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	caadria2023_446
id	caadria2023_446
authors	Guida, George
year	2023
title	Multimodal Architecture: Applications of Language in a Machine Learning Aided Design Process
doi	https://doi.org/10.52842/conf.caadria.2023.2.561
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 561–570
summary	Recent advances in Natural Language Processing (NLP) and Diffusion Models (DMs) are leading to a significant change in the way architecture is conceived. With capabilities that surpass those of current generative models, it is now possible to produce an unlimited number of high-quality images (Dhariwal and Nichol 2021). This opens up new opportunities for using synthetic images and marks a new phase in the creation of multimodal 3D forms, central to architectural concept design stages. Presented here are three methodologies of generation of meaningful 2D and 3D designs, merging text-to-image diffusion models Stable Diffusion, and DALL-E 2 with computational methods. These allow designers to intuitively navigate through a multimodal feedback loop of information originating from language and aided by artificial intelligence tools. This paper contributes to our understanding of machine-augmented design processes and the importance of intuitive user interfaces (UI) in enabling new dialogues between humans and machines. Through the creation of a prototype of an accessible UI, this exchange of information can empower designers, build trust in these tools, and increase control over the design process.
keywords	Machine Learning, Diffusion Models, Concept Design, Semantics, User Interface, Design Agency
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	ijac202119313
id	ijac202119313
authors	Saldana Ochoa, Karla; Ohlbrock, Patrick Ole; D’Acunto, Pierluigi; Moosavi, Vahid
year	2021
title	Beyond typologies, beyond optimization: Exploring novel structural forms at the interface of human and machine intelligence
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 3, 466–490
summary	This article presents a computer-aided design framework for the generation of non-standard structural forms in static equilibrium that takes advantage of the interaction between human and machine intelligence. The design framework relies on the implementation of a series of operations (generation, clustering, evaluation, selection, and regeneration) that allow to create multiple design options and to navigate in the design space according to objective and subjective criteria defined by the human designer. Through the interaction between human and machine intelligence, the machine can learn the nonlinear correlation between the design inputs and the design outputs preferred by the human designer and generate new options by itself. In addition, the machine can provide insights into the structural performance of the generated structural forms. Within the proposed framework, three main algorithms are used: Combinatorial Equilibrium Modeling for generating of structural forms in static equilibrium as design options, Self-Organizing Map for clustering the generated design options, and Gradient-Boosted Trees for classifying the design options. These algorithms are combined with the ability of human designers to evaluate non-quantifiable aspects of the design. To test the proposed framework in a real-world design scenario, the design of a stadium roof is presented as a case study.
keywords	Structural design, machine learning, topology, graphic statics, form-finding, Combinatorial Equilibrium Modeling, Self-Organizing Map, Gradient-Boosted Trees
series	journal
email
last changed	2024/04/17 14:29

_id	ecaade2021_237
id	ecaade2021_237
authors	Sönmez, Ayça and Gönenç Sorguç, Arzu
year	2021
title	Computer-Aided Fabrication Technologies as Computational Design Mediators
doi	https://doi.org/10.52842/conf.ecaade.2021.1.465
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 465-474
summary	The developments in recent technologies through Industry 4.0 lead to the integration of digital design and manufacturing processes. Albeit manufacturing continues to increase its importance as design input, it is generally considered at the last stages of the design process. This misconception results in a gap between digital design and fabrication, leading to differences between the initial design and the fabricated outcome in the context of architectural tectonics. Here, we present an artificial intelligence (AI)-based approach that aims to provide a basis to bridge the gap between computation and fabrication. We considered a case study of a 3D model in two stages. In the first stage, an intuitive and top-down design process is adopted, and in the second stage, an AI-based exploration is conducted with three cases derived from the same 3D model. The outcomes of the two stages provided a dataset including different design parameters to be used in a decision tree classifier algorithm which selects the manufacturing method for a given 3D model. Our results show that generative design simulations based on manufacturing constraints can provide a significant variety of manufacturable design alternatives, and minimizes the difference between design alternatives. Using our proposed approach, the time spent in form-finding and fabrication can be reduced significantly. Additionally, the implementation of decision tree classifier learning algorithm shows that AI can serve designers to make accurate predictions for manufacturing method.
