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	acadia19_298
id	acadia19_298
authors	Leach, Neil
year	2019
title	Do Robots Dream of Digital Sleep?
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. 298-309
doi	https://doi.org/10.52842/conf.acadia.2019.298
summary	AI is playing an increasingly important role in everyday life. But can AI actually design? This paper takes its point of departure from Philip K Dick’s novel, Do Androids Dream of Electric Sheep? and refers to Google’s DeepDream software, and other AI techniques such as GANs, Progressive GANs, CANs and StyleGAN, that can generate increasingly convincing images, a process often described as ‘dreaming’. It notes that although generative AI does not possess consciousness, and therefore cannot literally dream, it can still be a powerful design tool that becomes a prosthetic extension to the human imagination. Although the use of GANs and other deep learning AI tools is still in its infancy, we are at the dawn of an exciting – but also potentially terrifying – new era for architectural design. Most importantly, the paper concludes, the development of AI is also helping us to understand human intelligence and 'creativity'.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:52

_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
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
doi	https://doi.org/10.52842/conf.ecaade.2019.2.021
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	caadria2020_259
id	caadria2020_259
authors	Rhee, Jinmo, Veloso, Pedro and Krishnamurti, Ramesh
year	2020
title	Integrating building footprint prediction and building massing - an experiment in Pittsburgh
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 669-678
doi	https://doi.org/10.52842/conf.caadria.2020.2.669
summary	We present a novel method for generating building geometry using deep learning techniques based on contextual geometry in urban context and explore its potential to support building massing. For contextual geometry, we opted to investigate the building footprint, a main interface between urban and architectural forms. For training, we collected GIS data of building footprints and geometries of parcels from Pittsburgh and created a large dataset of Diagrammatic Image Dataset (DID). We employed a modified version of a VGG neural network to model the relationship between (c) a diagrammatic image of a building parcel and context without the footprint, and (q) a quadrilateral representing the original footprint. The option for simple geometrical output enables direct integration with custom design workflows because it obviates image processing and increases training speed. After training the neural network with a curated dataset, we explore a generative workflow for building massing that integrates contextual and programmatic data. As trained model can suggest a contextual boundary for a new site, we used Massigner (Rhee and Chung 2019) to recommend massing alternatives based on the subtraction of voids inside the contextual boundary that satisfy design constraints and programmatic requirements. This new method suggests the potential that learning-based method can be an alternative of rule-based design methods to grasp the complex relationships between design elements.
keywords	Deep Learning; Prediction; Building Footprint; Massing; Generative Design
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	ijac201917102
id	ijac201917102
authors	Cutellic, Pierre
year	2019
title	Towards encoding shape features with visual event-related potential based brain–computer interface for generative design
source	International Journal of Architectural Computing vol. 17 - no. 1, 88-102
summary	This article will focus on abstracting and generalising a well-studied paradigm in visual, event-related potential based brain–computer interfaces, for the spelling of characters forming words, into the visually encoded discrimination of shape features forming design aggregates. After identifying typical technologies in neuroscience and neuropsychology of high interest for integrating fast cognitive responses into generative design and proposing the machine learning model of an ensemble of linear classifiers in order to tackle the challenging features that electroencephalography data carry, it will present experiments in encoding shape features for generative models by a mechanism of visual context updating and the computational implementation of vision as inverse graphics, to suggest that discriminative neural phenomena of event-related potentials such as P300 may be used in a visual articulation strategy for modelling in generative design.
keywords	Generative design, machine learning, brain–computer interface, design computing and cognition, integrated cognition, neurodesign, shape, form and geometry, design concepts and strategies
series	journal
email
last changed	2019/08/07 14:04

