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	caadria2017_105
id	caadria2017_105
authors	Janssen, Patrick
year	2017
title	Evolutionary Urbanism - Exploring Form-based Codes Using Neuroevolution Algorithms
doi	https://doi.org/10.52842/conf.caadria.2017.303
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. 303-312
summary	Form-Based Codes are legal regulations adopted by local government that allow specific urban forms to be achieved. Such codes have a significant impact on the performative potential of the urban environment. This paper explores the possibility of using a neuroevolution algorithm to elucidate the complex relationship between Form-based Codes and their performative potential. More specifically, Compositional Pattern Producing Networks (CPPN) are used to generate parameter fields, which then drive the generation of varied urban models. For evolving the CPPN networks, a neuroevolution algorithm is used, called Neuroevolution of Augmenting Topologies (NEAT). In order to test the feasibility of the proposed approach, an abstract experiment is described in which a population of urban models are evolved, optimising a set of performance criteria related to the vista and location of the residential units.
keywords	Form-based codes; evolutionary design; neural networks; neuroevolution; urban planning
series	CAADRIA
email
last changed	2022/06/07 07:52

_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	acadia17_202
id	acadia17_202
authors	Cupkova, Dana; Promoppatum, Patcharapit
year	2017
title	Modulating Thermal Mass Behavior Through Surface Figuration
doi	https://doi.org/10.52842/conf.acadia.2017.202
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. 202-211
summary	This research builds upon a previous body of work focused on the relationship between surface geometry and heat transfer coefficients in thermal mass passive systems. It argues for the design of passive systems with higher fidelity to multivariable space between performance and perception. Rooted in the combination of form and matter, the intention is to instrumentalize design principles for the choreography of thermal gradients between buildings and their environment from experiential, spatial and topological perspectives (Figure 1). Our work is built upon the premise that complex geometries can be used to improve both the aesthetic and thermodynamic performance of passive building systems (Cupkova and Azel 2015) by actuating thermal performance through geometric parameters primarily due to convection. Currently, the engineering-oriented approach to the design of thermal mass relies on averaged thermal calculations (Holman 2002), which do not adequately describe the nuanced differences that can be produced by complex three-dimensional geometries of passive thermal mass systems. Using a combination of computational fluid dynamic simulations with physically measured data, we investigate the relationship of heat transfer coefficients related to parameters of surface geometry. Our measured results suggest that we can deliberately and significantly delay heat absorption re-radiation purely by changing the geometric surface pattern over the same thermal mass. The goal of this work is to offer designers a more robust rule set for understanding approximate thermal lag behaviors of complex geometric systems, with a focus on the design of geometric properties rather than complex thermal calculations.
keywords	design methods; information processing; physics; smart materials
series	ACADIA
email
last changed	2022/06/07 07:56

_id	acadia17_330
id	acadia17_330
authors	Krietemeyer, Bess; Bartosh, Amber; Covington, Lorne
year	2017
title	Shared Realities: A Method for Adaptive Design Incorporating Real-Time User Feedback using Virtual Reality and 3D Depth-Sensing Systems
doi	https://doi.org/10.52842/conf.acadia.2017.330
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. 330- 339
summary	When designing interactive architectural systems and environments, the ability to gather user feedback in real time provides valuable insight into how the system is received and ultimately performs. However, physically testing or simulating user behavior with an interactive system outside of the actual context of use can be challenging due to time constraints and assumptions that do not reflect accurate social, behavioral, or environmental conditions. Employing evidence based, user-centered design practices from the field of human–computer interaction (HCI) coupled with emerging architectural design methodologies creates new opportunities for achieving optimal system performance and design usability for interactive architectural systems. This paper presents a methodology for developing a mixed reality computational workflow combining 3D depth sensing and virtual reality (VR) to enable iterative user-centered design. Using an interactive museum installation as a case study, user pointcloud data is observed via VR at full scale and in real time for a new design feedback experience. Through this method, the designer is able to virtually position him/herself among the museum installation visitors in order to observe their actual behaviors in context and iteratively make modifications instantaneously. In essence, the designer and user effectively share the same prototypical design space in different realities. Experimental deployment and preliminary results of the shared reality workflow are presented to demonstrate the viability of the method for the museum installation case study and for future interactive architectural design applications. Contributions to computational design, technical challenges, and ethical considerations are discussed for future work.
keywords	design methods; information processing; hci; VR; AR; mixed reality; computer vision
series	ACADIA
email
last changed	2022/06/07 07:52

