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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Reformat results as: short short into frame detailed detailed into frame

_id	ecaadesigradi2019_605
id	ecaadesigradi2019_605
authors	Andrade Zandavali, Bárbara and Jiménez García, Manuel
year	2019
title	Automated Brick Pattern Generator for Robotic Assembly using Machine Learning and Images
doi	https://doi.org/10.52842/conf.ecaade.2019.3.217
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. 217-226
summary	Brickwork is the oldest construction method still in use. Digital technologies, in turn, enabled new methods of representation and automation for bricklaying. While automation explored different approaches, representation was limited to declarative methods, as parametric filling algorithms. Alternatively, this work proposes a framework for automated brickwork using a machine learning model based on image-to-image translation (Conditional Generative Adversarial Networks). The framework consists of creating a dataset, training a model for each bond, and converting the output images into vectorial data for robotic assembly. Criteria such as: reaching wall boundary accuracy, avoidance of unsupported bricks, and brick's position accuracy were individually evaluated for each bond. The results demonstrate that the proposed framework fulfils boundary filling and respects overall bonding structural rules. Size accuracy demonstrated inferior performance for the scale tested. The association of this method with 'self-calibrating' robots could overcome this problem and be easily implemented for on-site.
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:54

_id	acadia19_412
id	acadia19_412
authors	Del Campo, Matias; Manninger, Sandra; Carlson, Alexandra
year	2019
title	Imaginary Plans
doi	https://doi.org/10.52842/conf.acadia.2019.412
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. 412-418
summary	Artificial Neural Networks (NN) have become ubiquitous across disciplines due to their high performance in modeling the real world to execute complex tasks in the wild. This paper presents a computational design approach that uses the internal representations of deep vision neural networks to generate and transfer stylistic form edits to both 2D floor plans and building sections. The main aim of this paper is to demonstrate and interrogate a design technique based on deep learning. The discussion includes aspects of machine learning, 2D to 2D style transfers, and generative adversarial processes. The paper examines the meaning of agency in a world where decision making processes are defined by human/machine collaborations (Figure 1), and their relationship to aspects of a Posthuman design ecology. Taking cues from the language used by experts in AI, such as Hallucinations, Dreaming, Style Transfer, and Vision, the paper strives to clarify the position and role of Artificial Intelligence in the discipline of Architecture.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:55

_id	ecaadesigradi2019_648
id	ecaadesigradi2019_648
authors	Eisenstadt, Viktor, Langenhan, Christoph and Althoff, Klaus-Dieter
year	2019
title	Generation of Floor Plan Variations with Convolutional Neural Networks and Case-based Reasoning - An approach for transformative adaptation of room configurations within a framework for support of early conceptual design phases
doi	https://doi.org/10.52842/conf.ecaade.2019.2.079
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 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 79-84
summary	We present an approach for computer-aided generation of different variations of floor plans during the early phases of conceptual design in architecture. The early design phases are mostly characterized by the processes of inspiration gaining and search for contextual help in order to improve the building design at hand. The generation method described in this work uses the novel as well as established artificial intelligence methods, namely, generative adversarial nets and case-based reasoning, for creation of possible evolutions of the current design based on the most similar previous designs. The main goal of this approach is to provide the designer with information on how the current floor plan can evolve over time in order to influence the direction of the design process. The work described in this paper is part of the methodology FLEA (Find, Learn, Explain, Adapt) whose task is to provide a holistic structure for support of the early conceptual phases in architecture. The approach is implemented as the adaptation component of the framework MetisCBR that is based on FLEA.
keywords	room configuration; adaptation; case-based reasoning; convolutional neural networks; conceptual design
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:55

_id	ecaadesigradi2019_514
id	ecaadesigradi2019_514
authors	de Miguel, Jaime, Villafa?e, Maria Eugenia, Piškorec, Luka and Sancho-Caparrini, Fernando
year	2019
title	Deep Form Finding - Using Variational Autoencoders for deep form finding of structural typologies
doi	https://doi.org/10.52842/conf.ecaade.2019.1.071
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. 71-80
summary	In this paper, we are aiming to present a methodology for generation, manipulation and form finding of structural typologies using variational autoencoders, a machine learning model based on neural networks. We are giving a detailed description of the neural network architecture used as well as the data representation based on the concept of a 3D-canvas with voxelized wireframes. In this 3D-canvas, the input geometry of the building typologies is represented through their connectivity map and subsequently augmented to increase the size of the training set. Our variational autoencoder model then learns a continuous latent distribution of the input data from which we can sample to generate new geometry instances, essentially hybrids of the initial input geometries. Finally, we present the results of these computational experiments and lay out the conclusions as well as outlook for future research in this field.
keywords	artificial intelligence; deep neural networks; variational autoencoders; generative design; form finding; structural design
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:55

