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	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	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	ecaade2017_041
id	ecaade2017_041
authors	Fukuda, Tomohiro, Kuwamuro, Yasuyuki and Yabuki, Nobuyoshi
year	2017
title	Optical Integrity of Diminished Reality Using Deep Learning
doi	https://doi.org/10.52842/conf.ecaade.2017.1.241
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. 241-250
summary	A new method is proposed to improve diminished reality (DR) simulations to allow the demolition and removal of entire buildings in large-scale spaces. Our research goal was to obtain optical integrity by using a scientific and reliable simulation approach. Further, we tackled presumption of the texture of the background sky by applying deep learning. Our approach extracted the background sky using information from the actual sky obtained from a photographed image. This method comprised two steps: (1) detection of the sky area from the image through image segmentation and (2) creation of an image of the sky through image inpainting. The deep convolutional neural networks developed by us to train and predict images were evaluated to be feasible and effective.
keywords	Diminished Reality; Optical Integrity; Deep Learning; Augmented Reality; Landscape assessment
series	eCAADe
email
last changed	2022/06/07 07:50

_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
doi	https://doi.org/10.52842/conf.ecaade.2023.2.419
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
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	ecaade2017_271
id	ecaade2017_271
authors	Narahara, Taro
year	2017
title	Collective Construction Modeling and Machine Learning: Potential for Architectural Design
doi	https://doi.org/10.52842/conf.ecaade.2017.2.341
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
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	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	ecaade2017_234
id	ecaade2017_234
authors	Benetti, Alberto, Favargiotti, Sara and Ricci, Mos?
year	2017
title	RE.S.U.ME. - REsilient and Smart Urban MEtabolism
doi	https://doi.org/10.52842/conf.ecaade.2017.1.1113
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. 1113-1120
summary	New technologies and uncontrolled open-data policies lead public to a new way of approaching the built environment. To enlarge the competences of the professionals that work within the cities, we believe that providing a deep and dynamic knowledge on the heritage and urban built environment is the more effective solution to offer a unique support to the needs. By providing a boosted geographical database with detailed information about the status of each building, we aim to support the professional by providing a neat vision about vacant buildings available citywide. We think this knowledge is an important asset in covering every kind of public requests: from flat to rent to an abandoned building to restore or to drive better investors. The city of Trento will be the pilot project to test these statements.We studied the phenomenon of pushing new constructions rather investing on the reuse of abandoned buildings with the consequences of unsustainable land use. To address the work we adopted a comprehensive approach across the fields of urbanism, ICT engineering and social sciences. We believe that sharing knowledge and know-hows with municipalities, agencies, and citizens is the way to support better market strategies as well as urban transformation policies.
keywords	Information Technology; Urban Metabolism; Re-cycle; Urban Reserves; Policy Decision-Making; Data-driven Analysis
series	eCAADe
email
last changed	2022/06/07 07:54

_id	ecaade2017_009
id	ecaade2017_009
authors	Takizawa, Atsushi and Furuta, Airi
year	2017
title	3D Spatial Analysis Method with First-Person Viewpoint by Deep Convolutional Neural Network with Omnidirectional RGB and Depth Images
doi	https://doi.org/10.52842/conf.ecaade.2017.2.693
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. 693-702
summary	The fields of architecture and urban planning widely apply spatial analysis based on images. However, many features can influence the spatial conditions, not all of which can be explicitly defined. In this research, we propose a new deep learning framework for extracting spatial features without explicitly specifying them and use these features for spatial analysis and prediction. As a first step, we establish a deep convolution neural network (DCNN) learning problem with omnidirectional images that include depth images as well as ordinary RGB images. We then use these images as explanatory variables in a game engine to predict a subjects' preference regarding a virtual urban space. DCNNs learn the relationship between the evaluation result and the omnidirectional camera images and we confirm the prediction accuracy of the verification data.
keywords	Space evaluation; deep convolutional neural network; omnidirectional image; depth image; Unity; virtual reality
series	eCAADe
email
last changed	2022/06/07 07:56