_id	caadria2017_094
id	caadria2017_094
authors	Matthews, Linda and Perin, Gavin
year	2017
title	A Productive Ambiguity:Diffraction Aberrations as a Template for the Architectural Surface
doi	https://doi.org/10.52842/conf.caadria.2017.571
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. 571-580
summary	The hi-resolution imaging of public urban space for both promotional and surveillance purposes is now undertaken by a range of ubiquitous visioning technology such as Internet webcams, drones (UAV's) and high-altitude aircraft cameras. The ability to control and manipulate these types of images is a growing concern in an increasingly 'envisioned' environment. One approach is to disrupt or modify the 'emission signatures' of urban surfaces, which requires an understanding of the digital algorithms used to assemble and transmit image content into grids of visual data. Recent scaled tests show that Fraunhofer diffraction algorithms can interfere with the smooth transmission of image data. When these algorithmic patterns are physically constructed into a building façade, they create natural disruption glitches in the camera's successful transmission of visual data. The paper details how the quantum of visual aberration in the digital portrayal of the city can be determined by algorithm-based façade patterning.
keywords	surveillance; camoufleur; envisioned; algorithms; diffraction; façades; aberration
series	CAADRIA
email
last changed	2022/06/07 07:58

_id	caadria2017_001
id	caadria2017_001
authors	He, Yi, Schnabel, Marc Aurel, Chen, Rong and Wang, Ning
year	2017
title	A Parametric Analysis Process for Daylight Illuminance - The Influence of Perforated Facade Panels on the Indoor Illuminance
doi	https://doi.org/10.52842/conf.caadria.2017.417
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. 417-424
summary	BIM modelling systems and graph-based modelling systems have been widely used in the architecture design process recently. Based on the systems, an alternative approach to study the influence of perforated façade panels on the indoor illuminance by using a parametric performance analysis in a practical architectural project is proposed. The workflow we developed makes the modelling process faster, more accurate, and easier to modify. From the circulation of modelling-to-analysis process, the performance can be compared, feedback can be generated. Accordingly, optimized design can be concluded. This study suggests an analysis method to evaluate the indoor illuminance performance in the early design stages. The simulation is not a conventional typical in-depth one, but a practical method to immediately evaluate the performance for each design alternative and provide guidelines for design modification. Moreover, the first generation of digital modeling programs allow designers to conceive new forms, and allow these forms to be controled and realized. It reacts to the conference theme by presenting a protocol for a digital workflow in the early stage of the design development.
keywords	Daylight illuminance; BIM; parametric sustainability; parametric modelling; facade panels
series	CAADRIA
email
last changed	2022/06/07 07:49

_id	ijac201715102
id	ijac201715102
authors	Klemmt, Christoph and Klaus Bollinger
year	2017
title	Angiogenesis as a model for the generation of load-bearing networks
source	International Journal of Architectural Computing vol. 15 - no. 1, 18-36
summary	This research suggests an algorithm to generate structural networks based on discreet elements for given locations of support points and point loads. Previous research attempted to achieve this by using a computational growth simulation of venation systems, which form the structure of leaves. However, such networks always start from a single point and therefore cannot be used to form arches or beams. In order to generate networks that are based on two or three support points, an algorithm has been developed that is inspired instead by angiogenesis, the process by which vascular systems develop. The algorithm is based on a spring system with a variable network graph that connects the support points and is pulled upwards and split sideways into multiple veins by a given set of load points. The algorithm has been used to grow architectural structures. Different networks have been tested using finite element analysis and compared with both venation and column-and-beam structures. The angiogenesis networks as well as the venation network are shown to perform well and may be suitable as architectural structural systems.
keywords	Architecture, angiogenesis, structure, network, growth
series	other
type	normal paper
email
last changed	2019/08/02 08:25