_id	acadia19_370
id	acadia19_370
authors	Mohammad, Ali; Beorkrem, Christopher; Ellinger, Jefferson
year	2019
title	Hybrid Elevations using GAN Networks
doi	https://doi.org/10.52842/conf.acadia.2019.370
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. 370-379
summary	This project is an attempt to develop and test a method for generating one-sided hybrid exterior building elevations using designer’s base criteria and design rule sets as inputs in an advanced artificial intelligence network. Architects are using computational design to expedite the iteration process in an efficient manner. Optimization techniques utilizing genetic solvers allow designers to explore broad sets of iterations within a predefined subset. However, with the application of artificial intelligence networks these fields of exploration can be expanded upon to develop ranges of exploration which can explore iterations outside of typical ranges. This paper explores the use of Generative Adversarial Networks (GAN) to explore and demonstrate their possible capabilities to typical design problems. In this instance we are exploring their application in the development of architectural elevations.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:58

_id	ecaadesigradi2019_135
id	ecaadesigradi2019_135
authors	Newton, David
year	2019
title	Deep Generative Learning for the Generation and Analysis of Architectural Plans with Small Datasets
doi	https://doi.org/10.52842/conf.ecaade.2019.2.021
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 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 21-28
summary	The field of generative architectural design has explored a wide range of approaches in the automation of design production, but these approaches have demonstrated limited artificial intelligence. Generative Adversarial Networks (GANs) are a leading deep generative model that use deep neural networks (DNNs) to learn from a set of training examples in order to create new design instances with a degree of flexibility and fidelity that outperform competing generative approaches. Their application to generative tasks in architecture, however, has been limited. This research contributes new knowledge on the use of GANs for architectural plan generation and analysis in relation to the work of specific architects. Specifically, GANs are trained to synthesize architectural plans from the work of the architect Le Corbusier and are used to provide analytic insight. Experiments demonstrate the efficacy of different augmentation techniques that architects can use when working with small datasets.
keywords	generative design; deep learning; artificial intelligence; generative adversarial networks
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:58

_id	artificial_intellicence2019_117
id	artificial_intellicence2019_117
authors	Stanislas Chaillou
year	2020
title	ArchiGAN: Artificial Intelligence x Architecture
doi	https://doi.org/https://doi.org/10.1007/978-981-15-6568-7_8
source	Architectural Intelligence Selected Papers from the 1st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	AI will soon massively empower architects in their day-to-day practice. This article provides a proof of concept. The framework used here offers a springboard for discussion, inviting architects to start engaging with AI, and data scientists to consider Architecture as a field of investigation. In this article, we summarize a part of our thesis, submitted at Harvard in May 2019, where Generative Adversarial Neural Networks (or GANs) get leveraged to design floor plans and entire buildings .
series	Architectural Intelligence
email
last changed	2022/09/29 07:28

_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	caadria2019_422
id	caadria2019_422
authors	Wang, Xiao, Tang, Peng and Shi, Xing
year	2019
title	Analysis and Conservation Methods of Traditional Architecture and Settlement Based on Knowledge Discovery and Digital Generation - A Case Study of Gunanjie Street in China
doi	https://doi.org/10.52842/conf.caadria.2019.1.757
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 1, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 757-766
summary	In the conservation plan of traditional architecture and settlement, the mismatch between design and construction is an inevitable problem. The mismatch commonly shows as the variations in the cognition of the traditionality of architecture feature. In most cases, the evaluation of historical feature is made based on designers' subjective perception, experience, and understanding of the traditional style. Also, without an appropriate guide and unified control, it could make the conservation plan less efficient in practice. Therefore, a quantitative method for conservation plan is needed, which is expected to be effective especially for massive non-key but traditional ordinary buildings. In this study on Gunanjie Street, in Yixing, China, a new method of feature analysis and generative design was developed to regenerate the district. The proposed method first adapted new data acquisition and processing techniques to gather information and build the database. Cognition investigation and morphology analysis were then implemented to quantify and evaluate the features of historical characteristics, as well as the knowledge discovery tools, were further used to abstract the rules of the traditional facade. With these phases, the proposed method was able to generate the referable design schemes quantitatively and establish generally accepted conservation plans and guidelines.
keywords	Traditional architecture and settlement; historical feature; Knowledge Discovery; digital generation; conservation
series	CAADRIA
email
last changed	2022/06/07 07:58

