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	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	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	ecaade2018_303
id	ecaade2018_303
authors	Werner, Liss C.
year	2018
title	Biological Computation of Physarum - From DLA to spatial adaptive Voronoi
doi	https://doi.org/10.52842/conf.ecaade.2018.2.531
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. 531-536
summary	Physarum polycephalum, also called slime mold or myxamoeba, has started attracting the attention of those architects, urban designers, and scholars, who work in experimental trans- and flexi-disciplines between architecture, computer sciences, biology, art, cognitive sciences or soft matter; disciplines that build on cybernetic principles. Slime mold is regarded as a bio-computer with intelligence embedded in its physical mechanisms. In its plasmodium stage, the single cell organism shows geometric, morphological and cognitive principles potentially relevant for future complexity in human-machines-networks (HMN) in architecture and urban design. The parametric bio-blob presents itself as a geometrically regulated graph structure-morphologically adaptive, logistically smart. It indicates cognitive goal-driven navigation and the ability to externally memorize (like ants). Physarum communicates with its environment. The paper introduces physarum polycephalum in the context of 'digital architecture' as a biological computer for self-organizing 2D- to 4D-geometry generation.
keywords	generative geometry; bio-computation; Voronoi
series	eCAADe
email
last changed	2022/06/07 07:57

_id	acadia22pr_124
id	acadia22pr_124
authors	Ago, Viola; Tursack, Hans
year	2022
title	Understorey - A Pavilion in Parts
source	ACADIA 2022: Hybrids and Haecceities [Projects Catalog of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-7-4]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 124-129.
summary	In the summer of 2018, our collaboration was awarded a University Design Fellowship from the Exhibit Columbus organization to design, fabricate, and build a large pavilion in Columbus, Indiana as part of a biannual contemporary architecture exhibition. Our proposal for the competition was a pavilion that would double as an ecological education center. Our inspiration for this program was triggered in part by our reading of Jane Bennett’s materialist philosophy outlined in her book Vibrant Matter (2009). Through Bennett’s lens, our design rendered our site’s context as an animate field, replete with pre-existing material composites that we wanted to celebrate through a series of displays, information boards, and artificial lighting. In this, the installation would feature samples of local plants, minerals, and rocks, indigenous to Southern Indiana.
series	ACADIA
type	project
email
last changed	2024/02/06 14:06

_id	ecaade2018_389
id	ecaade2018_389
authors	Algeciras-Rodriguez, Jose
year	2018
title	Stochastic Hybrids - From references to design options through Self-Organizing Maps methodology.
doi	https://doi.org/10.52842/conf.ecaade.2018.1.119
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. 119-128
summary	This ongoing research aims to define a general assisted design method to offer non-trivial design options, where form is produced by merging characteristics from initial reference samples collection that serves as an input set. This project explores design processes laying on the use of non-linear procedures and experiments with Self-Organizing Map (SOM), as neural networks algorithms, to generate geometries. All processes are applied to a set of models representing classic sculpture, whose characteristics are encoded by the SOM process. The result of it is a set of new geometry resembling characteristics from the original references. This method produces hybrid forms that acquire characteristics from several input references. The resulting hybrid entities are intended to be non-trivial solutions to specific design situations, so far, at the stage of this research, mainly formal requirements.
keywords	Self-Orgnizing Maps; Cognitive Space; Design Options; Form Finding; Artificial Intelligence
series	eCAADe
email
last changed	2022/06/07 07:54

_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	ecaade2018_139
id	ecaade2018_139
authors	Cudzik, Jan and Radziszewski, Kacper
year	2018
title	Artificial Intelligence Aided Architectural Design
doi	https://doi.org/10.52842/conf.ecaade.2018.1.077
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. 77-84
summary	Tools and methods used by architects always had an impact on the way building were designed. With the change in design methods and new approaches towards creation process, they became more than ever before crucial elements of the creation process. The automation of architects work has started with computational functions that were introduced to traditional computer-aided design tools. Nowadays architects tend to use specified tools that suit their specific needs. In some cases, they use artificial intelligence. Despite many similarities, they have different advantages and disadvantages. Therefore the change in the design process is more visible and unseen before solution are brought in the discipline. The article presents methods of applying the selected artificial intelligence algorithms: swarm intelligence, neural networks and evolutionary algorithms in the architectural practice by authors. Additionally research shows the methods of analogue data input and output approaches, based on vision and robotics, which in future combined with intelligence based algorithms, might simplify architects everyday practice. Presented techniques allow new spatial solutions to emerge with relatively simple intelligent based algorithms, from which many could be only accomplished with dedicated software. Popularization of the following methods among architects, will result in more intuitive, general use design tools.
keywords	computer aideed design; artificial intelligence,; evolutionary algorithms; swarm behaviour; optimization; parametric design
series	eCAADe
email
last changed	2022/06/07 07:56