_id	acadia17_164
id	acadia17_164
authors	Brugnaro, Giulio; Hanna, Sean
year	2017
title	Adaptive Robotic Training Methods for Subtractive Manufacturing
doi	https://doi.org/10.52842/conf.acadia.2017.164
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. 164-169
summary	This paper presents the initial developments of a method to train an adaptive robotic system for subtractive manufacturing with timber, based on sensor feedback, machine-learning procedures and material explorations. The methods were evaluated in a series of tests where the trained networks were successfully used to predict fabrication parameters for simple cutting operations with chisels and gouges. The results suggest potential benefits for non-standard fabrication methods and a more effective use of material affordances.
keywords	design methods; information processing; construction; robotics; ai & machine learning; digital craft; manual craft
series	ACADIA
email
last changed	2022/06/07 07:52

_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
doi	https://doi.org/10.52842/conf.acadia.2017.552
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
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	acadia19_392
id	acadia19_392
authors	Steinfeld, Kyle
year	2019
title	GAN Loci
doi	https://doi.org/10.52842/conf.acadia.2019.392
source	ACADIA 19:UBIQUITY AND AUTONOMY [Proceedings of the 39th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-59179-7] (The University of Texas at Austin School of Architecture, Austin, Texas 21-26 October, 2019) pp. 392-403
summary	This project applies techniques in machine learning, specifically generative adversarial networks (or GANs), to produce synthetic images intended to capture the predominant visual properties of urban places. We propose that imaging cities in this manner represents the first computational approach to documenting the Genius Loci of a city (Norberg-Schulz, 1980), which is understood to include those forms, textures, colors, and qualities of light that exemplify a particular urban location and that set it apart from similar places. Presented here are methods for the collection of urban image data, for the necessary processing and formatting of this data, and for the training of two known computational statistical models (StyleGAN (Karras et al., 2018) and Pix2Pix (Isola et al., 2016)) that identify visual patterns distinct to a given site and that reproduce these patterns to generate new images. These methods have been applied to image nine distinct urban contexts across six cities in the US and Europe, the results of which are presented here. While the product of this work is not a tool for the design of cities or building forms, but rather a method for the synthetic imaging of existing places, we nevertheless seek to situate the work in terms of computer-assisted design (CAD). In this regard, the project is demonstrative of a new approach to CAD tools. In contrast with existing tools that seek to capture the explicit intention of their user (Aish, Glynn, Sheil 2017), in applying computational statistical methods to the production of images that speak to the implicit qualities that constitute a place, this project demonstrates the unique advantages offered by such methods in capturing and expressing the tacit.
series	ACADIA
type	normal paper
email
last changed	2022/06/07 07:56

_id	acadia23_v3_71
id	acadia23_v3_71
authors	Vassigh, Shahin; Bogosian, Biayna
year	2023
title	Envisioning an Open Knowledge Network (OKN) for AEC Roboticists
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 3: Proceedings of the 43rd Annual Conference for the Association for Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9891764-1-0]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 24-32.
summary	The construction industry faces numerous challenges related to productivity, sustainability, and meeting global demands (Hatoum and Nassereddine 2020; Carra et al. 2018; Barbosa, Woetzel, and Mischke 2017; Bock 2015; Linner 2013). In response, the automation of design and construction has emerged as a promising solution. In the past three decades, researchers and innovators in the Architecture, Engineering, and Construction (AEC) fields have made significant strides in automating various aspects of building construction, utilizing computational design and robotic fabrication processes (Dubor et al. 2019). However, synthesizing innovation in automation encounters several obstacles. First, there is a lack of an established venue for information sharing, making it difficult to build upon the knowledge of peers. First, the absence of a well-established platform for information sharing hinders the ability to effectively capitalize on the knowledge of peers. Consequently, much of the research remains isolated, impeding the rapid dissemination of knowledge within the field (Mahbub 2015). Second, the absence of a standardized and unified process for automating design and construction leads to the individual development of standards, workflows, and terminologies. This lack of standardization presents a significant obstacle to research and learning within the field. Lastly, insufficient training materials hinder the acquisition of skills necessary to effectively utilize automation. Traditional in-person robotics training is resource-intensive, expensive, and designed for specific platforms (Peterson et al. 2021; Thomas 2013).
series	ACADIA
type	field note
email
last changed	2024/04/17 13:59