_id	acadia19_168
id	acadia19_168
authors	Adilenidou, Yota; Ahmed, Zeeshan Yunus; Freek, Bos; Colletti, Marjan
year	2019
title	Unprintable Forms
doi	https://doi.org/10.52842/conf.acadia.2019.168
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.168-177
summary	This paper presents a 3D Concrete Printing (3DCP) experiment at the full scale of virtualarchitectural bodies developed through a computational technique based on the use of Cellular Automata (CA). The theoretical concept behind this technique is the decoding of errors in form generation and the invention of a process that would recreate the errors as a response to optimization (Adilenidou 2015). The generative design process established a family of structural and formal elements whose proliferation is guided through sets of differential grids (multi-grids) leading to the build-up of large span structures and edifices, for example, a cathedral. This tooling system is capable of producing, with specific inputs, a large number of outcomes in different scales. However, the resulting virtual surfaces could be considered as "unprintable" either due to their need of extra support or due to the presence of many cavities in the surface topology. The above characteristics could be categorized as errors, malfunctions, or undesired details in the geometry of a form that would need to be eliminated to prepare it for printing. This research project attempts to transform these "fabrication imprecisions" through new 3DCP techniques into factors of robustness of the resulting structure. The process includes the elimination of the detail / "errors" of the surface and their later reinsertion as structural folds that would strengthen the assembly. Through this process, the tangible outputs achieved fulfill design and functional requirements without compromising their structural integrity due to the manufacturing constraints.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:54

_id	ecaade2021_203
id	ecaade2021_203
authors	Arora, Hardik, Bielski, Jessica, Eisenstadt, Viktor, Langenhan, Christoph, Ziegler, Christoph, Althoff, Klaus-Dieter and Dengel, Andreas
year	2021
title	Consistency Checker - An automatic constraint-based evaluator for housing spatial configurations
doi	https://doi.org/10.52842/conf.ecaade.2021.2.351
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 2, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 351-358
summary	The gradual rise of artificial intelligence (AI) and its increasing visibility among many research disciplines affected Computer-Aided Architectural Design (CAAD). Architectural deep learning (DL) approaches are being developed and published on a regular basis, such as retrieval (Sharma et al. 2017) or design style manipulation (Newton 2019; Silvestre et al. 2016). However, there seems to be no method to evaluate highly constrained spatial configurations for specific architectural domains (such as housing or office buildings) based on basic architectural principles and everyday practices. This paper introduces an automatic constraint-based consistency checker to evaluate the coherency of semantic spatial configurations of housing construction using a small set of design principles to evaluate our DL approaches. The consistency checker informs about the overall performance of a spatial configuration followed by whether it is open/closed and the constraints it didn't satisfy. This paper deals with the relation of spaces processed as mathematically formalized graphs contrary to existing model checking software like Solibri.
keywords	model checking, building information modeling, deep learning, data quality
series	eCAADe
email
last changed	2022/06/07 07:54

_id	ecaadesigradi2019_619
id	ecaadesigradi2019_619
authors	Beyer, Bastian, Suárez, Daniel and Palz, Norbert
year	2019
title	Microbiologically Activated Knitted Composites - Reimagining a column for the 21st century
doi	https://doi.org/10.52842/conf.ecaade.2019.2.541
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 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 541-552
summary	A column is an archetypal constituent of architecture which historically underwent constant reiteration in accordance with the prevalent architectural style, material culture or technical and structural possibilities. The project reimagined this architectural element through harnessing the synergies of digital design, textile logic, and contemporary biotechnology. Textile materiality and aesthetic are deeply rooted in architectural history as a soft and ephemeral antipode to rigid building materials. An investigation in historic mechanical hand-knitting techniques allowed to extract their underlying structural and geometric logic to develop a structural optimisation pipeline with a graded yarn as a base material and a geometric optimization based on local distribution of knitting patterns. Bacterially driven biocalcification was applied to transform the soft textile structure into a rigid material. Hereby an active textile microbiome was established through colonizing of the yarn with the bacterium S. pasteurii which successively precipitated calcite on microscale within the textile substrate hence ultimately influencing the global structural behaviour of the column.
keywords	textile microbiome; material customization; knitting; yarn augmentation
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:52

