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	caadria2021_305
id	caadria2021_305
authors	Keshavarzi, Mohammad, Afolabi, Oladapo, Caldas, Luisa, Yang, Allen Y. and Zakhor, Avideh
year	2021
title	GenScan: A Generative Method for Populating Parametric 3D Scan Datasets
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 91-100
doi	https://doi.org/10.52842/conf.caadria.2021.1.091
summary	The availability of rich 3D datasets corresponding to the geometrical complexity of the built environments is considered an ongoing challenge for 3D deep learning methodologies. To address this challenge, we introduce GenScan, a generative system that populates synthetic 3D scan datasets in a parametric fashion. The system takes an existing captured 3D scan as an input and outputs alternative variations of the building layout including walls, doors, and furniture with corresponding textures. GenScan is fully automated system that can also be manually controlled by a user through an assigned user interface. Our proposed system utilizes a combination of a hybrid deep neural network and a parametrizer module to extract and transform elements of a given 3D scan. GenScan takes advantage of style transfer techniques to generate new textures for the generated scenes. We believe our system would facilitate data augmentation to expand the currently limited 3D geometry datasets commonly used in 3D computer vision, generative design and general 3D deep learning tasks.
keywords	Computational Geometry; Generative Modeling; 3D Manipulation; Texture Synthesis
series	CAADRIA
email
last changed	2022/06/07 07:52

_id	caadria2021_052
id	caadria2021_052
authors	Yousif, Shermeen and Bolojan, Daniel
year	2021
title	Deep-Performance - Incorporating Deep Learning for Automating Building Performance Simulation in Generative Systems
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 151-160
doi	https://doi.org/10.52842/conf.caadria.2021.1.151
summary	In this study, we introduce a newly developed method called Deep-Performance, to enable automatic environmental performance simulation prediction without the need to perform simulations, by integrating deep learning strategies. The aim is to train neural networks on datasets with thousands of building design samples and their corresponding performance simulation. The trained model would offer performance prediction for design options emerging in generative protocols. The research is a work-in-progress within a broader project aimed at automating buildings environmental performance evaluations of daylight analysis and energy simulation, using deep learning (DL) models. This paper focuses on the implementation of a supervised DL method for automating the retrieval of daylight analysis metrics, targeting successful daylight design and higher building enclosure efficiency. We have further improved a Pix2Pix model trained on 5 different datasets, each containing 6000 paired images of architectural floor plans and their daylight simulation metrics. In the inference phase, the model was able to accurately predict the daylight simulation for unseen sets of floor plans. For validation, two quantitative assessment metrics were followed to assess the predicted daylight performance against the daylight performance simulation. Both assessment metrics showed high accuracy levels.
keywords	Deep Learning; Artificial Intelligence; Deep-Performance; Automating Building Performance Simulation; Generative Systems
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	ecaade2021_254
id	ecaade2021_254
authors	Eisenstadt, Viktor, Arora, Hardik, Ziegler, Christoph, Bielski, Jessica, Langenhan, Christoph, Althoff, Klaus-Dieter and Dengel, Andreas
year	2021
title	Comparative Evaluation of Tensor-based Data Representations for Deep Learning Methods in Architecture
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 45-54
doi	https://doi.org/10.52842/conf.ecaade.2021.1.045
summary	This paper presents an extended evaluation of tensor-based representations of graph-based architectural room configurations. This experiment is a continuation of examination of recognition of semantic architectural features by contemporary standard deep learning methods. The main aim of this evaluation is to investigate how the deep learning models trained using the relation tensors as data representation means perform on data not available in the training dataset. Using a straightforward classification task, stepwise modifications of the original training dataset and manually created spatial configurations were fed into the models to measure their prediction quality. We hypothesized that the modifications that influence the class label will not decrease this quality, however, this was not confirmed and most likely the latent non-class defining features make up the class for the model. Under specific circumstances, the prediction quality still remained high for the winning relation tensor type.
keywords	Deep Learning; Spatial Configuration; Semantic Building Fingerprint
series	eCAADe
email
last changed	2022/06/07 07:55

