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	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	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	caadria2018_197
id	caadria2018_197
authors	Rogers, Jessie, Schnabel, Marc Aurel and Lo, Tian Tian
year	2018
title	Digital Culture - An Interconnective Design Methodology Ecosystem
doi	https://doi.org/10.52842/conf.caadria.2018.1.493
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. 493-502
summary	Transitioning away from traditional design methodology, for example, paper sketching, CAAD works, and 'flat screen' rendering, this paper proposes a new methodological ecosystem of which tests its validity within a studio-based case study. The focus will prove whether dynamic implementation and interconnectivity of evolving design tools can create richness and complexity of a design outcome through arbitrary phases of a generative design methodology ecosystem. Processes tested include combinations of agent simulations, artistic image processing analysis, site photogrammetry, 3D immersive sketching both abstract and to site-scale, parametric design generation, and virtual reality style presentations. Enhancing the process of design with evolving techniques in a generative way which dynamically interconnects will stimulate a digital culture of design generation that includes new aspects of interest and introduces innovative opportunities within all corners of the architectural realm. Methodology components within this ecosystem of interaction prove that the architecture cannot be as rich and complex without the utilisation of all strengths within each unique design tool.
keywords	Methodology Ecosystem; Simulation; Immersive; Virtual Reality; Photogrammetry
series	CAADRIA
email
last changed	2022/06/07 07:56

_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	acadia18_176
id	acadia18_176
authors	Bidgoli, Ardavan; Veloso,Pedro
year	2018
title	DeepCloud. The Application of a Data-driven, Generative Model in Design
doi	https://doi.org/10.52842/conf.acadia.2018.176
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
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	acadia18_276
id	acadia18_276
authors	Bilotti, Jeremy; Norman, Bennett; Rosenwasser, David; Leo Liu, Jingyang; Sabin, Jenny
year	2018
title	Robosense 2.0. Robotic sensing and architectural ceramic fabrication
doi	https://doi.org/10.52842/conf.acadia.2018.276
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. 276-285
summary	Robosense 2.0: Robotic Sensing and Architectural Ceramic Fabrication demonstrates a generative design process based on collaboration between designers, robotic tools, advanced software, and nuanced material behavior. The project employs fabrication tools which are typically used in highly precise and predetermined applications, but uniquely thematizes the unpredictable aspects of these processes as applied to architectural component design. By integrating responsive sensing systems, this paper demonstrates real-time feedback loops which consider the spontaneous agency and intuition of the architect (or craftsperson) rather than the execution of static or predetermined designs. This paper includes new developments in robotics software for architectural design applications, ceramic-deposition 3D printing, sensing systems, materially-driven pattern design, and techniques with roots in the arts and crafts. Considering the increasing accessibility and advancement of 3D printing and robotic technologies, this project seeks to challenge the erasure of materiality: when mistakes or accidents caused by inconsistencies in natural material are avoided or intentionally hidden. Instead, the incorporation of material and user-input data yields designs which are imbued with more nuanced traces of making. This paper suggests the potential for architects and craftspeople to maintain a more direct and active relationship with the production of their designs.
keywords	full paper, fabrication & robotics, robotic production, digital fabrication, digital craft
series	ACADIA
type	paper
email
last changed	2022/06/07 07:54

_id	ecaadesigradi2019_249
id	ecaadesigradi2019_249
authors	Chiarella, Mauro, Gronda, Luciana and Veizaga, Martín
year	2019
title	RILAB - architectural envelopes - From spatial representation (generative algorithm) to geometric physical optimization (scientific modeling)
doi	https://doi.org/10.52842/conf.ecaade.2019.3.017
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 3, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 17-24
summary	Augmented graphical thinking operates by integrating algorithmic, heuristic, and manufacturing processes. The Representation and Ideation Laboratory (RILAB-2018) exercise begins with the application of a parametric definition developed by the team of teachers, allowing for the construction of structural systems by the means of the combination of segmental shells and bending-active. The main objetive is the construction of a scientific model of simulation for bending-active laminar structures has brought into reality trustworthy previews for architectural envelopes through the interaction of parametrized relational variables. This way we put designers in a strategic role for the building of the pre-analysis models, allowing more preciseness at the time of picking and defining materials, shapes, spaces and technologies and thus minimizing the decisions based solely in the definition of structural typological categories, local tradition or direct experience. The results verify that the strategic integration of models of geometric physical optimization and spatial representation greatly expand the capabilities in the construction of the complex system that operates in the act of projecting architecture.
keywords	architectural envelopes; augmented graphic thinking; geometric optimization; bending-active
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:55