_id	acadia19_80
id	acadia19_80
authors	Bouayad, Ghali
year	2019
title	Three-Dimensional Translation of Japanese Katagami Patterns
doi	https://doi.org/10.52842/conf.acadia.2019.080
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. 80-89
summary	The aim of this ongoing doctoral research is to rely on the incommensurable potential held in Japanese Katagami patterns in order to translate them into three-dimensional speculative architectures and architectural components that afford architects other design approaches differentiated from systemic and typical space configurations. While many designers are diving in the generative and computational design world by developing new personal methods, we would like to recycle the existing production of Katagami patterns into three-dimensional architectural elements that will perpetuate work of Katagami artists beyond time, borders, and scope of applicability. Given that the current digital shift has given us more computation power, we are broadening Katagami with new fabrication strategies and new methods to explore, produce, and stock geometry and data. In this paper, we rely on the Processing library IGeo (developed by Satoru Sugihara) to build bottom-up agent-based algorithms to study the architectural potential of Katagami patterns as a top-down clean and simple initial topology that avoids imitation of standard templates applied during the process of configuring and planning architectural space.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:54

_id	caadria2019_491
id	caadria2019_491
authors	Cai, Chenyi, Tang, Peng and Li, Biao
year	2019
title	Intelligent Generation of Architectural layout inheriting spatial features of Chinese Garden Based on Prototype and Multi-agent System - A Case Study on Lotus Teahouse in Yixing
doi	https://doi.org/10.52842/conf.caadria.2019.1.291
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 1, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 291-300
summary	This study presents an approach for the intelligent generation of architectural layout, in which partial space inherits Chinese garden spatial features. The approach combines spatial prototype analysis and evolutionary optimization process. On one hand, from the perspective of shape grammar, this paper both analyzes and abstracts the spatial prototype that describes the spatial characteristics of Chinese gardens, including the organization system of architecture and landscape, with the spatial sequences along the tourism orientation. On the other hand, taking the design task of Lotus teahouse as an example, a typical spatial prototype is selected to develop the generative intelligent experiment to achieve the architectural layout, in which the spatial prototype is inherited. Through rule-making and parameter adjustment, the spatial prototype will eventually be transformed into a computational model based on the multi-agent system. Hence, the experiment of intelligent generation of architectural layout is carried out under the influence of the function, form and environmental factors; and a three-dimensional conceptual model that inherits the Chinese garden spatial prototype is obtained ultimately.
keywords	Chinese garden; Architectural layout; Spatial prototype; Multi-agent system; Intelligent generation
series	CAADRIA
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	ecaadesigradi2019_369
id	ecaadesigradi2019_369
authors	Contreras, Camilo Hernán
year	2019
title	Surfaces Plot - A data visualization system to support design space exploration
doi	https://doi.org/10.52842/conf.ecaade.2019.2.145
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 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 145-152
summary	The notion of design spaces (DS) can be understood as the potential of a parametric model, it is basically the number of possible combinations for its input parameters. When combining tools that produce these alternatives automatically with different simulation softwares, the concept of parametric analysis (PA) emerges. This implies a simultaneous evaluation of the alternatives as they are constructed by the parametric model, producing large amounts of information. This article describes a sectional approach to the management of this information and a visualization technique to represent it looking for correlations between the input parameters and their performance. Correlations that are fundamental to making decisions with confidence when design problems challenge traditional methods of decision-making based on heuristics and design expertise.
keywords	Design Space ; Performance-Based Design; Parametric Analysis; Generative Design; Data Visualization
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:56

