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	ecaade2017_269
id	ecaade2017_269
authors	Rahmani Asl, Mohammad, Das, Subhajit, Tsai, Barry, Molloy, Ian and Hauck, Anthony
year	2017
title	Energy Model Machine (EMM) - Instant Building Energy Prediction using Machine Learning
source	Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 2, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 277-286
doi	https://doi.org/10.52842/conf.ecaade.2017.2.277
summary	In the process of building design, energy performance is often simulated using physical principles of thermodynamics and energy behaviour using elaborate simulation tools. However, energy simulation is computationally expensive and time consuming process. These drawbacks limit opportunities for design space exploration and prevent interactive design which results in environmentally inefficient buildings. In this paper we propose Energy Model Machine (EMM) as a general and flexible approximation model for instant energy performance prediction using machine learning (ML) algorithms to facilitate design space exploration in building design process. EMM can easily be added to design tools and provide instant feedback for real-time design iterations. To demonstrate its applicability, EMM is used to estimate energy performance of a medium size office building during the design space exploration in widely used parametrically design tool as a case study. The results of this study support the feasibility of using machine learning approaches to estimate energy performance for design exploration and optimization workflows to achieve high performance buildings.
keywords	Machine Learning; Artificial Neural Networks; Boosted Decision Tree; Building Energy Performance; Parametric Modeling and Design; Building Performance Optimization
series	eCAADe
email
last changed	2022/06/07 08:00

_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
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
doi	https://doi.org/10.52842/conf.ecaade.2021.2.351
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	sigradi2017_069
id	sigradi2017_069
authors	Briones Lazo, Carolina; Carolina Soto Ogueta
year	2017
title	La enseñanza de BIM en Chile, el desafío de un cambio de enfoque centrado en la metodología por sobre la tecnología. [BIM education in Chile, the challenge of a shift of focus centered on methodology over technology.]
source	SIGraDi 2017 [Proceedings of the 21th Conference of the Iberoamerican Society of Digital Graphics - ISBN: 978-956-227-439-5] Chile, Concepción 22 - 24 November 2017, pp.470-478
summary	This article presents the level of adoption of BIM in Chile referring to recent studies carried out in the country, demonstrating that there has not been a significant increase in the use of this methodology by the industry. According to the analysis of international cases on educational frameworks, the authors argue that the development of a national education strategy for BIM with a focus on defining BIM capabilities required to assume the national mandate 2020, along with promoting collaborative work environments and active learning methodologies would be very beneficial.
keywords	Building Information Modelling; Metodología BIM; Adopción de BIM; Estrategia de enseñanza de BIM.
series	SIGRADI
email
last changed	2021/03/28 19:58

_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	acadia17_284
id	acadia17_284
authors	Hu, Zhengrong; Park, Ju Hong
year	2017
title	HalO [Indoor Positioning Mobile Platform]: A Data-Driven, Indoor-Positioning System With Bluetooth Low Energy Technology To Datafy Indoor Circulation And Classify Social Gathering Patterns For Assisting Post Occupancy Evaluation
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 284-291
doi	https://doi.org/10.52842/conf.acadia.2017.284
summary	Post-Occupancy Evaluation (POE) as an integrated field between architecture and sociology has created practical guidelines for evaluating indoor human behavior within a built environment. This research builds on recent attempts to integrate datafication and machine learning into POE practices that may one day assist Building Information Modeling (BIM) and multi-agent modeling. This research is based on two premises: 1) that the proliferation of Bluetooth Low Energy (BLE) technology allows us to collect a building user’s data cost-effectively and 2) that the growing application of machine learning algorithms allows us to process, analyze and synthesize data efficiently. This study illustrates that the mobile platform HalO can serve as a generic tool for datafication and automation of data analysis of the movement of a building user. In this research, the iOS mobile application HalO, combined with BLE beacons enable building providers (architects, developers, engineers and facility managers etc.) to collect the user’s indoor location data. Triangulation was used to pinpoint the user’s indoor positions, and k-means clustering was applied to classify users into different gathering groups. Through four research procedures—Design Intention Analysis, Data Collection, Data Storage and Data Analysis—the visualized and classified data helps building providers to better evaluate building performance, optimize building operations and improve the accuracy of simulations.
keywords	design methods; information processing; data mining; IoT; AI; machine learning
series	ACADIA
email
last changed	2022/06/07 07:49