_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
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
doi	https://doi.org/10.52842/conf.ecaade.2019.1.071
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_412
id	acadia19_412
authors	Del Campo, Matias; Manninger, Sandra; Carlson, Alexandra
year	2019
title	Imaginary Plans
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
doi	https://doi.org/10.52842/conf.acadia.2019.412
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	caadria2019_462
id	caadria2019_462
authors	Koh, Immanuel, Amorim, Pedro and Huang, Jeffrey
year	2019
title	Machinic Design Inference: from PokÃ©mon to Architecture - A Probabilistic Machine Learning Model for Generative Design using Game Levels Abstractions
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. 421-430
doi	https://doi.org/10.52842/conf.caadria.2019.2.421
summary	In this paper, we use a probabilistic machine learning model, trained with a corpus of existing game levels tile-maps, to study the potential of an inference design system for architectural design. Our system is able to extract implicit spatial patterns and generate new spatial configurations with similar semantics of perception and navigation.
keywords	Machine Learning; Artificial Intelligence; Generative Design
series	CAADRIA
email
last changed	2022/06/07 07:51

_id	caadria2021_053
id	caadria2021_053
authors	Rhee, Jinmo and Veloso, Pedro
year	2021
title	Generative Design of Urban Fabrics Using Deep Learning
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 31-40
doi	https://doi.org/10.52842/conf.caadria.2021.1.031
summary	This paper describes the Urban Structure Synthesizer (USS), a research prototype based on deep learning that generates diagrams of morphologically consistent urban fabrics from context-rich urban datasets. This work is part of a larger research on computational analysis of the relationship between urban context and morphology. USS relies on a data collection method that extracts GIS data and converts it to diagrams with context information (Rhee et al., 2019). The resulting dataset with context-rich diagrams is used to train a Wasserstein GAN (WGAN) model, which learns how to synthesize novel urban fabric diagrams with the morphological and contextual qualities present in the dataset. The model is also trained with a random vector in the input, which is later used to enable parametric control and variation for the urban fabric diagram. Finally, the resulting diagrams are translated to 3D geometric entities using computer vision techniques and geometric modeling. The diagrams generated by USS suggest that a learning-based method can be an alternative to methods that rely on experts to build rule sets or parametric models to grasp the morphological qualities of the urban fabric.
keywords	Deep Learning; Urban Fabric; Generative Design; Artificial Intelligence; Urban Morphology
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	cf2019_003
id	cf2019_003
authors	Steinfeld, Kyle; Katherine Park, Adam Menges and Samantha Walker
year	2019
title	Fresh Eyes A framework for the application of machine learning to generative architectural design, and a report of activities at Smartgeometry 2018
source	Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, p. 22
summary	This paper presents a framework for the application of Machine Learning (ML) to Generative Architectural Design (GAD), and illustrates this framework through a description of a series of projects completed at the Smart Geometry conference in May of 2018 (SG 2018) in Toronto. Proposed here is a modest modification of a 3-step process that is well-known in generative architectural design, and that proceeds as: generate, evaluate, iterate. In place of the typical approaches to the evaluation step, we propose to employ a machine learning process: a neural net trained to perform image classification. This modified process is different enough from traditional methods as to warrant an adjustment of the terms of GAD. Through the development of this framework, we seek to demonstrate that generative evaluation may be seen as a new locus of subjectivity in design.
keywords	Machine Learning, Generative Design, Design Methods
series	CAAD Futures
email
last changed	2019/07/29 14:08

_id	acadia19_16
id	acadia19_16
authors	Hosmer, Tyson; Tigas, Panagiotis
year	2019
title	Deep Reinforcement Learning for Autonomous Robotic Tensegrity (ART)
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. 16-29
doi	https://doi.org/10.52842/conf.acadia.2019.016
summary	The research presented in this paper is part of a larger body of emerging research into embedding autonomy in the built environment. We develop a framework for designing and implementing effective autonomous architecture defined by three key properties: situated and embodied agency, facilitated variation, and intelligence.We present a novel application of Deep Reinforcement Learning to learn adaptable behaviours related to autonomous mobility, self-structuring, self-balancing, and spatial reconfiguration. Architectural robotic prototypes are physically developed with principles of embodied agency and facilitated variation. Physical properties and degrees of freedom are applied as constraints in a simulated physics-based environment where our simulation models are trained to achieve multiple objectives in changing environments. This holistic and generalizable approach to aligning deep reinforcement learning with physically reconfigurable robotic assembly systems takes into account both computational design and physical fabrication. Autonomous Robotic Tensegrity (ART) is presented as an extended case study project for developing our methodology. Our computational design system is developed in Unity3D with simulated multi-physics and deep reinforcement learning using Unity’s ML-agents framework. Topological rules of tensegrity are applied to develop assemblies with actuated tensile members. Single units and assemblies are trained for a series of policies using reinforcement learning in single-agent and multi-agent setups. Physical robotic prototypes are built and actuated to test simulated results.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:50

