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	ecaadesigradi2019_671
id	ecaadesigradi2019_671
authors	Jabi, Wassim, Chatzivasileiadi, Aikaterini, Wardhana, Nicholas Mario, Lannon, Simon and Aish, Robert
year	2019
title	The synergy of non-manifold topology and reinforcement learning for fire egress
doi	https://doi.org/10.52842/conf.ecaade.2019.2.085
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. 85-94
summary	This paper illustrates the synergy of non-manifold topology (NMT) and a branch of artificial intelligence and machine learning (ML) called reinforcement learning (RL) in the context of evaluating fire egress in the early design stages. One of the important tasks in building design is to provide a reliable system for the evacuation of the users in emergency situations. Therefore, one of the motivations of this research is to provide a framework for architects and engineers to better design buildings at the conceptual design stage, regarding the necessary provisions in emergency situations. This paper presents two experiments using different state models within a simplified game-like environment for fire egress with each experiment investigating using one vs. three fire exits. The experiments provide a proof-of-concept of the effectiveness of integrating RL, graphs, and non-manifold topology within a visual data flow programming environment. The results indicate that artificial intelligence, machine learning, and RL show promise in simulating dynamic situations as in fire evacuations without the need for advanced and time-consuming simulations.
keywords	Non-manifold topology; Topologic; Reinforcement Learning; Fire egress
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:52

_id	ecaadesigradi2019_672
id	ecaadesigradi2019_672
authors	Wardhana, Nicholas Mario, Jabi, Wassim, Chatzivasileiadi, Aikaterini and Petrova, Nikoleta
year	2019
title	A Spatial Reasoning Framework Based on Non-Manifold Topology
doi	https://doi.org/10.52842/conf.ecaade.2019.3.071
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 3, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 71-80
summary	Non-Manifold Topology (NMT) has been previously proven to be an appropriate representation of interconnected architectural and spatial structures. This paper further explores the suitability of NMT for spatial reasoning purposes. A literature survey is done to identify the necessary components of a spatial reasoning framework, and an adaptation of such framework based on NMT is presented. This paper also describes the implementation of an NMT-based spatial reasoning framework, its integration into the Topologic software library which the authors develop, as well as the implementation challenges. Finally, a pathfinding study case on an NMT model, which has been generated from a Building Information Modelling (BIM) structure, is presented and analysed.
keywords	Non-Manifold Topology; Topologic; Spatial Reasoning; Pathfinding
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:58

_id	acadia19_130
id	acadia19_130
authors	Devadass, Pradeep; Heimig, Tobias; Stumm, Sven; Kerber, Ethan; Cokcan, Sigrid Brell
year	2019
title	Robotic Constraints Informed Design Process
doi	https://doi.org/10.52842/conf.acadia.2019.130
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. 130-139
summary	Promising results in efficiently producing highly complex non-standard designs have been accomplished by integrating robotic fabrication with parametric design. However, the project workflow is hampered due to the disconnect between designer and robotic fabricator. The design is most often developed by the designer independently from fabrication process constraints. This results in fabrication difficulties or even non manufacturable components. In this paper we explore the various constraints in robotic fabrication and assembly processes, analyze their influence on design, and propose a methodology which bridges the gap between parametric design and robotic production. Within our research we investigate the workspace constraints of robots, end effectors, and workpieces used for the fabrication of an experimental architectural project: “The Twisted Arch.” This research utilizes machine learning approaches to parameterize, quantify, and analyze each constraint while optimizing how those parameters impact the design output. The research aims to offer a better planning to production process by providing continuous feedback to the designer during early stages of the design process. This leads to a well-informed “manufacturable” design.
keywords	Robotic Fabrication and Assembly, Mobile Robotics, Machine Learning, Parametric Design, Constraint Based Design.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:55