_id	ecaade2017_021
id	ecaade2017_021
authors	Agirbas, Asli
year	2017
title	The Use of Simulation for Creating Folding Structures - A Teaching Model
doi	https://doi.org/10.52842/conf.ecaade.2017.1.325
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. 325-332
summary	In architectural education, the demand for creating forms with a non-Euclidean geometry, which can only be achieved by using the computer-aided design tools, is increasing. The teaching of this subject is a great challenge for both students and instructors, because of the intensive nature of architecture undergraduate programs. Therefore, for the creation of those forms with a non-Euclidean geometry, experimental work was carried out in an elective course based on the learning visual programming language. The creation of folding structures with form-finding by simulation was chosen as the subject of the design production which would be done as part of the content of the course. In this particular course, it was intended that all stages should be experienced, from the modeling in the virtual environment to the digital fabrication. Hence, in their early years of architectural education, the students were able to learn versatile thinking by experiencing, simultaneously, the use of simulation in the environment of visual programming language, the forming space by using folding structures, the material-based thinking and the creation of their designs suitable to the digital fabrication.
keywords	Folding Structures; CAAD; Simulation; Form-finding; Architectural Education
series	eCAADe
email
last changed	2022/06/07 07:54

_id	caadria2018_333
id	caadria2018_333
authors	Cupkova, Dana, Byrne, Daragh and Cascaval, Dan
year	2018
title	Sentient Concrete - Developing Embedded Thermal and Thermochromic Interactions for Architecture and Built Environment
doi	https://doi.org/10.52842/conf.caadria.2018.2.545
source	T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 545-554
summary	Historically, architectural design focused on adaptation of built environment to serve human needs. Recently embedded computation and digital fabrication have advanced means to actuate physical infrastructure in real-time. These 'reactive spaces' have typically explored movement and media as a means to achieve reactivity and physical deformation (Chatting et al. 2017). However, here we recontextualize 'reactive' as finding new mechanisms for permanent and non-deformable everyday materials and environments. In this paper, we describe our ongoing work to create a series of complex forms - modular concrete panels - using thermal, tactile and thermochromic responses controlled by embedded networked system. We create individualized pathways to thermally actuate these surfaces and explore expressive methods to respond to the conditions around these forms - the environment, the systems that support them, their interaction and relationships to human occupants. We outline the design processes to achieve thermally adaptive concrete panels, illustrate interactive scenarios that our system enables, and discuss opportunities for new forms of interactivity within the built environment.
keywords	Responsive environments; Geometrically induced thermodynamics; Ambient devices; Internet of things; Modular electronic systems
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	acadia17_238
id	acadia17_238
authors	El-Zanfaly, Dina
year	2017
title	A Multisensory Computational Model for Human-Machine Making and Learning
doi	https://doi.org/10.52842/conf.acadia.2017.238
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. 238-247
summary	Despite the advancement of digital design and fabrication technologies, design practices still follow Alberti’s hylomorphic model of separating the design phase from the construction phase. This separation hinders creativity and flexibility in reacting to surprises that may arise during the construction phase. These surprises often come as a result of a mismatch between the sophistication allowed by the digital technologies and the designer’s experience using them. These technologies and expertise depend on one human sense, vision, ignoring other senses that could be shaped and used in design and learning. Moreover, pedagogical approaches in the design studio have not yet fully integrated digital technologies as design companions; rather, they have been used primarily as tools for representation and materialization. This research introduces a multisensory computational model for human-machine making and learning. The model is based on a recursive process of embodied, situated, multisensory interaction between the learner, the machines and the thing-in-the-making. This approach depends heavily on computational making, abstracting, and describing the making process. To demonstrate its effectiveness, I present a case study from a course I taught at MIT in which students built full-scale, lightweight structures with embedded electronics. This model creates a loop between design and construction that develops students’ sensory experience and spatial reasoning skills while at the same time enabling them to use digital technologies as design companions. The paper shows that making can be used to teach design while enabling the students to make judgments on their own and to improvise.
keywords	education, society & culture; fabrication
series	ACADIA
email
last changed	2022/06/07 07:55

_id	ecaade2017_jgo
id	ecaade2017_jgo
authors	Gero, John S.
year	2017
title	Cognitive Design Computing
doi	https://doi.org/10.52842/conf.ecaade.2017.1.037
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. 37-40
summary	This talk describes the foundational concepts of cognitive design computing and then presents some examples. Cognitive computing is concerned with modeling human cognition computationally and using that model as the foundation for constructing computer models of design activities. Human cognition is based on perception, learning and adaptation. Here we present human cognition in terms of situated cognition - cognition involving interaction with an environment. The talk briefly introduces a set of principles for cognitive design computing founded on the three concepts of interaction, constructive memory and situatedness. It then presents two examples of applications of this approach.
series	eCAADe
email
last changed	2022/06/07 07:51

