Chowdhury, Shuva and Schnabel, Marc Aurel
year	2018
title	An Algorithmic Methodology to Predict Urban Form - An Instrument for Urban Design
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. 401-410
doi	https://doi.org/10.52842/conf.caadria.2018.2.401
summary	We question the recent practices of conventional and participatory urban design approaches and offer a middle approach by exploring computational design tools in the design system. On the one hand, the top-down urban planning approaches investigate urban form as a holistic matter which only can be calibrated by urban professionals. These approaches are not able to offer enough information to the end users to predict the urban form. On the other hand, the bottom-up urban design approaches cannot visualise predicted urban scenarios, and most often the design decisions stay as general assumptions. We developed and tested a parametric design platform combines both approaches where all the stakeholders can participate and visualise multiple urban scenarios in real-time feedback. Parametric design along with CIM modelling system has influenced urban designers for a new endeavour in urban design. This paper presents a methodology to generate and visualise urban form. We present a novel decision-making platform that combines city level and local neighbourhood data to aid participatory urban design decisions. The platform allows for stakeholder collaboration and engagement in complex urban design processes.
keywords	knowledge-based system; algorithmic methodology ; design decision tool; urban form;
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	ecaade2018_295
id	ecaade2018_295
authors	Dezen-Kempter, Eloisa, Cogima, Camila Kimi, Vieira de Paiva, Pedro Victor and Garcia de Carvalho, Marco Antonio
year	2018
title	BIM for Heritage Documentation - An ontology-based approach
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. 213-222
doi	https://doi.org/10.52842/conf.ecaade.2018.1.213
summary	In the recent decades, the high-resolution remote sensing, through 3D laser scanning and photogrammetry benefited historic buildings maintenance, conservation, and restoration works. However, the dense surface models (DSM) generated from the data capture have nonstructured features as lack of topology and semantic discretization. The process to create a semantically oriented 3D model from the DSM, using the of Building Information Model technology, is a possibility to integrate historical information about the life cycle of the building to maintain and improving architectural valued building stock to its functional level and safeguarding its outstanding historical value. Our approach relies on an ontology-based system to represent the knowledge related to the building. Our work outlines a model-driven approach based on the hybrid data acquisition, its post-processing, the identification of the building' main features for the parametric modeling, and the development of an ontological map integrated with the BIM model. The methodology proposed was applied to a large-scale industrial historical building, located in Brazil. The DSM were compared, providing a qualitative assessment of the proposed method.
keywords	Reality-based Surveying; Ontology-based System; BIM; Built heritage management
series	eCAADe
email
last changed	2022/06/07 07:55

_id	caadria2018_057
id	caadria2018_057
authors	Nandavar, Anirudh, Petzold, Frank, Nassif, Jimmy and Schubert, Gerhard
year	2018
title	Interactive Virtual Reality Tool for BIM Based on IFC - Development of OpenBIM and Game Engine Based Layout Planning Tool - A Novel Concept to Integrate BIM and VR with Bi-Directional Data Exchange
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. 453-462
doi	https://doi.org/10.52842/conf.caadria.2018.1.453
summary	With recent advancements in VR (Virtual Reality) technology in the past year, it has emerged as a new paradigm in visualization and immersive HMI (Human-machine Interface). On the other hand, in the past decades, BIM (Building Information Modelling) has emerged as the new standard of implementing construction projects and is quickly becoming a norm than just a co-ordination tool in the AEC industry.Visualization of the digital data in BIM plays an important role as it is the primary communication medium to the project participants, where VR can offer a new dimension of experiencing BIM and improving the collaboration of various stakeholders of a project. There are both open source and commercial solutions to extend visualization of a BIM project in VR, but so far, there are no complete solutions that offer a pure IFC format based solution, which makes the VR integration vendor neutral. This work endeavors to develop a concept for a vendor-neutral BIM-VR integration with bi-directional data exchange in order to extend VR as a collaboration tool than a mere visualization tool in the BIM ecosystem.
keywords	BIM; VR; IFC; Unity; BIM-VR integration; HMI
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	ijac201816101
id	ijac201816101
authors	Nisztu, Maciejk and Pawe³ B. Myszkowsk
year	2018
title	Usability of contemporary tools for the computational design of architectural objects: Review, features evaluation and reflection
source	International Journal of Architectural Computing vol. 16 - no. 1, 58-84
summary	This article is an overview focused on functionality and usability of selected contemporary approaches for the computational floor plan generation of architectural objects. This article describes current solutions for generative architectural design and focuses on their usability from the point of view of architectural design practice. Recent research papers and prototypes, as well as the most important tools (selected computer-aided design and BIM software) for generative design from the architectural perspective, are described. The functionalities and level of usability of present-day software and prototypes are described. In addition, the descriptive review of the research prototypes architectural design outcomes is present. Furthermore, the survey among active architects regarding the usage of computational tools in the professional practice and possible guidelines for the development of such tools are present. This article summarises with the conclusion about the current state of generative floor plan design tools, the lack of fully functional and developed commercial tools of this type on the market and future directions for the development of generative floor plans tools.
keywords	Architectural design, case studies, computer-aided architectural design, optimisation in computer-aided architectural design, computer-aided architectural design applications
series	journal
email
last changed	2019/08/07 14:03