_id	acadia20_382
id	acadia20_382
authors	Hosmer, Tyson; Tigas, Panagiotis; Reeves, David; He, Ziming
year	2020
title	Spatial Assembly with Self-Play Reinforcement Learning
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 382-393.
doi	https://doi.org/10.52842/conf.acadia.2020.1.382
summary	We present a framework to generate intelligent spatial assemblies from sets of digitally encoded spatial parts designed by the architect with embedded principles of prefabrication, assembly awareness, and reconfigurability. The methodology includes a bespoke constraint-solving algorithm for autonomously assembling 3D geometries into larger spatial compositions for the built environment. A series of graph-based analysis methods are applied to each assembly to extract performance metrics related to architectural space-making goals, including structural stability, material density, spatial segmentation, connectivity, and spatial distribution. Together with the constraint-based assembly algorithm and analysis methods, we have integrated a novel application of deep reinforcement (RL) learning for training the models to improve at matching the multiperformance goals established by the user through self-play. RL is applied to improve the selection and sequencing of parts while considering local and global objectives. The user’s design intent is embedded through the design of partial units of 3D space with embedded fabrication principles and their relational constraints over how they connect to each other and the quantifiable goals to drive the distribution of effective features. The methodology has been developed over three years through three case study projects called ArchiGo (2017–2018), NoMAS (2018–2019), and IRSILA (2019-2020). Each demonstrates the potential for buildings with reconfigurable and adaptive life cycles.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2019_650
id	caadria2019_650
authors	Papasotiriou, Tania
year	2019
title	Identifying the Landscape of Machine Learning-Aided Architectural Design - A Term Clustering and Scientometrics Study
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. 815-824
doi	https://doi.org/10.52842/conf.caadria.2019.2.815
summary	Recent advances in Machine Learning and Deep Learning revolutionise many industry disciplines and underpin new ways of problem-solving. This paradigm shift hasn't left Architecture unaffected. To investigate the impact on architectural design, this study utilises two approaches. First, a text mining method for content analysis is employed, to perform a robust review of the field's literature. This allows identifying and discussing current trends and possible future directions of this research domain in a systematic manner. Second, a Scientometrics study based on bibliometric reviews is employed to obtain quantitative measures of the global research activity in the described domain. Insights on research trends and identification of the most influential networks in this dataset were acquired by analysing terms co-occurrence, scientific collaborations, geographic distribution, and co-citation analysis. The paper concludes with a discussion on the limitations, opportunities and future research directions in the field of Machine Learning-aided architectural design.
keywords	Machine Learning; Text mining; Scientometrics
series	CAADRIA
email
last changed	2022/06/07 08:00

_id	acadia19_596
id	acadia19_596
authors	Anton, Ana; Yoo, Angela; Bedarf, Patrick; Reiter, Lex; Wangler, Timothy; Dillenburger, Benjamin
year	2019
title	Vertical Modulations
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. 596-605
doi	https://doi.org/10.52842/conf.acadia.2019.596
summary	The context of digital fabrication allows architects to reinvestigate material, process and the design decisions they entail to explore novel expression in architecture. This demands a new approach to design thinking, as well as the relevant tools to couple the form of artefacts with the process in which they are made. This paper presents a customised computational design tool developed for exploring the novel design space of Concrete Extrusion 3D Printing (CE3DP), enabling a reinterpretation of the concrete column building typology. This tool allows the designer to access generative engines such as trigonometric functions and mesh subdivision through an intuitive graphical user interface. Balancing process efficiency as understood by our industry with a strong design focus, we aim to articulate the unique architectural qualities inherent to CE3DP, energising much needed innovation in concrete technology.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:54