_id	acadia20_192p
id	acadia20_192p
authors	Doyle, Shelby; Hunt, Erin
year	2020
title	Melting 2.0
source	ACADIA 2020: Distributed Proximities / Volume II: Projects [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95253-6]. Online and Global. 24-30 October 2020. edited by M. Yablonina, A. Marcus, S. Doyle, M. del Campo, V. Ago, B. Slocum. 192-197
summary	This project presents computational design and fabrication methods for locating standard steel reinforcement within 3D printed water-soluble PVA (polyvinyl alcohol) molds to create non-standard concrete columns. Previous methods from “Melting: Augmenting Concrete Columns with Water Soluble 3D Printed Formwork” and “Dissolvable 3D Printed Formwork: Exploring Additive Manufacturing for Reinforced Concrete” (Doyle & Hunt 2019) were adapted for larger-scale construction, including the introduction of new hardware, development of custom programming strategies, and updated digital fabrication techniques. Initial research plans included 3D printing continuous PVA formwork with a KUKA Agilus Kr10 R1100 industrial robotic arm. However, COVID-19 university campus closures led to fabrication shifting to the author’s home, and this phase instead relied upon a LulzBot TAZ 6 (build volume of 280 mm x 280 mm x 250 mm) with an HS+ (Hardened Steel) tool head (1.2 mm nozzle diameter). Two methods were developed for this project phase: new 3D printing hardware and custom GCode production. The methods were then evaluated in the fabrication of three non-standard columns designed around five standard reinforcement bars (3/8-inch diameter): Woven, Twisted, Aperture. Each test column was eight inches in diameter (the same size as a standard Sonotube concrete form) and 4 feet tall, approximately half the height of an architecturally scaled 8-foot-tall column. Each column’s form was generated from combining these diameter and height restrictions with the constraints of standard reinforcement placement and minimum concrete coverage. The formwork was then printed, assembled, cast, and then submerged in water to dissolve the molds to reveal the cast concrete. This mold dissolving process limits the applicable scale for the work as it transitions from the research lab to the construction site. Therefore, the final column was placed outside with its mold intact to explore if humidity and water alone can dissolve the PVA formwork in lieu of submersion.
series	ACADIA
type	project
email
last changed	2021/10/26 08:08

_id	acadia19_16
id	acadia19_16
authors	Hosmer, Tyson; Tigas, Panagiotis
year	2019
title	Deep Reinforcement Learning for Autonomous Robotic Tensegrity (ART)
doi	https://doi.org/10.52842/conf.acadia.2019.016
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
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
doi	https://doi.org/10.52842/conf.acadia.2020.1.382
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.
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	ecaadesigradi2019_346
id	ecaadesigradi2019_346
authors	Kaftan, Martin, Sautter, Sebastian and Kubicek, Bernhard
year	2019
title	Integrating BIPV during Early Stages of Building Design
doi	https://doi.org/10.52842/conf.ecaade.2019.2.139
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. 139-144
summary	In the quest to achieve the ambitious climate and clean energy targets the broad implementation of Integrated Photovoltaics (BIPV) is one of the keys. Photovoltaic (PV) modules can be installed above or on current roofing or traditional wall structures. In addition, BIPV devices substitute the skin of the exterior construction frame, i.e. the weather screen, thus simultaneously acting as both a climate screen and an energy producing source. However, while the integral planning strategy to building projects promotes the effective execution of BIPV, the limitation lies in the absence of both instruments and easy-to-use planning aid guidelines, particularly by non-PV experts in the early design stage. This study presents computational methods that help to quickly analyze the BIPV potential for a given building project and to suggest the optimal economical amount and location of the panels based on the building's energy demand profile.
keywords	building integrated photovoltaic (BIPV); integral planning; design rules; simplified models; machine learning
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:52

_id	ecaadesigradi2019_280
id	ecaadesigradi2019_280
authors	Rossi, Gabriella and Nicholas, Paul
year	2019
title	Haptic Learning - Towards Neural-Network-based adaptive Cobot Path-Planning for unstructured spaces
doi	https://doi.org/10.52842/conf.ecaade.2019.2.201
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. 201-210
summary	Collaborative Robots, or Cobots, bring new possibilities for human-machine interaction within the fabrication process, allowing each actor to contribute with their specific capabilities. However creative interaction brings unexpected changes, obstacles, complexities and non-linearities which are encountered in real time and cannot be predicted in advance. This paper presents an experimental methodology for robotic path planning using Machine Learning. The focus of this methodology is obstacle avoidance. A neural network is deployed, providing a relationship between the robot's pose and its surroundings, thus allowing for motion planning and obstacle avoidance, directly integrated within the design environment. The method is demonstrated through a series of case-studies. The method combines haptic teaching with machine learning to create a task specific dataset, giving the robot the ability to adapt to obstacles without being explicitly programmed at every instruction. This opens the door to shifting to robotic applications for construction in unstructured environments, where adapting to the singularities of the workspace, its occupants and activities presents an important computational hurdle today.
keywords	Architectural Robotics; Neural Networks; Path Planning; Digital Fabrication; Artificial Intelligence; Data
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:56