_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
doi	https://doi.org/10.52842/conf.ecaade.2018.2.131
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
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_438
id	ecaade2018_438
authors	Das, Subhajit
year	2018
title	Interactive Artificial Life Based Systems, Augmenting Design Generation and Evaluation by Embedding Expert Opinion - A Human Machine dialogue for form finding.
doi	https://doi.org/10.52842/conf.ecaade.2018.1.085
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. 85-94
summary	Evolution of natural life and subsequently selection of life forms is an interesting topic that has been explored multiple times. This area of research and its application has high relevance in evolutionary design and automated design generation. Taking inspiration from Charles Darwin's theory, all biological species were formed by the process of evolution based on natural selection of the fittest (Darwin, n.d.) this paper explains exploratory research showcasing semi-automatic design generation. This is realized by an interactive artificial selection tool, where the designer or the end user makes key decisions steering the propagation and breeding of future design artifacts. This paper, describes two prototypes and their use cases, highlighting interaction based optimal design selection. One of the prototypes explains a 2d organic shape creator using a metaball shape approach, while the other discusses a spatial layout generation technique for conceptual design.
keywords	design generation; implicit surfaces; artificial life; decision making; artificial selection; spatial layout generation
series	eCAADe
email
last changed	2022/06/07 07:55

_id	ecaade2018_204
id	ecaade2018_204
authors	de Oliveira, Maria Jo?o, Moreira Rato, Vasco and Leit?o, Carla
year	2018
title	KINE[SIS]TEM'17 - A methodological process for a Nature-Based Design
doi	https://doi.org/10.52842/conf.ecaade.2018.1.561
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. 561-570
summary	Architecture is the mediator between the Environment and Humans. Nature maximal performance and minimal resources creations are Humanity inspiration that led us to exceed structural, material, mechanisms, tools, systems and methods boundaries (Oxman, 2010).Nature are the Architect of the most reliable and sustainable systems. Looking into Nature's lessons, this paper presents a Nature-based design methodology conducted during Kine[SIS]tem'17 Shading Systems International Summer School, held by the ISCTE-Instituto Universitário de Lisboa, Portugal, between 19th - 30th June 2017. The methodology encompasses two main stages, one before and other during the Summer School. From a pre-definition of context constrains, a nature based design strategy, to a planning of the manufacture and construction still during the phase of development of the design, conducted the Summer School participants through a defined biomimetic process that achieved the construction of 1:1 scale prototype.
keywords	Kinesis; Shading; System; Nature-based design
series	eCAADe
email
last changed	2022/06/07 07:55

_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	ecaaderis2018_116
id	ecaaderis2018_116
authors	Giannopoulou, Effimia, Montás Laracuente, Nelson Bernardo and Baquero, Pablo
year	2018
title	Qualitative Study on two Kinetic System Simulations - Experiments Based on Shape Memory Material and Stepper Motors
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. 95-102
keywords	This investigation intends to compare two computational design experiments operating on two kinetic architecture (Zuk and Clark 1970) design application domains: Shape-memory material (SMM) activated grids and stepper-actuated (SA) responsive skins. In the first one, the goal was to build a standard way of simulating SMM, which can be used as actuators in the construction of kinetic structures and in the second, to simulate and construct a responsive skin according to human interaction using kinect and stepper motors. In both experiments, a similar generative workflow was employed, combining insights from materials and mechanical systems. The objective is to investigate kinetic performance, kinetic design methodology, simulation implementation and applications within the two separate design domains. The general hypothesis is that both experiments become design workflows in themselves as real-time, dynamic modeling systems. A qualitatively study of both sets of cases, is taking in count general, simulation and application aspects, using evaluation criteria including workflow, material quantity, data capture and mechanical properties.
series	eCAADe
email
last changed	2018/05/29 14:33

_id	ecaade2018_169
id	ecaade2018_169
authors	Kasahara, Maki, Matsushita, Kiwa and Mizutani, Akihiro
year	2018
title	Learning from Generative Design System in the 60's - Case Study of Agricultural City Project by Kisho Kurokawa
doi	https://doi.org/10.52842/conf.ecaade.2018.2.095
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. 95-102
summary	The concept of generative design in Architecture and Urbanism can be found in the 60's before the wide availability of computer technology. This paper decodes one of the urban projects by Metabolist in 1960, which was intended to be a generative system applicable to other sites and evolves over time. Through our analysis, we de-code the formulation process, and verified our hypothesis by re-coding into the program using the software, Rhinoceros and Grasshopper. We found that the determinate factors rule more at the macro level of the project, but the parameters are set by taking the local conditions into account. At the micro level, the system leaves more freedom to accommodate various needs, reflecting the philosophy of the Metabolists. The investigation on this historical predecessor can provide useful insights for parameter settings in future generative system design.
keywords	Generative Design; Grasshopper; Kisho Kurokawa
series	eCAADe
email
last changed	2022/06/07 07:52