_id	ecaade2017_ws-archiedu
id	ecaade2017_ws-archiedu
authors	Kulcke, Matthias, Lorenz, Wolfgang E. and Wurzer, Gabriel
year	2017
title	Structuring of Teaching and Learning Situations in Architectural Education - Using and Integrating Digital Analysis within Interactive Genetic Algorithms
source	Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 1, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 59-62
doi	https://doi.org/10.52842/conf.ecaade.2017.1.059
series	eCAADe
email
last changed	2022/06/07 07:52

_id	ecaade2023_44
id	ecaade2023_44
authors	Mayrhofer-Hufnagl, Ingrid and Ennemoser, Benjamin
year	2023
title	From Linear to Manifold Interpolation: Exemplifying the paradigm shift through interpolation
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. 419–429
doi	https://doi.org/10.52842/conf.ecaade.2023.2.419
summary	The advent of artificial intelligence, specifically neural networks, has marked a significant turning point in the field of computation. During such transformative times, we are often faced with a dearth of appropriate vocabulary, which forces us to rely on existing terms, regardless of their inadequacy. This paper argues that the term “interpolation,” typically used in deep learning (DL), is a prime example of this phenomenon. It is not uncommon for beginners to misunderstand its meaning, as DL pioneer Francois Chollet (2017) has noted. This misreading is especially true in the discipline of architecture, and this study aims to demonstrate how the meaning of “interpolation” has evolved in the second digital turn. We begin by illustrating, using 2D data, the difference between linear interpolation in the context of topological figures and its use in DL algorithms. We then demonstrate how 3DGANs can be employed to interpolate across different topologies in complex 3D space, highlighting the distinction between linear and manifold interpolation. In both 2D and 3D examples, our results indicate that the process does not involve continuous morphing but instead resembles the piecing together of a jigsaw puzzle to form many parts of a larger ambient space. Our study reveals how previous architectural research on DL has employed the term “interpolation” without clarifying the crucial differences from its use in the first digital turn. We demonstrate the new possibilities that manifold interpolation offers for architecture, which extend well beyond parametric variations of the same topology.
keywords	Interpolation, 3D Generative Adversarial Networks, Deep Learning, Hybrid Space
series	eCAADe
email
last changed	2023/12/10 10:49

_id	acadia17_426
id	acadia17_426
authors	Moorman, Andrew
year	2017
title	Pattern Making and Learning: Non-Routine Practices in Generative Design
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 426- 435
doi	https://doi.org/10.52842/conf.acadia.2017.426
summary	We now witness an upsurge in mainstream generative design tools fortified by simulation that speed up the concealed linear synthesis of optimized design alternatives. In pursuit of optimality, these tools saturate local machines or cloud servers with analysis and design iteration data, only to discard it once the procedure has concluded. Largely absent, however, are tools for an active, adaptive relationship with design exploration and the reuse of corresponding design data and metadata. In Pattern Making and Pattern Learning, we propose that these characteristics are mutually beneficial. This paper presents a series of revisions to the optimization framework for routine design synthesis that examine a potential symbiosis between the production of large datasets (big data) and non-routine practices of making in design. Our engagement with iterative design exercises is twofold: as a supply of computer-generated design information to foster user intuition and explore the design space on non-objective terms, and as a supply of human-generated design information to learn artifacts of user preference in the interest of design software personalization. These concepts are applied to the generation of functionally graded patterning in chair design, combining methods of physical production with programmable sheet material behavior through a custom interactive synthesis framework.
keywords	design methods; information processing; ai & machine learning; simulation & optimization; generative system
series	ACADIA
email
last changed	2022/06/07 07:58