_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_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	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	ecaade2018_180
id	ecaade2018_180
authors	Kwieciñski, Krystian and Markusiewicz, Jacek
year	2018
title	HOPLA - Interfacing Automation for Mass-customization
doi	https://doi.org/10.52842/conf.ecaade.2018.2.159
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. 159-168
summary	HOPLA (Home Planner) is a computer-aided design system aimed at simplifying customization of house design. It merges aspects of user-centered computer-aided design with machine-centered computerized design, as defined by Negroponte in The Architecture Machine. The tool was developed to fulfill mass-customization principles without compromising mass production efficiency and to support users' participation in design processes to help them formulate expectations and search for design solutions. We describe the details of the system development and its possible use in the process of mass-customization and participatory design of single-family houses. The system consists of two core elements: an algorithm based on a generic grammar responsible for generating design solutions in relation to user input, and a Tangible User Interface allowing users to introduce data and to control the process in an intuitive way. The main challenge in developing the system was to synchronize the freedom of user's design decisions with the rigor of machine's verification process.
keywords	mass-customization; participatory design; tangible user interface; house design; generative design
series	eCAADe
email
last changed	2022/06/07 07:55

_id	acadia18_434
id	acadia18_434
authors	Meibodi, Mania Aghaei ; Jipa, Andrei; Giesecke, Rena; Shammas, Demetris; Bernhard, Mathias; Leschok, Matthias; Graser, Konrad; Dillenburger, Benjamin
year	2018
title	Smart Slab. Computational design and digital fabrication of a lightweight concrete slab
doi	https://doi.org/10.52842/conf.acadia.2018.434
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. 434-443
summary	This paper presents a computational design approach and novel digital fabrication method for an optimized lightweight concrete slab using a 3D-printed formwork. Smart Slab is the first concrete slab fabricated with a 3D-printed formwork. It is a lightweight concrete slab, displaying three-dimensional geometric differentiation on multiple scales. The optimization of slab systems can have a large impact on buildings: more compact slabs allow for more usable space within the same building volume, refined structural concepts allow for material reduction, and integrated prefabrication can reduce complexity on the construction site. Among the main challenges is that optimized slab geometries are difficult to fabricate in a conventional way because non-standard formworks are very costly. Novel digital fabrication methods such as additive manufacturing of concrete can provide a solution, but until now the material properties and the surface quality only allow for limited applications. The fabrication approach presented here therefore combines the geometric freedom of 3D binderjet printing of formworks with the structural performance of fiber reinforced concrete. Using 3D printing to fabricate sand formwork for concrete, enables the prefabrication of custom concrete slab elements with complex geometric features with great precision. In addition, space for building systems such as sprinklers and Lighting could be integrated in a compact way. The design of the slab is based on a holistic computational model which allows fast design optimization and adaptation, the integration of the planning of the building systems, and the coordination of the multiple fabrication processes involved with an export of all fabrication data. This paper describes the context, design drivers, and digital design process behind the Smart Slab, and then discusses the digital fabrication system used to produce it, focusing on the 3D-printed formwork. It shows that 3D printing is already an attractive alternative for custom formwork solutions, especially when strategically combined with other CNC fabrication methods. Note that smart slab is under construction and images of finished elements can be integrated within couple of weeks.
keywords	full paper, digital fabrication, computation, generative design, hybrid practices
series	ACADIA
type	paper
email
last changed	2022/06/07 07:58

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

_id	caadria2018_228
id	caadria2018_228
authors	Newton, David
year	2018
title	Accommodating Change and Open-Ended Search in Design Optimization
doi	https://doi.org/10.52842/conf.caadria.2018.2.175
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. 175-184
summary	Many real-world architectural multi-objective problems (MOPs) are dynamic and may have objectives, decision variables, and constraints that change during the optimization process. These problems are known as dynamic MOPs (DMOPs). Dynamic multi-objective evolutionary algorithms (DMOEAs) have emerged in the fields of optimization, operations research, and computer science as one way to address the challenges posed by DMOPs. DMOEAs offer new capacities for exploration and interaction with the designer, but they have not yet been studied in the field of architecture. This research addresses these issues through the development of a unique interactive DMOEA-based design tool for the conceptual design phase. We propose a new modification to the popular nondominated sorting genetic algorithm II (NSGA-II), that we call the dynamic progressive for architecture NSGA-II (DPA-NSGA-II). We show that DPA-NSGA-II outperforms NSGA-II in finding novel solutions.
keywords	algorithmic design; multi-objective optimization; evolutionary computation; parametric design; generative design
series	CAADRIA
email
last changed	2022/06/07 07:58