_id	caadria2017_016
id	caadria2017_016
authors	Lee, Ju Hyun, Ostwald, Michael J. and Yu, Rongrong
year	2017
title	Investigating Visibility Properties in the Design of Aged-Care Facilities
doi	https://doi.org/10.52842/conf.caadria.2017.365
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. 365-374
summary	This paper uses a Space Syntax approach - a computational and mathematical method using graph-based measurements - to undertake a comparative assessment of the visibility properties of three architectural plans with unusual spatial requirements. Specifically, the method is used to compare the spatio-visual properties of an idealised plan for a residential aged-care facility with the actual plans used for two facilities. The purpose of this analysis is to begin to examine the ways in which syntactical values and isovist properties can be used to capture spatial and social characteristics of plans designed for the physical and cognitive needs of an ageing populace. The application of this approach seeks to support a better understanding of the relationship between spaces and their social properties in the design of aged-care facilities.
keywords	visibility analysis; Space Syntax; spatial cognition; social property
series	CAADRIA
email
last changed	2022/06/07 07:52

_id	cf2017_360
id	cf2017_360
authors	Ofluo?lu, Salih
year	2017
title	BIM-based Interdisciplinary Collaborations in a Student Project Competition
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. 360-373.
summary	Architecture is a profession that requires collaboration among professionals from various fields. Despite the important nature of these interdisciplinary collaborations, architecture students rarely obtain the opportunity to learn about the work areas of other stakeholders and the practice of working together. In all sectors there is a growing need for professionals who possess in-depth knowledge in their own disciplines and also develop an understanding about other related disciplines. In a setting of a student project competition, this article examines how students from various AEC fields collaborate using BIM as a common data environment and emphasizes several considerations for implementing interdisciplinary collaborations in curriculums of architecture schools in students’ perspective.
keywords	Interdisciplinary Collaborations, Architectural Design Studio, BIM, Building Information Modeling
series	CAAD Futures
email
last changed	2017/12/01 14:38

_id	acadia17_474
id	acadia17_474
authors	Peng, Wenzhe; Zhang, Fan; Nagakura, Takehiko
year	2017
title	Machines’ Perception of Space: Employing 3D Isovist Methods and a Convolutional Neural Network in Architectural Space Classification
doi	https://doi.org/10.52842/conf.acadia.2017.474
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. 474- 481
summary	Simple and common architectural elements can be combined to create complex spaces. Different spatial compositions of elements define different spatial boundaries, and each produces a unique local spatial experience to observers inside the space. Therefore an architectural style brings about a distinct spatial experience. While multiple representation methods are practiced in the field of architecture, there lacks a compelling way to capture and identify spatial experiences. Describing an observer’s spatial experiences quantitatively and efficiently is a challenge. In this paper, we propose a method that employs 3D isovist methods and a convolutional neural network (CNN) to achieve recognition of local spatial compositions. The case studies conducted validate that this methodology works well in capturing and identifying local spatial conditions, illustrates the pattern and frequency of their appearance in designs, and indicates peculiar spatial experiences embedded in an architectural style. The case study used small designs by Mies van der Rohe and Aldo van Eyck. The contribution of this paper is threefold. First, it introduces a sampling method based on 3D Isovist that generates a 2D image that can be used to represent a 3D space from a specific observation point. Second, it employs a CNN model to extract features from the sampled images, then classifies their corresponding space. Third, it demonstrates a few case studies where this space classification method is applied to different architectural styles.
keywords	design methods; information processing; AI; machine learning; computer vision; representation
series	ACADIA
email
last changed	2022/06/07 08:00

_id	caadria2017_134
id	caadria2017_134
authors	Schwartz, Mathew and Zarzycki, Andrzej
year	2017
title	Efficacy of Localization Through Magnets Embedded in Infrastructure
doi	https://doi.org/10.52842/conf.caadria.2017.735
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. 735-744
summary	This paper investigates localization and guidance systems as important future considerations for autonomous mobility within the built environment. Specifically, it looks at embedding magnets within building construction assemblies, using magnetic sensors for autonomous navigation, and understanding the impact construction materials may have on magnetic-field-based localization and guidance systems of autonomous agents.
keywords	Autonomous Car; Localization; Infrastructure; Robotics
series	CAADRIA
email
last changed	2022/06/07 07:57