_id	ecaade2018_111
id	ecaade2018_111
authors	Khean, Nariddh, Fabbri, Alessandra and Haeusler, M. Hank
year	2018
title	Learning Machine Learning as an Architect, How to? - Presenting and evaluating a Grasshopper based platform to teach architecture students machine learning
doi	https://doi.org/10.52842/conf.ecaade.2018.1.095
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. 95-102
summary	Machine learning algorithms have become widely embedded in many aspects of modern society. They have come to enhance systems, such as individualised marketing, social media services, and search engines. However, contrasting its growing ubiquity, the architectural industry has been comparatively resistant in its adoption; objectively one of the slowest industries to integrate with machine learning. Machine learning expertise can be separate from professionals in other fields; however, this separation can be a major hinderance in architecture, where interaction between the designer and the design facilitates the production of favourable outcomes. To bridge this knowledge gap, this research suggests that the solution lies with architectural education. Through the development of a novel educative framework, the research aims to teach architecture students how to implement machine learning. Exploration of student-centred pedagogical strategies was used to inform the conceptualisation of the educative module, which was subsequently implemented into an undergraduate computational design studio, and finally evaluated on its ability to effectively teach designers machine learning. The developed educative module represents a step towards greater technological adoption in the architecture industry.
keywords	Artificial Intelligence; Machine Learning; Neural Networks; Student-Centred Learning; Educative Framework
series	eCAADe
email
last changed	2022/06/07 07:52

_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	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	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	ecaade2018_323
id	ecaade2018_323
authors	Newton, David
year	2018
title	Multi-Objective Qualitative Optimization (MOQO) in Architectural Design
doi	https://doi.org/10.52842/conf.ecaade.2018.1.187
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. 187-196
summary	Architectural design problems are often multi-objective in nature, involving both qualitative and quantitative objectives. Previous research has focused exclusively on the development of multi-objective optimization algorithms that work with multiple quantitative objectives. No previous research has looked at the topic of multi-objective qualitative optimization (MOQO), in which multiple qualitative objectives are optimized simultaneously. This research addresses MOQO through the development of a unique multi-objective optimization algorithm for the conceptual design phase that uses three-dimensional convolutional neural networks (3D CNNs) to measure user-defined qualities in architectural massing models.
keywords	multi-objective optimization; generative design; multi-objective qualitative optimization; algorithmic design
series	eCAADe
email
last changed	2022/06/07 07:58

_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	ijac201816403
id	ijac201816403
authors	Pantazis, Evangelos and David Gerber
year	2018
title	A framework for generating and evaluating façade designs using a multi-agent system approach
source	International Journal of Architectural Computing vol. 16 - no. 4, 248-270
summary	Digital design paradigms in architecture have been rooted in representational models which are geometry centered and therefore fail to capture building complexity holistically. Due to a lack of computational design methodologies, existing digital design workflows do little in predicting design performance in the early design stage and in most cases analysis and design optimization are done after a design is fixed. This work proposes a new computational design methodology, intended for use in the area of conceptual design of building design. The proposed methodology is implemented into a multi-agent system design toolkit which facilitates the generation of design alternatives using stochastic algorithms and their evaluation using multiple environmental performance metrics. The method allows the user to probabilistically explore the solution space by modeling the design parameters’ architectural design components (i.e. façade panel) into modular programming blocks (agents) which interact in a bottom-up fashion. Different problem requirements (i.e. level of daylight inside a space, openings) described into agents’ behavior allow for the coupling of data from different engineering fields (environmental design, structural design) into the a priori formation of architectural geometry. In the presented design experiment, a façade panel is modeled into an agent-based fashion and the multi-agent system toolkit is used to generate and evolve alternative façade panel configurations based on environmental parameters (daylight, energy consumption). The designer can develop the façade panel geometry, design behaviors, and performance criteria to evaluate the design alternatives. The toolkit relies on modular and functionally specific programming modules (agents), which provide a platform for façade design exploration by combining existing three-dimensional modeling and analysis software.
keywords	Generative design, multi-agent systems, façade design, agent-based modeling, stochastic search
series	journal
email
last changed	2019/08/07 14:04

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