keywords	Generative Design; Computer-Aided Fabrication; Arcihtecture 4.0; Artificial Intelligence; Digital Tectonics
series	eCAADe
email
last changed	2022/06/07 07:56

_id	ijac202119106
id	ijac202119106
authors	Del Campo, Matias; Alexandra Carlson, and Sandra Manninger
year	2021
title	Towards Hallucinating Machines - Designing with Computational Vision
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 1, 88–103
summary	There are particular similarities in how machines learn about the nature of their environment, and how humans learn to process visual stimuli. Machine Learning (ML), more specifically Deep Neural network algorithms rely on expansive image databases and various training methods (supervised, unsupervised) to “make sense” out of the content of an image. Take for example how students of architecture learn to differentiate various architectural styles. Whether this be to differentiate between Gothic, Baroque or Modern Architecture, students are exposed to hundreds, or even thousands of images of the respective styles, while being trained by faculty to be able to differentiate between those styles. A reversal of the process, striving to produce imagery, instead of reading it and understanding its content, allows machine vision techniques to be utilized as a design methodology that profoundly interrogates aspects of agency and authorship in the presence of Artificial Intelligence in architecture design. This notion forms part of a larger conversation on the nature of human ingenuity operating within a posthuman design ecology. The inherent ability of Neural Networks to process large databases opens up the opportunity to sift through the enormous repositories of imagery generated by the architecture discipline through the ages in order to find novel and bespoke solutions to architectural problems. This article strives to demystify the romantic idea of individual artistic design choices in architecture by providing a glimpse under the hood of the inner workings of Neural Network processes, and thus the extent of their ability to inform architectural design.The approach takes cues from the language and methods employed by experts in Deep Learning such as Hallucinations, Dreaming, Style Transfer and Vision. The presented approach is the base for an in-depth exploration of its meaning as a cultural technique within the discipline. Culture in the extent of this article pertains to ideas such as the differentiation between symbolic and material cultures, in which symbols are defined as the common denominator of a specific group of people.1 The understanding and exchange of symbolic values is inherently connected to language and code, which ultimately form the ingrained texture of any form of coded environment, including the coded structure of Neural Networks.A first proof of concept project was devised by the authors in the form of the Robot Garden. What makes the Robot Garden a distinctively novel project is the motion from a purely two dimensional approach to designing with the aid of Neural Networks, to the exploration of 2D to 3D Neural Style Transfer methods in the design process.
keywords	Artificial intelligence, design agency, neural networks, machine learning, machine vision
series	journal
email
last changed	2021/06/03 23:29

_id	caadria2021_118
id	caadria2021_118
authors	Huang, Chien-hua
year	2021
title	Reinforcement Learning for Architectural Design-Build - Opportunity of Machine Learning in a Material-informed Circular Design Strategy
doi	https://doi.org/10.52842/conf.caadria.2021.1.171
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. 171-180
summary	This paper discusses the potentials of reinforcement learning in game engine for design, implementation, and construction of architecture. It inaugurates a new design tool that promotes a material-informed design-build workflow for architectural design and construction industries that achieves a comprehensive circular economy. As a proof of concept, it uses the project Reform Standard, a machine-learning-based searching system that designs new shell structures composed of existing wasted materials, as a demonstration to discuss how reinforcement learning, machine vision and automated searching algorithm in the game engine can promote a material-aware design and converts wastes into construction materials. The demonstrator project sorts and transforms irregular chunks of wasted broken plastics into a new form. Instead of recycling those wastes in an energy-intensive process, the game engine is capable of finding the intricacy and new machine-oriented aesthetics in those otherwise neglected wastes. Furthermore, future research directions such as robotic-aided construction are discussed by exposing the potentials and problems in the demonstrated project. Finally, the future circular strategy is discussed beyond the demonstrated tests and local uses. The standardization of material, legislation and material lifecycle needs to be comprehensively considered and designed by architects and designers during conceptual design phase.