_id	ecaade2017_271
id	ecaade2017_271
authors	Narahara, Taro
year	2017
title	Collective Construction Modeling and Machine Learning: Potential for Architectural Design
source	Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 2, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 341-348
doi	https://doi.org/10.52842/conf.ecaade.2017.2.341
summary	Recently, there are significant developments in artificial intelligence using advanced machine learning algorithms such as deep neural networks. These new methods can defeat human expert players in strategy-based board games such as Go and video games such as Breakout. This paper suggests a way to incorporate such advanced computing methods into architectural design through introducing a simple conceptual design project inspired by computational interpretations of wasps' collective constructions. At this stage, the paper's intent is not to introduce a practical and fully finished tool directly useful for architectural design. Instead, the paper proposes an example of a program that can potentially become a conceptual framework for incorporating such advanced methods into architectural design.
keywords	Design tools; Stigmergy; Machine learning
series	eCAADe
email
last changed	2022/06/07 07:58

_id	ecaade2017_220
id	ecaade2017_220
authors	Quartara, Andrea and Figliola, Angelo
year	2017
title	Tangible Computing - Manufacturing of Intertwined Logics
source	Fioravanti, A, Cursi, S, Elahmar, S, Gargaro, S, Loffreda, G, Novembri, G, Trento, A (eds.), ShoCK! - Sharing Computational Knowledge! - Proceedings of the 35th eCAADe Conference - Volume 2, Sapienza University of Rome, Rome, Italy, 20-22 September 2017, pp. 115-122
doi	https://doi.org/10.52842/conf.ecaade.2017.2.115
summary	This paper explores the process of digital materialization through robotic fabrication techniques by presenting three wooden projects. The analysis of the case studies is oriented to underline the impact that computation had on architectural construction due to its methodological and instrumental innovations over the last decades. The absorption of computing and digital fabrication logics within the discipline is explored from either an architectural point of view and from the improvements related to automation of the constructive process. On the one hand the case studies are caught because of the desire to expand material complexity and, on the other hand because of the integration with other technological systems. The narrative allows gathering pros and cons in three different investigative macro areas: material culture, methodological oversights, and operative setbacks coming from digital machine and communicational constraints. This analytical investigation helps the definition of a new pathway for future researches, looking forward the assimilation of digital materiality learning in building construction.
keywords	computational design; file-to-factory; large-scale robotic woodworking; new production methods
series	eCAADe
email
last changed	2022/06/07 08:00

_id	acadia17_552
id	acadia17_552
authors	Sjoberg, Christian; Beorkrem, Christopher; Ellinger, Jefferson
year	2017
title	Emergent Syntax: Machine Learning for the Curation of Design Solution Space
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 552- 561
doi	https://doi.org/10.52842/conf.acadia.2017.552
summary	The expanding role of computational models in the process of design is producing exponential growth in parameter spaces. As designers, we must create and implement new methods for searching these parameter spaces, considering not only quantitative optimization metrics but also qualitative features. This paper proposes a methodology that leverages the pattern modeling properties of artificial neural networks to capture designers' inexplicit selection criteria and create user-selection-based fitness functions for a genetic solver. Through emulation of learned selection patterns, fitness functions based on trained networks provide a method for qualitative evaluation of designs in the context of a given population. The application of genetic solvers for the generation of new populations based on the trained network selections creates emergent high-density clusters in the parameter space, allowing for the identification of solutions that satisfy the designer’s inexplicit criteria. The results of an initial user study show that even with small numbers of training objects, a search tool with this configuration can begin to emulate the design criteria of the user who trained it.
keywords	design methods; information processing; AI; machine learning; generative system
series	ACADIA
email
last changed	2022/06/07 07:56