_id	caadria2017_109
id	caadria2017_109
authors	Imanishi, Naoki, Hinoki, Shinichiro, Muraoka, Mizuki, Tateyama, Ran, Abe, U-ichi, Kensuke, Hotta and Ikeda, Yasushi
year	2017
title	Bamboo Concrete Shells - An Adaptable Construction Method Using Onsite Materials in a Remote Location
doi	https://doi.org/10.52842/conf.caadria.2017.445
source	P. Janssen, P. Loh, A. Raonic, M. A. Schnabel (eds.), Protocols, Flows, and Glitches - Proceedings of the 22nd CAADRIA Conference, Xi'an Jiaotong-Liverpool University, Suzhou, China, 5-8 April 2017, pp. 445-455
summary	This paper proposes an on site construction support system using digital techniques to solve the issue of logistical inconvenience on remote islands, where industrialized construction methods are absent. Transporting heavy machinery is costly and difficult in isolated rural areas. In addition, introducing materials from outside creates a heavy ecological footprint when building. Locally produced construction materials resolve many of these issues. To test the potential of building within these constraints a case study site on an isolated island of Japan, named Kuchinoerabu, was chosen. A concrete shell structure was created using locally sourced bamboo as reinforcement. Through the study, several technical issues are revealed. Significantly, there is broad variation in the material properties of bamboo, and reducing errors when using unskilled labor is difficult. The system nominally manages the following functions: 1) Synchronizing data between CAD and the materialized form; 2) Checking errors between the target form and the one that was actually produced; 3) Inputting material characteristics on site. 4) Making a structural analysis, and reflecting its execution during construction. These functions minimize the margin of error, and aid an unskilled labor force to work more accurately.
keywords	Bamboo; natural material; digital construction
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	ecaade2017_301
id	ecaade2017_301
authors	Kalantari, Saleh and Ghandi, Mona
year	2017
title	Data-responsive Architectural Design Processes
doi	https://doi.org/10.52842/conf.ecaade.2017.2.503.2
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. 503-512
summary	Current advancements in information technology and mechanical components offer incredible new possibilities for innovation in architecture. Many aspects of our physical environment are becoming integrated with information systems, a phenomenon that has been referred to as the "Internet of Things." The implications and applications of this technology are far-reaching, and students who are learning about design in today's environment have a bewildering array of new tools available for their exploration. This paper reviews some of the central concepts of contemporary data-driven design, and describes how these concepts can be used in a pedagogical framework to encourage student innovation. The authors provide details about their work with students in IDR Studios, and highlight some of the innovative design solutions created by students using information-based toolsets. This research provides a pedagogical framework for helping design students to engage with new technological resources as they work to develop the architectural intelligence.
keywords	Adaptive Systems; Internet of Things; Big Data; Data Driven Design Process
series	eCAADe
email
last changed	2022/06/07 07:52

_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
doi	https://doi.org/10.52842/conf.ecaade.2017.1.059
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
series	eCAADe
email
last changed	2022/06/07 07:52

_id	acadia17_366
id	acadia17_366
authors	Lin, Yuming; Huang, Weixin
year	2017
title	Behavior Analysis and Individual Labeling Using Data from Wi-Fi IPS
doi	https://doi.org/10.52842/conf.acadia.2017.366
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. 366- 373
summary	It is fairly important for architects and urban designers to understand how different people interact with the environment. However, traditional investigation methods for studying environmental behavior are quite limited in their coverage of samples and regions, which are not sufficient to delve into the behavioral differences of people. Only recently, the development of indoor positioning systems (IPS) and data-mining techniques has made it possible to collect full-time, full-coverage data for behavioral difference research and individualized identification. In our research, the Wi-Fi IPS system is chosen among the various IPS systems as the data source due to its extensive applicability and acceptable cost. In this paper, we analyzed a 60-day anonymized dataset from a ski resort, collected by a Wi-Fi IPS system with 110 Wi-Fi access points. Combining this with mobile phone data and questionnaires, we revealed some interesting characteristics of tourists from different origins through spatial-temporal behavioral data, and further conducted individual labeling through supervised learning. Through this case study, temporal-spatial behavioral data from an IPS system exhibited great potential in revealing individual characteristics besides exploring group differences, shedding light on the prospect of architectural space personalization.
keywords	design methods; information processing; data mining; big data
series	ACADIA
email
last changed	2022/06/07 07:59

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