_id	caadria2019_117
id	caadria2019_117
authors	Deniz Kiraz, Leyla and Kocaturk, Tuba
year	2019
title	Integrating User-Behaviour as Performance Criteria in Conceptual Parametric Design
doi	https://doi.org/10.52842/conf.caadria.2019.1.215
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 1, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 215-224
summary	Prediction of user behaviour has always been problematic in architectural design. Several methods have already been developed and explored to model human behaviour in architecture. However, the majority of these methods are implemented during post-design evaluation where the insights obtained can only be implemented in a limited capacity. There is an apparent gap and opportunity, in current research and practice, to embed behaviour simulations directly into the conceptual design process. The proposed paper (research) aims to fill this gap. This paper will report on the results of a recently completed research exploring the integration process of Agent Based Modelling into the conceptual design process, using a parametric design approach. The research resulted in the development of a methodological framework for the integration of behavioural parameters into the explorative stages of the early design process. This paper also offers a categorisation and critical evaluation of existing Agent Based Modelling applications in current research and practice, which leads to the formulation of possible pathways for future implementation.
keywords	Performance Based Design; Generative Design; Behaviour Modelling; Agent Based Modelling; Parametric Design
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	ecaadesigradi2019_191
id	ecaadesigradi2019_191
authors	Engel, Pedro
year	2019
title	CONTROLING DESIGN VARIATIONS - DESIGNING A SEMANTIC CONTROLER FOR A GENERATIVE SYSTEM
doi	https://doi.org/10.52842/conf.ecaade.2019.2.369
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 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 369-376
summary	This article will describe the recent steps in the development of a computational generative system based on the selection and combination of ordinary architectural elements. Built as a Grasshopper definition, the system was conceived to generate designs of architectural façades and to produce models, physical and digital, for didactic use. More specifically, The paper will address the conception of controlling devices, that is, the parts of the computational system that govern design variations. This process involved two complementary actions: first, the definition of a clear organizational logic, where elements can be represented as a data structure that encompasses classes, sub-classes, sets, libraries and attributes; secondly, the establishment of means to operate the variations through the use of filters and heuristics based on visual patterns, allowing varying degrees of automation and user control. It will be argued that such organizational model paves the way to increase the number of design possibilities in the future and to and provide means to integrate of architectural criteria into the generation process. This research has received the support of CNPq.
keywords	Algorithm; Parametric Design; Architectural Design; Teaching ; Physical Model
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:55

_id	ecaadesigradi2019_239
id	ecaadesigradi2019_239
authors	Garrido, Federico and Meyer, Joost
year	2019
title	Dexterity-controlled Design Procedures
doi	https://doi.org/10.52842/conf.ecaade.2019.1.659
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. 659-668
summary	This paper explores the development of design procedures in relationship to their digital proceedings, in order to interface human movement and parametric design procedures. The research studied the use of Leap Motion controller, a gesture recognition device using infrared sensors combined with time-based generative tools in Rhinoceros Grasshopper. A physical, artistic procedure was used as a reference to model a digital design procedure, including a series of parametric definitions combined with them in an attempt to produce complex three-dimensional designs in real time. In a later stage of this research, a modular, open source, digitizing arm was developed to capture hand movement and interact with an autonomous parametric definition, augmenting even more the range of applications of dexterity-based digital design. The challenge of this experimental investigation lies in the negotiation of the designer's needs for a complex yet open design process and the possibilities of defined soft- and hardware solutions.
keywords	digital design; dexterity; parametric design; motion detection
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:51

_id	caadria2019_109
id	caadria2019_109
authors	Kim, Jinsung, Song, Jaeyeol and Lee, Jin-Kook
year	2019
title	Approach to Auto-recognition of Design Elements for the Intelligent Management of Interior Pictures
doi	https://doi.org/10.52842/conf.caadria.2019.2.785
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 2, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 785-794
summary	This paper explores automated recognition of elements in interior design pictures for an intelligent design reference management system. Precedent design references have a significant role to help architects, designer and even clients in general architecture design process. Pictures are one of the representation that could exactly show a kind of design idea and knowledge. Due to the velocity, variety and volume of reference pictures data with growth of references platform, it is hard and time-consuming to handle the data with current manual way. To solve this problem , this paper depicts a deep learning-based approach to figuring out design elements and recognizing the design feature of them on the interior pictures using faster-RCNN and CNN algorithms. The targets are the residential furniture such as a table and a seating. Through proposed application, input pictures can automatically have tagging data as follows; seating1(type: sofa, seating capacity: two-seaters, design style: classic)
keywords	Interior design picture; Design element; Design feature; Automated recognition; Design Reference management
series	CAADRIA
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last changed	2022/06/07 07:52

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