_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

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

_id	ecaade2020_445
id	ecaade2020_445
authors	Spiegelhalter, Thomas, Andia, Alfredo, Levente, Juhasz and Namuduri, Srikanth
year	2020
title	Part 1: The Integrated Decision Support System - Generative and synthetic biological design imaginations for the Miami bay area
doi	https://doi.org/10.52842/conf.ecaade.2020.2.011
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 11-20
summary	In less than 150 years our carbon society transformed the planet. Today more than 50% of ecologies in the world are determined by unsustainable industrialization processes. The latest IPCC reports show that we are quickly arriving at points of no return in the warming of our planet. We cannot afford to continue in the same direction, we need a new imagination. As part of an E.U.-US funded $1.9 million research project we have been working on multiple projects for the future of the Miami islands since 2018:1. We developed a generative GIS-BIM based Python API for mapping and optimization of carbon-neutral design workflows. It includes genetic design combinatorics with intuitive graphical Dynamo-Python-Grasshopper programming with experimental design results. 2. We worked on a series of design research for the Miami Bay that envisions islands, living shorelines, programmable soils, and infrastructures that grow by themselves using synthetic biology.
keywords	Automated Workflows, Synthetic Biology, Artificial Intelligence, Architecture, Sea-level Rise
series	eCAADe
email
last changed	2022/06/07 07:56

_id	ecaadesigradi2019_101
id	ecaadesigradi2019_101
authors	Tebaldi, Isadora, Henriques, Gonçalo Castro and Passaro, Andres Martin
year	2019
title	A Generative System for the Terrain Vague - Transcarioca Bus Expressway in Rio de Janeiro
doi	https://doi.org/10.52842/conf.ecaade.2019.1.035
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 1, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 35-44
summary	The transport infrastructures are important elements in the cities, but, as there is a lack of planning, they tear through the urban fabric and leave empty spaces. Due to government and private disinterest, these spaces become vacant, forgotten and degraded. However, these extensive Terrain Vague offer new potential for urban use. To exploit this potential, we need methodologies that can offer personalised, extensive, feasible urban solutions. For this, we propose a computational generative system, following a 4-step methodology: 1) Site analyses and Terrain Vague identification; 2) Site classification according to parameters based on a "visual grammar"; 3) Algorithm associating space properties with geometric transformation to generate solutions: namely transformative operations in public spaces, additive transformations in semi-public spaces and subtractive operations in semi-private spaces; 4) Solution evaluation and development, according to shade criteria, spatial hierarchy and volumetric density. With our own algorithms combined with genetic algorithms, we guided the evolution of 50 volumetric solutions. The exponential increase in information requires new methodologies (Schwab, 2018). Results show the potential of computational methodologies to produce extensive urban solutions. This research, developed in a final graduation project in Architecture, aims at stimulating generative methodologies in undergraduate courses.
keywords	Terrain Vague; generative systems; parametric urbanism; genetic algorithms
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:58

_id	caadria2018_332
id	caadria2018_332
authors	van Ameijde, Jeroen and Song, Yutao
year	2018
title	Data-Driven Urban Porosity - Incorporating Parameters of Public Space into a Generative Urban Design Process
doi	https://doi.org/10.52842/conf.caadria.2018.1.173
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. 173-182
summary	This paper presents an urban design project for a new city district, using generative design processes in architecture and urbanism developed over several years within academic research and practice work. The paper discusses the opportunities and challenges found when using a data-driven urban design methodology in relation to the complex logistical, social and economical networks of new urban centers.
keywords	Design Methods and Information Processing; Generative System; Simulation & Optimization; Urban Planning and Design; Public Space Design
series	CAADRIA
email
last changed	2022/06/07 07:58

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