keywords	Reinforcement Learning; ML-Agents; Unity3D; circular design; geometric analysis
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	acadia21_48
id	acadia21_48
authors	Nahmad Vazquez, Alicia; Chen, Li
year	2021
title	Automated Generation of Custom Fit PPE Inserts
doi	https://doi.org/10.52842/conf.acadia.2021.048
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 48-57.
summary	This research presents a machine learning-based interactive design method for the creation of customized inserts that improve the fit of the PPE 3M 1863 and 3M 8833 respiratory face masks. These two models are the most commonly used by doctors and professionals during the recent covid19 pandemic. The proper fit of the mask is crucial for their performance. Characteristics and fit of current leading market brands were analyzed to develop a parametric design software workflow that results in a 3D printed insert customized to specific facial features and the mask that will be used. The insert provides a perfect fit for the respirator mask. Statistical face meshes were generated from an anthropometric database, and 3D facial scans and photos were taken from 200 doctors and nurses on an NHS trust hospital. The software workflow can start from either a 2D image of the face (picture) or a 3D mesh taken from a scanning device. The platform uses machine learning and a parametric design workflow based on key performance facial parameters to output the insert between the face and the 3M masks. It also generates the 3d printing file, which can be processed onsite at the hospital. The 2D image approach and the 3D scan approach initializing the system were digitally compared, and the resultant inserts were physically tested by 20 frontline personnel in an NHS trust hospital. Finally, we demonstrate the criticality of proper fit on masks for doctors and nurses and the versatility of our approach augmenting an already tested product through customized digital design and fabrication.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2023_259
id	ecaade2023_259
authors	Sonne-Frederiksen, Povl Filip, Larsen, Niels Martin and Buthke, Jan
year	2023
title	Point Cloud Segmentation for Building Reuse - Construction of digital twins in early phase building reuse projects
doi	https://doi.org/10.52842/conf.ecaade.2023.2.327
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 327–336
summary	Point cloud processing has come a long way in the past years. Advances in computer vision (CV) and machine learning (ML) have enabled its automated recognition and processing. However, few of those developments have made it through to the Architecture, Engineering and Construction (AEC) industry. Here, optimizing those workflows can reduce time spent on early-phase projects, which otherwise could be spent on developing innovative design solutions. Simplifying the processing of building point cloud scans makes it more accessible and therefore, usable for design, planning and decision-making. Furthermore, automated processing can also ensure that point clouds are processed consistently and accurately, reducing the potential for human error. This work is part of a larger effort to optimize early-phase design processes to promote the reuse of vacant buildings. It focuses on technical solutions to automate the reconstruction of point clouds into a digital twin as a simplified solid 3D element model. In this paper, various ML approaches, among others KPConv Thomas et al. (2019), ShapeConv Cao et al. (2021) and Mask-RCNN He et al. (2017), are compared in their ability to apply semantic as well as instance segmentation to point clouds. Further it relies on the S3DIS Armeni et al. (2017), NYU v2 Silberman et al. (2012) and Matterport Ramakrishnan et al. (2021) data sets for training. Here, the authors aim to establish a workflow that reduces the effort for users to process their point clouds and obtain object-based models. The findings of this research show that although pure point cloud-based ML models enable a greater degree of flexibility, they incur a high computational cost. We found, that using RGB-D images for classifications and segmentation simplifies the complexity of the ML model but leads to additional requirements for the data set. These can be mitigated in the initial process of capturing the building or by extracting the depth data from the point cloud.