_id	caadria2019_464
id	caadria2019_464
authors	Scott, Sophie, Doherty, Ben, Fabbri, Alessandra, Gardner, Nicole and Haeusler, M. Hank
year	2019
title	Discoverable Desks - Finding location and orientation in a mobile workplace
doi	https://doi.org/10.52842/conf.caadria.2019.2.653
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. 653-662
summary	The drive towards increasing productivity through collaborative ways of working has spurred a parallel trend in flexible and adaptable workplace environments. Mobile desks are one feasible solution to this but workplaces that adopt mobile desks risk creating spatial inefficiencies. These range from overcrowding or underutilization, to potential compliance issues in terms of fire egress requirements and health and safety regulations. While there is a need to understand mobile desking configurations there are currently no well-established ways to track the location and orientation of mobile desks within workplaces. Consequently, this paper describes a research project that adopts an action research methodology as an iterative and participatory framework to investigate and develop a unique method for capturing the location and orientation of freely moveable desks in an open workplace environment. This uses an ensemble of Bluetooth location beacons and computer vision techniques to provide a finer resolution than either method alone can currently provide. The demonstration of this ensemble method is the main contribution of this work. This paper demonstrates that combining these methods can enhance the advantages of each; computer vision gives higher resolution and beacons reduce the scope of the image search task
keywords	Indoor Positioning Systems; Office Space Planning; Location Data; Computer vision; activity-based working
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	acadia19_380
id	acadia19_380
authors	Özel, Güvenç; Ennemoser, Benjamin
year	2019
title	Interdisciplinary AI
doi	https://doi.org/10.52842/conf.acadia.2019.380
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. 380- 391
summary	Architecture does not exist in a vacuum. Its cultural, conceptual, and aesthetic agendas are constantly influenced by other visual and artistic disciplines ranging from film, photography, painting and sculpture to fashion, graphic and industrial design. The formal qualities of the cultural zeitgeist are perpetually influencing contemporary architectural aesthetics. In this paper, we aim to introduce a radical yet methodical approach toward regulating the relationship between human agency and computational form-making by using Machine Learning (ML) as a conceptual design tool for interdisciplinary collaboration and engagement. Through the use of a highly calibrated and customized ML systems that can classify and iterate stylistic approaches that exist outside the disciplinary boundaries of architecture, the technique allows for machine intelligence to design, coordinate, randomize, and iterate external formal and aesthetic qualities as they relate to pattern, color, proportion, hierarchy, and formal language. The human engagement in this design process is limited to the initial curation of input data in the form of image repositories of non-architectural disciplines that the Machine Learning system can extrapolate from, and consequently in regulating and choosing from the iterations of images the Artificial Neural Networks are capable of producing. In this process the architect becomes a curator that samples and streamlines external cultural influences while regulating their significance and weight in the final design. By questioning the notion of human agency in the design process and providing creative license to Artificial Intelligence in the conceptual design phase, we aim to develop a novel approach toward human-machine collaboration that rejects traditional notions of disciplinary autonomy and streamlines the influence of external aesthetic disciplines on contemporary architectural production.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:57