_id	acadia17_52
id	acadia17_52
authors	Ajlouni, Rima
year	2017
title	Simulation of Sound Diffusion Patterns of Fractal-Based Surface Profiles
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 52-61
doi	https://doi.org/10.52842/conf.acadia.2017.052
summary	Acoustical design is one of the most challenging aspects of architecture. A complex system of competing influences (e.g., space geometry, size, proportion, material properties, surface detail, etc.) contribute to shaping the quality of the auditory experience. In particular, architectural surfaces affect the way that sound reflections propagate through space. By diffusing the reflected sound energy, surface designs can promote a more homogeneous auditory atmosphere by mitigating sharp and focused reflections. One of the challenges with designing an effective diffuser is the need to respond to a wide band of sound wavelengths, which requires the surface profile to precisely encode a range of detail sizes, depths and angles. Most of the available sound diffusers are designed to respond to a narrow band of frequencies. In this context, fractal-based surface designs can provide a unique opportunity for mitigating such limitations. A key principle of fractal geometry is its multilevel hierarchical order, which enables the same pattern to occur at different scales. This characteristic makes it a potential candidate for diffusing a wider band of sound wavelengths. However, predicting the reflection patterns of complicated fractal-based surface designs can be challenging using available acoustical software. These tools are often costly, complicated and are not designed for predicting early sound propagation paths. This research argues that writing customized algorithms provides a valuable, free and efficient alternative for addressing targeted acoustical design problems. The paper presents a methodology for designing and testing a customized algorithm for predicting sound diffusion patterns of fractal-based surfaces. Both quantitative and qualitative approaches were used to develop the code and evaluate the results.
keywords	design methods; information processing; simulation & optimization; data visualization
series	ACADIA
email
last changed	2022/06/07 07:54

_id	cf2017_137
id	cf2017_137
authors	Ensari, Elif; Kobas, Bilge; Sucuo?lu, Can
year	2017
title	Computational Decision Support for an Airport Complex Roof Design: A Case Study of Evolutionary Optimization for Daylight Provision and Overheating Prevention
source	Gülen Çagdas, Mine Özkar, Leman F. Gül and Ethem Gürer (Eds.) Future Trajectories of Computation in Design [17th International Conference, CAAD Futures 2017, Proceedings / ISBN 978-975-561-482-3] Istanbul, Turkey, July 12-14, 2017, pp. 137-149.
summary	This study focuses on generating geometric design alternatives for an airport roof structure with an evolutionary design method based on optimizing solar heat gain and daylight levels. The method incorporates a parametric 3D model of the building, a multi objective genetic algorithm that was linked with the model to iteratively test for various geometric solutions, a custom module that was developed to simulate solar conditions, and external energy simulation environments that was used to validate the outcomes. The integral outcome was achieved through an iterative workflow of many software tools, and the study is significant in dealing with several space typologies at the same time, taking real-life constraints such as applicability, ease of operation, construction loads into consideration, and satisfying design and aesthetic requirements of the architectural design team.
keywords	Evolutionary algorithms, daylight and energy performance, multi-objective optimization
series	CAAD Futures
email
last changed	2017/12/01 14:37

_id	cf2017_457
id	cf2017_457
authors	Erdine, Elif; Kallegias, Alexandros; Lara Moreira, Angel Fernando; Devadass, Pradeep; Sungur, Alican
year	2017
title	Robot-Aided Fabrication of Interwoven Reinforced Concrete Structures
source	Gülen Çagdas, Mine Özkar, Leman F. Gül and Ethem Gürer (Eds.) Future Trajectories of Computation in Design [17th International Conference, CAAD Futures 2017, Proceedings / ISBN 978-975-561-482-3] Istanbul, Turkey, July 12-14, 2017, p. 457.
summary	This paper focuses on the realization of three-dimensionally interwoven concrete structures and their design process. The output is part of an ongoing research in developing an innovative strategy for the use of robotics in construction. The robotic fabrication techniques described in this paper are coupled with the computational methods dealing with geometry rationalization and material constraints among others. By revisiting the traditional bar bending techniques, this research aims to develop a novel approach by the reduction of mechanical parts for retaining control over the desired geometrical output. This is achieved by devising a robotic tool-path, developed in KUKA\|prc with Python scripting, where fundamental material properties, including tolerances and spring-back values, are integrated in the bending motion methods via a series of mathematical calculations in accord with physical tests. This research serves to demonstrate that robotic integration while efficient in manufacturing it also retains valid alignment with the architectural design sensibility.
keywords	Robotic fabrication, Robotic bar bending, Concrete composite, Geometry optimization, Polypropylene formwork
series	CAAD Futures
email
last changed	2017/12/01 14:38