_id	acadia19_80
id	acadia19_80
authors	Bouayad, Ghali
year	2019
title	Three-Dimensional Translation of Japanese Katagami Patterns
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
doi	https://doi.org/10.52842/conf.acadia.2019.080
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	ijac202119302
id	ijac202119302
authors	BuHamdan, Samer; Alwisy, Aladdin; Bouferguene, Ahmed
year	2021
title	Generative systems in the architecture, engineering and construction industry: A systematic review and analysis
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 3, 226–249
summary	Researchers have been extensively exploring the employment of generative systems to support design practices in the architecture, engineering and construction industry since the 1970s. More than half a century passed since the first architecture, engineering and construction industry’s generative systems were developed; researchers have achieved remarkable leaps backed by advances in computing power and algorithms’ capacity. In this article, we present a systematic analysis of the literature published between 2009 and 2019 on the utilization of generative systems in the design practices of the architecture, engineering and construction industry. The present research studies present trends, collaborations and applications of generative systems in the architecture, engineering and construction industry in order to identify existing shortcomings and potential advancements that balance the need for theory development and practical application. It provides insightful observations that are translated into meaningful recommendations for future research necessary to progress the incorporation of generative systems into the design practices of the architecture, engineering and construction industry.
keywords	Generative systems, architecture, engineering and construction industry, performative design, generative design, systematic literature review, future directions
series	journal
email
last changed	2024/04/17 14:29

_id	cf2019_069
id	cf2019_069
authors	Caetano, Inês ;and António Leitão
year	2019
title	Weaving Architectural Façades: Exploring algorithmic stripe-based design patterns
source	Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, pp. 565-584
summary	With the recent technological developments, particularly, the integration of computational design approaches in architecture, the traditional art techniques became increasingly important in the field. This includes weaving techniques, which have a promising application in architectural screens and façade designs. Nevertheless, the adoption of weaving as a design strategy still has many unexplored areas, particularly those related to Algorithmic Design (AD). This paper addresses the creation of weave-based façade patterns by presenting a Generative System (GS) that aids architects that intend to use AD in the design of façades inspired on traditional weaving techniques. This GS proves to reduce the time and effort spent with the programming task, while supporting the exploration of a wider solution space. Moreover, in addition to enabling the integration of user-generated weaving patterns, the GS also provides rationalization algorithms to assess the construction feasibility of the obtained solutions.
keywords	Algorithmic Design, Façade Design, Weaving Patterns, Algorithmic Framework, Rationalization Processes
series	CAAD Futures
type	normal paper
email
last changed	2019/07/29 14:19

_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
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
doi	https://doi.org/10.52842/conf.caadria.2019.1.291
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	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
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
doi	https://doi.org/10.52842/conf.caadria.2019.1.215
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
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
doi	https://doi.org/10.52842/conf.ecaade.2019.2.369
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_516
id	ecaadesigradi2019_516
authors	Fioravanti, Antonio and Trento, Armando
year	2019
title	Close Future: Co-Design Assistant - How Proactive design paradigm can help
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. 155-160
doi	https://doi.org/10.52842/conf.ecaade.2019.1.155
summary	The present paper is focused on exploring a new paradigm in architectural design process that should raise the bar for a mutual collaboration between humans and digital assistants, able to face challenging problems of XXI century. Such a collaboration will aid design process freeing designer from middle level reasoning tasks, so they could focus on exploring - on the fly - design alternatives at a higher abstraction layer of knowledge. Such an assistant should explore and instantiate as much as possible knowledge structures and their inferences thanks to an extensive use of defaults, demons and agents, combined with its power and ubiquity so that they will be able to mimic behaviour of architectural design human experts. It aims other than to deal with data (1st layer) and simple reasoning tools (2nd layer) to automate design exploring consequences and side effects of design decisions and comparing goals (3rd layer). This assistant will speed up the evaluation of fresh design solutions, will suggest solutions by means of generative systems and will be able of a digital creativity.
keywords	Design process paradigm; Architectural design; Design assistant; Agents; Knowledge structures
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:50

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