_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	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	cf2019_048
id	cf2019_048
authors	Argota Sanchez-Vaquerizo, Javier and Daniel Cardoso Llach
year	2019
title	The Social Life of Small Urban Spaces 2.0 Three Experiments in Computational Urban Studies
source	Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, p. 430
summary	This paper introduces a novel framework for urban analysis that leverages computational techniques, along with established urban research methods, to study how people use urban public space. Through three case studies in different urban locations in Europe and the US, it demonstrates how recent machine learning and computer vision techniques may assist us in producing unprecedently detailed portraits of the relative influence of urban and environmental variables on people’s use of public space. The paper further discusses the potential of this framework to enable empirically-enriched forms of urban and social analysis with applications in urban planning, design, research, and policy.
keywords	Data Analytics, Urban Design, Machine Learning, Artificial Intelligence, Big Data, Space Syntax
series	CAAD Futures
email
last changed	2019/07/29 14:18

_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_290
id	ecaadesigradi2019_290
authors	Assem, Ayman, Abdelmohsen, Sherif and Ezzeldin, Mohamed
year	2019
title	A Fuzzy-Based Approach for Evaluating Existing Spatial Layout Configurations
doi	https://doi.org/10.52842/conf.ecaade.2019.2.035
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 35-44
summary	This paper proposes a fuzzy-based approach for the automated evaluation of spatial layout configurations. Our objective is to evaluate soft and interdependent design qualities (such as connectedness, enclosure, spaciousness, continuity, adjacency, etc.), to satisfy multiple and mutually inclusive criteria, and to account for all potential and logical solutions without discarding preferable, likely or even less likely possible solutions. Using fuzzyTECH, a fuzzy logic software development tool, we devise all possible spatial relation inputs affecting physical and non-physical outputs for a given space using descriptive rule blocks. We implement this fuzzy logic system on an existing residential space to evaluate different layout alternatives. We define all linguistic input variables, output variables, and fuzzy sets, and present space-space relations using membership functions. We use the resulting database of fuzzy agents to evaluate the design of the existing residential spaces.
keywords	Fuzzy logic; Space layout planning; Heuristic methods
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:54

_id	acadia20_202p
id	acadia20_202p
authors	Battaglia, Christopher A.; Verian, Kho; Miller, Martin F.
year	2020
title	DE:Stress Pavilion
source	ACADIA 2020: Distributed Proximities / Volume II: Projects [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95253-6]. Online and Global. 24-30 October 2020. edited by M. Yablonina, A. Marcus, S. Doyle, M. del Campo, V. Ago, B. Slocum. 202-207
summary	Print-Cast Concrete investigates concrete 3D printing utilizing robotically fabricated recyclable green sand molds for the fabrication of thin shell architecture. The presented process expedites the production of doubly curved concrete geometries by replacing traditional formwork casting or horizontal corbeling with spatial concrete arching by developing a three-dimensional extrusion path for deposition. Creating robust non-zero Gaussian curvature in concrete, this method increases fabrication speed for mass customized elements eliminating two-part mold casting by combining robotic 3D printing and extrusion casting. Through the casting component of this method, concrete 3D prints have greater resolution along the edge condition resulting in tighter assembly tolerances between multiple aggregated components. Print-Cast Concrete was developed to produce a full-scale architectural installation commissioned for Exhibit Columbus 2019. The concrete 3D printed compression shell spanned 12 meters in length, 5 meters in width, and 3 meters in height and consisted of 110 bespoke panels ranging in weight of 45 kg to 160 kg per panel. Geometrical constraints were determined by the bounding box of compressed sand mold blanks and tooling parameters of both CNC milling and concrete extrusion. Using this construction method, the project was able to be assembled and disassembled within the timeframe of the temporary outdoor exhibit, produce <1% of waste mortar material in fabrication, and utilize 60% less material to construct than cast-in-place construction. Using the sand mold to contain geometric edge conditions, the Print-Cast technique allows for precise aggregation tolerances. To increase the pavilions resistance to shear forces, interlocking nesting geometries are integrated into each edge condition of the panels with .785 radians of the undercut. Over extruding strategically during the printing process casts the undulating surface with accuracy. When nested together, the edge condition informs both the construction logic of the panel’s placement and orientation for the concrete panelized shell.
series	ACADIA
type	project
email
last changed	2021/10/26 08:08