_id	acadia17_340
id	acadia17_340
authors	Landim, Gabriele; Digiandomenico, Dyego; Amaro, Jean; Pratschke, Anja; Tramontano, Marcelo; Toledo, Claudio
year	2017
title	Architectural Optimization and Open Source Development: Nesting and Genetic Algorithms
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 340- 349
doi	https://doi.org/10.52842/conf.acadia.2017.340
summary	This research presents a general overview of performance-oriented architectural design and how the rise of parametric modeling and algorithm-aided design enable an integrated environment for project design, simulation and optimization. For optimization processes, one of the most used methods in architectural problem solving is genetic algorithms (GAs). However, as the use of GAs becomes more common in the architecture, it is possible to identify a lack of clarity about the methods and procedures operated by the algorithms. Thus, this research seeks to contribute to the field through the implementation of an open source optimization plugin whose method of implemented algorithms, a GA and a nesting algorithm, can be accessed for evaluation, improvement and adaptation to other architectural problems. In the same way, it discusses the relevance of the openness and clarity of the methods employed in optimization processes in architecture. The proposed plugin was tested in an experiment that verified the feasibility of the development of the open source plugin and the efficiency of the method in solving the chosen architectural problem.
keywords	algorithm-aided design; optimization; genetic algorithm; nesting; open source; computational / artistic cultures; generative system; simulation & optimization; design methods; information processing
series	ACADIA
email
last changed	2022/06/07 07:52

_id	sigradi2017_045
id	sigradi2017_045
authors	Loyola, Mauricio; Sebastián Rozas, Sebastián Caldera
year	2017
title	Kerfing2: Una técnica para el diseño, fabricación y optimización de elementos de doble curvatura a partir de placas rígidas de madera [Kerfing2: A technique for the design, manufacture and optimization of double-curved elements from rigid wooden plates]
source	SIGraDi 2017 [Proceedings of the 21th Conference of the Iberoamerican Society of Digital Graphics - ISBN: 978-956-227-439-5] Chile, Concepción 22 - 24 November 2017, pp.307-313
summary	We present a novel technique for the design, optimization, and fabrication of plywood double-curvature building components, based on an ancient woodworking method known as kerfing. We explain the principles of geometric optimization, their implementation into computational algorithms, and show the first prototypes as proofs of concept (PoC).
keywords	CAD/CAM; Digital Fabrication; Kerfing; Complex Geometries
series	SIGRADI
email
last changed	2021/03/28 19:58

_id	acadia17_382
id	acadia17_382
authors	Melenbrink, Nathan; Kassabian, Paul; Menges, Achim; Werfel, Justin
year	2017
title	Towards Force-aware Robot Collectives for On-site Construction
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 382- 391
doi	https://doi.org/10.52842/conf.acadia.2017.382
summary	Due to the irregular and variable environments in which most construction projects take place, the topic of on-site automation has previously been largely neglected in favor of off-site prefabrication. While prefabrication has certain obvious economic and schedule benefits, a number of potential applications would benefit from a fully autonomous robotic construction system capable of building without human supervision or intervention; for example, building in remote environments, or building structures whose form changes over time. Previous work using a swarm approach to robotic assembly generally neglected to consider forces acting on the structure, which is necessary to guarantee against failure during construction. In this paper we report on key findings for how distributed climbing robots can use local force measurements to assess aspects of global structural state. We then chart out a broader trajectory for the affordances of distributed on-site construction in the built environment and position our contributions within this research agenda. The principles explored in simulation are demonstrated in hardware, including solutions for force-sensing as well as a climbing robot.
keywords	material and construction; physics; construction/robotics; simulation & optimization
series	ACADIA
email
last changed	2022/06/07 07:58