keywords	Point Clouds, Machine Learning, Segmentation, Reuse, Digital Twins
series	eCAADe
email
last changed	2023/12/10 10:49

_id	caadria2021_148
id	caadria2021_148
authors	Hou, Yuhan and Loh, Paul
year	2021
title	Towards Swarm Construction
doi	https://doi.org/10.52842/conf.caadria.2021.1.673
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. 673-682
summary	Swarm intelligence has primarily been explored in architecture as a form-finding technique with resulting material articulation using advanced 3d-printing technology. Researchers in engineering have developed swarm robotics for construction and fabrication, typically constraints to small scale prototypes as the technology matures within the field. However, a few research explores the implication of swarm robotics for construction on the building or urban scale. This paper presents a novel swarm robotics construction method using mole-like digging technology to construct new architectural language using machine intelligence. The research discusses the role of swarm intelligence behaviours in design and synthesis such behaviour with machine logics. The paper addresses the conference theme through the speculative projection of future construction methodology and reflects on how automation can impact the future of construct and design.
keywords	Swarm; Digital Fabrication; Robotic
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	acadia21_112
id	acadia21_112
authors	Kahraman, Ridvan; Zechmeister, Christoph; Dong, Zhetao; Oguz, Ozgur S.; Drachenberg, Kurt; Menges, Achim; Rinderspacher, Katja
year	2021
title	Augmenting Design
doi	https://doi.org/10.52842/conf.acadia.2021.112
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 112-121.
summary	In recent years, generative machine learning methods such as variational autoencoders (VAEs) and generative adversarial networks (GANs) have opened up new avenues of exploration for architects and designers. The presented work explores how these methods can be expanded by incorporating multiple abstract criteria directly into the formulation of the algorithm that negotiates these complex criteria and proposes a fitting design. It draws inspiration from the works of several design theorists who have developed such goal-oriented approaches to design, and sets up multiple-objective VAE and GAN frameworks with this idea in mind. The research demonstrates that by incorporating multiple constraints using auxiliary discriminator networks, the developed algorithms are able to generate innovative solutions to two example problems: the design of 2D digits, and the design of 3D voxel chairs. By speculating and examining the role of the designer in data based generative computational design workflows, the research aims to provide an approach for solving design tasks in the age of big data.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	sigradi2021_200
id	sigradi2021_200
authors	Karabagli, Kaan, Koc, Mustafa, Basu, Prithwish and As, Imdat
year	2021
title	A Machine Learning Approach to Translate Graph Representations into Conceptual Massing Models
source	Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 191–202
summary	Machine learning (ML) has popular applications in domains involving image, video, text and voice. However, in architecture, image-based ML systems face challenges capturing the complexity of three-dimensional space. In this paper, we leverage a graph-based ML system that can capture the inherent topology of architectural conceptual designs and identify high-performing latent patterns within such designs. In particular, our goal is to translate architectural graph data into three-dimensional massing models. We are building on our prior ML work, where we, a. discovered latent topological features, b. composed building blocks into new designs, c. evaluated their feasibility, and d. explored Generative Adversarial (Neural) Networks (GAN)-generated design variations. We trained the ML system with architectural design data that we gathered from an online architectural design competition platform, translated them into machine-readable graph representations, and identified their essential subgraphs to develop novel compositions. In this paper, we explore how these novel designs (outputted in graph form), can be translated into three-dimensional architectural form. We present an ML approach to turn graph representations into functional volumetric massing models. The ultimate goal of the study is to develop an end-to-end pipeline to generate architectural design - from a graph representation to a fully developed conceptual proxy of a designed product. The research question is promising in automating conceptual design, and we believe the outcome can be relevant to other design disciplines as well.
keywords	Architectural design, machine learning, conceptual design, deep learning, artificial intelligence
series	SIGraDi
email
last changed	2022/05/23 12:10

_id	caadria2021_220
id	caadria2021_220
authors	MacDonald, Katie and Schumann, Kyle
year	2021
title	Twinned Assemblage - Curating and Distilling Digital Doppelgangers
doi	https://doi.org/10.52842/conf.caadria.2021.1.693
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. 693-702
summary	Recent developments in digital fabrication have made increasingly intelligent use of machine visioning and 3D scanning. These technologies enable ever-higher resolution digital models of physical material, and present opportunities for physical material to gain agency in the design process. Digital design workflows using such technologies require ever-greater computing power as the resolution of digitized models increases, and high-fidelity 3D scanning systems become cost-prohibitive, creating obstacles to widespread use. Twinned assemblage uses consumer-grade photogrammetry software, lowering the cost of equipment required, and presents a series of distillation methods that strategically reduce the fidelity of data digitally describing a physical object. Distillation methods discussed include reducing a mesh to a low-poly geometry, identifying the location and orientation of an object's largest faces, and creating 2D sections, among others. These methods can be designed intentionally to extract or highlight certain qualities in digital models, that in turn inform aggregation strategies generated through computational simulation. This paper presents several examples of such aggregations in a variety of materials, conveying benefits and challenges of the process. Such methods present opportunities for granting agency to physical materials in the design process, and for the democratized use of digitizing technologies.