_id	ijac201917103
id	ijac201917103
authors	Bejarano, Andres; and Christoph Hoffmann
year	2019
title	A generalized framework for designing topological interlocking configurations
source	International Journal of Architectural Computing vol. 17 - no. 1, 53-73
summary	A topological interlocking configuration is an arrangement of pieces shaped in such a way that the motion of any piece is blocked by its neighbors. A variety of interlocking configurations have been proposed for convex pieces that are arranged in a planar space. Published algorithms for creating a topological interlocking configuration start from a tessellation of the plane (e.g. squares colored as a checkerboard). For each square S of one color, a plane P through each edge E is considered, tilted by a given angle ? against the tessellated plane. This induces a face F supported by P and limited by other such planes nearby. Note that E is interior to the face. By adjacency, the squares of the other color have similarly delimiting faces. This algorithm generates a topological interlocking configuration of tetrahedra or antiprisms. When checked for correctness (i.e. for no overlap), it rests on the tessellation to be of squares. If the tessellation consists of rectangles, then the algorithm fails. If the tessellation is irregular, then the tilting angle is not uniform for each edge and must be determined, in the worst case, by trial and error. In this article, we propose a method for generating topological interlocking configurations in one single iteration over the tessellation or mesh using a height value and a center point type for each tile as parameters. The required angles are a function of the given height and selected center; therefore, angle choices are not required as an initial input. The configurations generated using our method are compared against the configurations generated using the angle-choice approach. The results show that the proposed method maintains the alignment of the pieces and preserves the co-planarity of the equatorial sections of the pieces. Furthermore, the proposed method opens a path of geometric analysis for topological interlocking configurations based on non-planar tessellations.
keywords	Topological interlocking, surface tessellation, irregular geometry, parametric design, convex assembly
series	journal
email
last changed	2019/08/07 14:04

_id	cf2019_020
id	cf2019_020
authors	Belém, Catarina; Luís Santos and António Leitão
year	2019
title	On the Impact of Machine Learning: Architecture without Architects?
source	Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, pp. 148-167
summary	Architecture has always followed and adopted technological breakthroughs of other areas. As a case in point, in the last decades, the field of computation changed the face of architectural practice. Considering the recent breakthroughs of Machine Learning (ML), it is expectable to see architecture adopting ML-based approaches. However, it is not yet clear how much this adoption will change the architectural practice and in order to forecast this change it is necessary to understand the foundations of ML and its impact in other fields of human activity. This paper discusses important ML techniques and areas where they were successfully applied. Based on those examples, this paper forecast hypothetical uses of ML in the realm of building design. In particular, we examine ML approaches in conceptualization, algorithmization, modeling, and optimization tasks. In the end, we conjecture potential applications of such approaches, suggest future lines of research, and speculate on the future face of the architectural profession.
keywords	Machine Learning, Algorithmic Design, AI for Building Design
series	CAAD Futures
type	normal paper
email
last changed	2019/07/29 14:54

_id	ecaadesigradi2019_425
id	ecaadesigradi2019_425
authors	Betti, Giovanni, Aziz, Saqib and Ron, Gili
year	2019
title	Pop Up Factory : Collaborative Design in Mixed Rality - Interactive live installation for the makeCity festival, 2018 Berlin
doi	https://doi.org/10.52842/conf.ecaade.2019.3.115
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. 115-124
summary	This paper examines a novel, integrated and collaborative approach to design and fabrication, enabled through Mixed Reality. In a bespoke fabrication process, the design is controlled and altered by users in holographic space, through a custom, multi-modal interface. Users input is live-streamed and channeled to 3D modelling environment,on-demand robotic fabrication and AR-guided assembly. The Holographic Interface is aimed at promoting man-machine collaboration. A bespoke pipeline translates hand gestures and audio into CAD and numeric fabrication. This enables non-professional participants engage with a plethora of novel technology. The feasibility of Mixed Reality for architectural workflow was tested through an interactive installation for the makeCity Berlin 2018 festival. Participants experienced with on-demand design, fabrication an AR-guided assembly. This article will discuss the technical measures taken as well as the potential in using Holographic Interfaces for collaborative design and on-site fabrication.Please write your abstract here by clicking this paragraph.
keywords	Holographic Interface; Augmented Reality; Multimodal Interface; Collaborative Design; Robotic Fabrication; On-Site Fabrication
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

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