_id	ijac202018202
id	ijac202018202
authors	Pasquero, Claudia and Marco Poletto
year	2020
title	Bio-digital aesthetics as value system of post-Anthropocene architecture
source	International Journal of Architectural Computing vol. 18 - no. 2, 120-140
summary	It is timely within the Anthropocene era, more than ever before, to search for a non-anthropocentric mode of reasoning, and consequently designing. The PhotoSynthetica Consortium, established in 2018 and including London-based ecoLogicStudio, the Urban Morphogenesis Lab (Bartlett School of Architecture, University College London) and the Synthetic Landscape Lab (University of Innsbruck, Austria), has therefore been pursuing architecture as a research-based practice, exploring the interdependence of digital and biological intelligence in design by working directly with non-human living organisms. The research focuses on the diagrammatic capacity of these organisms in the process of growing and becoming part of complex bio-digital architectures. A key remit is training architects’ sensibility at recognising patterns of reasoning across disciplines, materialities and technological regimes, thus expanding the practice’s repertoire of aesthetic qualities. Recent developments in evolutionary psychology demonstrate that the human sense of beauty and pleasure is part of a co-evolutionary system of mind and surrounding environment. In these terms, human senses of beauty and pleasure have evolved as selection mechanisms. Cultivating and enhancing them compensate and integrate the functions of logical thinking to gain a systemic view on the planet Earth and the dramatic changes it is currently undergoing. This article seeks to illustrate, through a series of recent research projects, how a renewed appreciation of beauty in architecture has evolved into an operational tool to design and measure its actual ecological intelligence.
keywords	Bio-digital, bio-computation, bio-city, effectiveness, empathy, impact, sensing
series	journal
email
last changed	2020/11/02 13:34

_id	sigradi2018_1724
id	sigradi2018_1724
authors	Ramos Pacheco, Paula; Sperling, David M.
year	2018
title	From DiY to DiWO: from Crafting to Digital Collaboration
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. 1313-1320
summary	Cultural changes based on recent development of information technologies suggests that knowledge could be spread with less control and greater accessibility, allowing the emergence of communities that launch alternatives like, for example, networks of laboratories for manufacturing. However, similar ambitions regarding the creation of alternatives to industrial production can be identified in the countercultural context of the 1960s and 1970s. This article traces some comparisons between these two historical moments with the goal of investigate how do-it-yourself (DiY) appears (again) in the design scene today as do-it-with-others (DiWO), establishing approximations and distances between two selected objects of study.
keywords	Open Design; Collaboration; Do-it-yourself; Do-it-with-others; Maker Movement
series	SIGRADI
email
last changed	2021/03/28 19:59

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