keywords	Authorship; Digitizing; Material Agency; Digital Design; Democratized Technology
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	ecaade2021_194
id	ecaade2021_194
authors	Scott, Jane, Gaston, Elizabeth and Agraviador, Armand
year	2021
title	Configured Knitting - Grafting as an assembly process for knitted architecture
doi	https://doi.org/10.52842/conf.ecaade.2021.2.473
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. 473-482
summary	There is a growing interest in knit as a material system for architectural research in a workflow that integrates computation and digital fabrication in the design and specification of highly engineered fabrics. However, the dimensional limitations of industrial machines mean that large scale work may require assembly from multiple pieces. Reconfiguring knitted fabric by joining fabric panels disrupts the performance of the material, challenging the computational model when fabric characteristics are transformed at the seams.The aim of this research is to evaluate the potential for grafting, a traditional joining method for knitted fabric, as an assembly technique for architectural scale knitted prototypes. The paper presents an overview of knitted loop geometry focusing on the impact of loop construction in textile joins. The paper presents experimental research conducted using unconventional off-machine techniques at two scales, demonstrating how grafting can be used to assemble 3D structures without compromising the integrity of the material. Findings highlight the significance of this technique and suggest how the work could translate to digital fabrication.
keywords	Knit; Grafting; Computational Form Generation; Textile Design
series	eCAADe
email
last changed	2022/06/07 07:56

_id	acadia21_170
id	acadia21_170
authors	Xydis, Achilleas; Perraudin, Nathanaël; Rust, Romana; Lytle, Beverly Ann; Gramazio, Fabio; Kohler, Matthias
year	2021
title	Data-Driven Acoustic Design of Diffuse Soundfields
doi	https://doi.org/10.52842/conf.acadia.2021.170
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 170-181.
summary	The paper demonstrates a novel approach to performance-driven acoustic design of architectural diffusive surfaces. It uses unsupervised machine learning techniques to analyze and explore the GIR Dataset, an extensive collection of real impulse responses and acoustically diffusive surfaces. The presented approach enables designers to explore many alternative acoustically-informed material patterns with various diffusive properties without requiring expert knowledge in acoustics. The paper introduces the computational pipeline, describes the used methods, and presents two use-cases in the form of design experiments. Finally, the paper discusses the challenges of developing such a method, its advantages, limitations, and future work.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ascaad2021_118
id	ascaad2021_118
authors	Abdelmohsen, Sherif; Passaint Massoud
year	2021
title	Material-Based Parametric Form Finding: Learning Parametric Design through Computational Making
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 521-535
summary	Most approaches developed to teach parametric design principles in architectural education have focused on universal strategies that often result in the fixation of students towards perceiving parametric design as standard blindly followed scripts and procedures, thus defying the purpose of the bottom-up framework of form finding. Material-based computation has been recently introduced in computational design, where parameters and rules related to material properties are integrated into algorithmic thinking. In this paper, we discuss the process and outcomes of a computational design course focused on the interplay between the physical and the digital. Two phases of physical/digital exploration are discussed: (1) physical exploration with different materials and fabrication techniques to arrive at the design logic of a prototype panel module, and (2) deducing and developing an understanding of rules and parameters, based on the interplay of materials, and deriving strategies for pattern propagation of the panel on a façade composition using variation and complexity. The process and outcomes confirmed the initial hypothesis, where the more explicit the material exploration and identification of physical rules and relationships, the more nuanced the parametrically driven process, where students expressed a clear goal oriented generative logic, in addition to utilizing parametric design to inform form finding as a bottom-up approach.