_id	acadia17_562
id	acadia17_562
authors	Soler, Vicente; Retsin, Gilles; Jimenez Garcia, Manuel
year	2017
title	A Generalized Approach to Non-Layered Fused Filament Fabrication
source	ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 562- 571
doi	https://doi.org/10.52842/conf.acadia.2017.562
summary	This research attempts to generalize an approach for large-scale, non-layered spatial extrusion. The methodology consists of splitting a volume, representing any arbitrary geometry, into discrete fragments with a finite number of possible arrangements. These fragments are combined in response to a series of design criteria. A novel application of graph theory algorithms is used to generate a continuous and non-overlapping path through the discrete segments. Physical and mechanical issues related to extrusion technology are explored. The computational model takes into consideration the grade and limitations of different kinds of equipment and material properties to counteract fabrication errors with the goal of speeding up the process and eliminating any need for human intervention. This approach is implemented as a cross-platform software product and programming library that can generate robot programs compatible with multiple industrial robot manufacturers. A physical prototype was fabricated using the seminal Panton Chair as a test model. We conclude that the computational approach is sound and most of the issues encountered were due to the equipment used. This will be addressed in future work.
keywords	design methods; information processing; simulation & optimization; construction/robotics
series	ACADIA
email
last changed	2022/06/07 07:56

_id	cf2017_415
id	cf2017_415
authors	Tschetwertak, Julia; Schneider, Sven; Hollberg, Alexander; Donath, Dirk; Ruth, Jürgen
year	2017
title	A Matter of Sequence: Investigating the Impact of the Order of Design Decisions in Multi-Stage Design Processes
source	Gülen Çagdas, Mine Özkar, Leman F. Gül and Ethem Gürer (Eds.) Future Trajectories of Computation in Design [17th International Conference, CAAD Futures 2017, Proceedings / ISBN 978-975-561-482-3] Istanbul, Turkey, July 12-14, 2017, p. 415.
summary	The design as a process is not a new topic in architecture, yet some theories are widely unexplored, such as the multi-stage decision-making (MD) process. This design method provides multiple solutions for one design problem and is characterized by design stages. By adding new building components in every stage, multiple solutions are created for each design solution from the previous stage. If the MD process is to be applied in architectural practice, fundamental and theoretical knowledge about it becomes necessary. This paper investigates the impact of sequence of design stages on the design solutions in the MD process. A basic case study provides the necessary data for comparing different sequences and gaining fundamental knowledge of the MD process. The study contains a parametric model for building generation, a parametric Life Cycle Assessment tool and an optimization mechanism based on Evolutionary Algorithms.
keywords	Multi-stage decision-making process, Design process, Life Cycle Performance, Design Automation
series	CAAD Futures
email
last changed	2017/12/01 14:38

_id	sigradi2022_51
id	sigradi2022_51
authors	Varsami, Constantina; Tsamis, Alexandros; Logan, Timothy
year	2022
title	Gaming Engine as a Tool for Designing Smart, Interactive, Light-Sculpting Systems
source	Herrera, PC, Dreifuss-Serrano, C, Gómez, P, Arris-Calderon, LF, Critical Appropriations - Proceedings of the XXVI Conference of the Iberoamerican Society of Digital Graphics (SIGraDi 2022), Universidad Peruana de Ciencias Aplicadas, Lima, 7-11 November 2022 , pp. 617–628
summary	Even though interactive (Offermans et.al., 2013), adaptive (Viani et.al., 2017), and self-optimizable (Sun et.al., 2020) lighting systems are becoming readily available, designing system automations, and evaluating their impact on user experience significantly challenges designers. In this paper we demonstrate the use of a gaming engine as a platform for designing, simulating, and evaluating autonomous smart lighting behaviors. We establish the Human - Lighting System Interaction Framework, a computational framework for developing a Light Sculpting Engine and for designing occupant-system interactions. Our results include a. a method for combining in real-time lighting IES profiles into a single ‘combined’ profile - b. algorithms that optimize in real-time, lighting configurations - c. direct glare elimination algorithms, and d. system energy use optimization algorithms. Overall, the evolution from designing static building components to designing interactive systems necessitates the reconsideration of methods and tools that allow user experience and system performance to be tuned by design.
keywords	User Experience, Human-Building Interaction, Smart Lighting, Lighting Simulation, Gaming Engine
series	SIGraDi
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last changed	2023/05/16 16:56

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