_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	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	ecaade2017_183
id	ecaade2017_183
authors	Wendell, Augustus and Altin, Ersin
year	2017
title	Learning Space - Incorporating spatial simulations in design history coursework
doi	https://doi.org/10.52842/conf.ecaade.2017.1.261
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. 261-266
summary	Art and architectural history education has long relied on photographic imagery. The geography of architectural history often demands an analog representation for the built form and photographic recordings have long been the widely adopted standard. In many cases, specific buildings have been taught for generations based on a handful of historical exposures. The impact of this precedent is an imperfect and highly privileged conception of architectural forms. Students learn only of a particular viewpoint of any given building, rather than understanding the building as a whole. Augmenting the tradition of select and static imagery in the classroom with new technologies can create a more comprehensive understanding of architectural precedents. This paper discusses an experiment conducted in Spring 2017 in presenting an architectural case study to a history class using a Virtual Reality 3D experience in comparison to a set of canonical photographs.
keywords	Unreal Engine; Virtual Reality; Photography; 3D; Education
series	eCAADe
email
last changed	2022/06/07 07:58

_id	ijac201715103
id	ijac201715103
authors	Wortmann, Thomas
year	2017
title	Surveying design spaces with performance maps: A multivariate visualization method for parametric design and architectural design optimization
source	International Journal of Architectural Computing vol. 15 - no. 1, 38-53
summary	This article presents a method to visualize high-dimensional parametric design spaces with applications in computational design processes and interactive optimization. The method extends Star Coordinates using a triangulation-based interpolation with Barycentric Coordinates. It supports the understanding of design problems in architectural design optimization by allowing designers to move between a high-dimensional design space and a low-dimensional Performance Map. This Performance Map displays the characteristics of the fitness landscape, develops designers’ intuitions about the relationships between design parameters and performance, allows designers to examine promising design variants, and delineates promising areas for further design exploration.
keywords	Fitness landscape, design space exploration, multivariate visualization, optimization, Star Coordinates
series	other
type	normal paper
email
last changed	2019/08/02 08:25

_id	caadria2017_113
id	caadria2017_113
authors	Huang, Weixin, Lin, Yuming and Wu, Mingbo
year	2017
title	Spatial-Temporal Behavior Analysis Using Big Data Acquired by Wi-Fi Indoor Positioning System
doi	https://doi.org/10.52842/conf.caadria.2017.745
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. 745-754
summary	Understanding of people's spatial behavior is fundamental to architectural and urban design. However, traditional investigation methods applied in environmental behavior studies is highly limited regarding the amount of samples and regions it covers, which is not sufficient for the exploration of complex dynamic human behaviors and social activities in architectural space. Only recently the developments in indoor positioning system (IPS) and big data analysis technique have made it possible to conduct a full-time, full-coverage study on human environmental behavior. Among the variety IPS systems, the Wi-Fi IPS system is increasingly widely used because it is easy to be applied with acceptable cost. In this paper, we analyzed a 60-days anonymized data set, collected by a Wi-Fi IPS system with 110 Wi-Fi access points. The analysis revealed interesting patterns on people's behavior besides temporal spatial distribution, ranging from the cyclical fluctuation in human flow to behavioral patterns of sub-regions, some of which are not easy to be identified and interpreted by the traditional field observation. Through this case study, behavioral data from IPS system has exhibited great potential in bringing about profound changes in the study of environmental behavior.
keywords	environmental behavior study; Wi-Fi; indoor positioning system; big data; spatial temporal behavior; ski resort
series	CAADRIA
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_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
doi	https://doi.org/10.52842/conf.ecaade.2017.2.277
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
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	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	ecaade2017_057
id	ecaade2017_057
authors	Al-Qattan, Emad, Yan, Wei and Galanter, Philip
year	2017
title	Tangible Computing for Establishing Generative Algorithms - A Case Study with Cellular Automata
doi	https://doi.org/10.52842/conf.ecaade.2017.1.347
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. 347-354
summary	The work presented in this paper investigates the potential of tangible interaction to setup algorithmic rules for creating computational models. The research proposes a workflow that allows designers to create complex geometric patterns through their physical interaction with design objects. The method aims to address the challenges of designers implementing algorithms for computational modeling. The experiments included in this work are prototype-based, which link a digital environment with an artifact - the physical representation of a digital model that is integrated with a Physical Computing System. The digital-physical workflow is tested through enabling users to physically setup the rules of a Cellular Automata algorithm. The experiments demonstrate the possibility of utilizing tangible interaction to setup the initial cell state and the rules of a CA algorithm to generate complex geometric patterns.
keywords	Physical Computing; Tangible User-Interface; Cellular Automata
series	eCAADe
email
last changed	2022/06/07 07:54

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