series	ASCAAD
email
last changed	2021/08/09 13:13

_id	caadria2021_157
id	caadria2021_157
authors	Huang, Xiaoran, Kimm, Geoff and Burry, Mark
year	2021
title	Exploiting game development environments for responsive urban design by non-programmers - melding real-time ABM pedestrian simulation and form modelling in Unity 3D
doi	https://doi.org/10.52842/conf.caadria.2021.2.689
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 689-698
summary	Precinct-level pedestrian simulation often requires moderate to high-level modelling skills with a steep learning curve, and is usually non-flexible, time-consuming and exclusive of the broader public community. Confronting these problems, our research investigates a novel and agile workflow to test precinct pedestrian behaviours by melding agent-based simulation (ABM) and responsive real-time form modelling mechanisms within accessible visualisation of city and precinct environments in a game engine, Unity 3D. We designed an agent system prototype of configurable and interoperable nodes that may be placed in an urban modelling scenario. Realtime CSG, a fast polygon-based modelling plugin, is also introduced to our workflow where users can use the evidence observed when running a scenario to quickly adjust the street morphology and buildings in response. In this process, end users are kept in the design loop and may make critical adjustments, whereby a responsive, collective, informed design agenda for our built environments can inform more detailed outcomes of pedestrian behaviour and action and promote more efficient collaborations for both professionals and local communities.
keywords	Agent-based pedestrian simulation; responsive modelling; computer-aided urban design; public participation
series	CAADRIA
email
last changed	2022/06/07 07:49

_id	acadia21_362
id	acadia21_362
authors	Bruscia, Nicholas
year	2021
title	Surface Disclination Topology in Self-Reactive Shell Structures
doi	https://doi.org/10.52842/conf.acadia.2021.362
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 362-371.
summary	This paper discusses recent developments on the geometric construction and fabrication techniques associated with large-scale surface disclinations. The basic concept of disclinations recognizes the role of “defects” in the composition of materials, the strategic placement of which shapes the material by inducing curvature from initially planar elements. By acknowledging the relationship between geometry and topology that governs disclination based form-finding and material prototyping, this work consciously explores its potential at the architectural scale. Basic geometric figures and their topological transformations are documented in the context of digital modeling and simulation, fabrication, and a specific material palette. Specifically, this work builds on recent efforts by focusing on three particular areas of investigation; a) enhancing the stability of surface disclinations with a synthetic fibrous layer, b) aggregation via periodic tilings, and c) harnessing snap-through buckling to increase bending stiffness in thin surfaces.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ijac202119101
id	ijac202119101
authors	Budig, Michael; Oliver Heckmann, Markus, Hudert, Amanda Qi Boon Ng, Zack Xuereb Conti, and Clement Jun Hao Lork
year	2021
title	Computational screening-LCA tools for early design stages
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 1, 6–22
summary	Life Cycle Assessment (LCA) has been widely adopted to identify the Global Warming Potential (GWP) in the construction industry and determine its high environmental impact through Greenhouse Gas (GHG) emissions, energy and resource consumptions. The consideration of LCA in the early stages of design is becoming increasingly important as a means to avoid costly changes at later stages of the project. However, typical LCA-based tools demand very detailed information about structural and material systems and thus become too laborious for designers in the conceptual stages, where such specifications are still loosely defined. In response, this paper presents a workflow for LCA-based evaluation where the selection of the construction system and material is kept open to compare the impacts of alternative design variants. We achieve this through a strict division into support and infill systems and a simplified visualization of a schematic floor layout using a shoebox approach, inspired from the energy modelling domain. The shoeboxes in our case are repeatable modules within a schematic floor plan layout, whose enclosures are defined by parametric 2D surfaces representing total ratios of permanent supports versus infill components. Thus, the assembly of modular surface enclosures simplifies the LCA evaluation process by avoiding the need to accurately specify the physical properties of each building component across the floor plan. The presented workflow facilitates the selection of alternative structural systems and materials for their comparison, and outputs the Global Warming Potential (GWP) in the form of an intuitive visualization output. The workflow for simplified evaluation is illustrated through a case study that compares the GWP for selected combinations of material choice and construction systems.
keywords	Computational life cycle assessment tool, embodied carbon, parametric design, construction systems, global warming potential
series	journal
email
last changed	2021/06/03 23:29

_id	caadria2021_137
id	caadria2021_137
authors	Fattahi Tabasi, Saba, Alaghmandan, Matin and Rafizadeh, Hamid Reza
year	2021
title	Simultaneous effect of form modifications and topology of the bracing system on the structural performance of timber high rise building - Introducing an innovative approach using parametric design
doi	https://doi.org/10.52842/conf.caadria.2021.1.421
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. 421-430
summary	Topology optimization is a tool that minimizes the material consumption in a structure, while at the same time provides us design alternatives integrating architectural and structural engineering concepts. However, topology optimization is a structural engineering subject and its known methods are required professional knowledge of engineering to be used. In this article, the mutual effect of form modifications and topology of the bracing system in a 9-story timber exoskeleton high-rise building regarding the governing wind load and seismic load is examined. What differentiates this study from former ones and in fact its main purpose is introducing an innovative approach towards structural topology optimization using parametric design. In this innovative approach, the possibility of moving for each central node of bracing systems in defined ranges independently and the possibility of the existence or absence of each bracing member is provided. This parametric model will enable architects to optimize the topology of the structural elements which are part of their architectural design by themselves. The CMA-ES-algorithm-based optimization is done to minimize both total mass of structure per unit area and the horizontal displacement of the top floor. For modeling, optimizing cross-sections and structural analysis, Grasshopper and its plug-in called Karamba are utilized.
keywords	Topology optimization; Form finding; Parametric design; Timber tall buildings; Exoskeleton structures
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	ascaad2021_063
id	ascaad2021_063
authors	Ronagh, Ehsan; Mohammadjavad Mahdavinejad, Anoosha Kia
year	2021
title	A New Paradigm in Generative Design Linking Parametric Architecture and Music to Form Finding
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 227-240
summary	In recent years, geometry and innovations have become an important topic in contemporary architecture. In addition, the 21st century is considered as a new era in architectural design. Computer software development has introduced the theory of form-finding. The present study proposes a novel design and construction method in form-finding based on the relationship between parametric architecture and music. To achieve this goal, several algorithms were designed. The simulation was performed in Rhino with Grasshopper and Firefly plugins, and extensive prototyping of the shells was performed at High-performance Architecture Lab (HAL). This study is aimed at presenting a new design and construction method as a generative design that can use two main characteristics of sound namely frequency and intensity over time. The design also forms the numerical outputs of the music to deform the modular two-dimensional geometric patterns and transform them into three-dimensional parametric shells. The resulting research is fully applicable at a large scale such as urban landscape and small scale as interior design.
series	ASCAAD
email
last changed	2021/08/09 13:13

_id	ascaad2021_022
id	ascaad2021_022
authors	Baºarir, Lale; Kutluhan Erol
year	2021
title	Briefing AI: From Architectural Design Brief Texts to Architectural Design Sketches
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 23-31
summary	The main focus of this research is to uncover the underlying intuitive knowledge of architecture with the help of machine learning models. To achieve this, a generic architectural design process is considered and divided into iterative portions based on their output for each phase. This study looks into the initial portion of the architectural design process called “Briefing”. The authors search for the intuition that exists within the design process and how it can be learned by artificial intelligence (AI) that is currently gained through master-apprentice relationship and experience that builds up this knowledge. In this study, a way to enable users to attain an architectural design sketch while defining an architectural design problem with text is explored. This on-going research decomposes the components of the briefing and preliminary design sketching processes. Therefore the domain knowledge at each phase is considered for translating to constraints via natural language processing (NLP) and machine learning (ML) models such as Generative Adversarial Networks (GANs).
series	ASCAAD
type	normal paper
email
last changed	2021/08/09 13:11

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