_id	acadia21_470
id	acadia21_470
authors	£ochnicki, Grzegorz; Kalousdian, Nicolas Kubail; Leder, Samuel; Maierhofer, Mathias; Wood, Dylan; Menges, Achim
year	2021
title	Co-Designing Material-Robot Construction Behaviors
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 470-479.
doi	https://doi.org/10.52842/conf.acadia.2021.470
summary	This paper presents research on designing distributed, robotic construction systems in which robots are taught construction behaviors relative to the elastic bending of natural building materials. Using this behavioral relationship as a driver, the robotic system is developed to deal with the unpredictability of natural materials in construction and further to engage their dynamic characteristics as methods of locomotion and manipulation during the assembly of actively bent structures. Such an approach has the potential to unlock robotic building practice with rapid-renewable materials, whose short crop cycles and small carbon footprints make them particularly important inroads to sustainable construction. The research is conducted through an initial case study in which a mobile robot learns a control policy for elastically bending bamboo bundles into designed configurations using deep reinforcement learning algorithms. This policy is utilized in the process of designing relevant structures, and for the in-situ assembly of these designs. These concepts are further investigated through the co-design and physical prototyping of a mobile robot and the construction of bundled bamboo structures. This research demonstrates a shift from an approach of absolute control and predictability to behavior-based methods of assembly. With this, materials and processes that are often considered too labor-intensive or unpredictable can be reintroduced. This reintroduction leads to new insights in architectural design and construction, where design outcome is uniquely tied to the building material and its assembly logic. This highly material-driven approach sets the stage for developing an effective, sustainable, light-touch method of building using natural materials.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ascaad2021_146
id	ascaad2021_146
authors	Aly, Zeyad; Aly Ibrahim, Sherif Abdelmohsen
year	2021
title	Augmenting Passive Actuation of Hygromorphic Skins in Desert Climates: Learning from Thorny Devil Lizard Skins
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 264-278
summary	The exploitation of latent properties of natural materials such as wood in the passive actuation of adaptive building skins is of growing interest due to their added value as a low-cost and low-energy approach. The control of wood response behavior is typically conducted via physical experiments and numerical simulations that explore the impact of hygroscopic design parameters. Desert climates however suffer from water scarcity and high temperatures. Complementary mechanisms are needed to provide sufficient sources of water for effective hygroscopic operation. This paper aims to exploit such mechanisms, with specific focus on thorny devil lizard skins whose microstructure surface properties allow for maximum humidity absorption. We put forward that this process enhances hygroscopic-based passive actuation systems and their adaptation to both humidity and temperature in desert climates. Specific parameters and rules are deduced based on the lizard skin properties. Physical experiments are conducted to observe different actuation mechanisms. These mechanisms are recorded, and texture and bending morphologies are modeled for adaptive skins using Grasshopper.
series	ASCAAD
email
last changed	2021/08/09 13:13

_id	acadia21_238
id	acadia21_238
authors	Anifowose, Hassan; Yan, Wei; Dixit, Manish
year	2021
title	BIM LOD + Virtual Reality
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 238-245.
doi	https://doi.org/10.52842/conf.acadia.2021.238
summary	Architectural Education faces limitations due to its tactile approach to learning in classrooms with only 2-D and 3-D tools. At a higher level, virtual reality provides a potential for delivering more information to individuals undergoing design learning. This paper investigates a hypothesis establishing grounds towards a new research in Building Information Modeling (BIM) and Virtual Reality (VR). The hypothesis is projected to determine best practices for content creation and tactile object virtual interaction, which potentially can improve learning in architectural & construction education with a less costly approach and ease of access to well-known buildings. We explored this hypothesis in a step-by-step game design demonstration in VR, by showcasing the exploration of the Farnsworth House and reproducing assemblage of the same with different game levels of difficulty which correspond with varying BIM levels of development (LODs). The game design prototype equally provides an entry way and learning style for users with or without a formal architectural or construction education seeking to understand design tectonics within diverse or cross-disciplinary study cases. This paper shows that developing geometric abstract concepts of design pedagogy, using varying LODs for game content and levels, while utilizing newly developed features such as snap-to-grid, snap-to-position and snap-to-angle to improve user engagement during assemblage may provide deeper learning objectives for architectural precedent study.
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
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	ecaade2021_230
id	ecaade2021_230
authors	De Luca, Francesco, Sepúlveda, Abel and Varjas, Toivo
year	2021
title	Static Shading Optimization for Glare Control and Daylight
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. 419-428
doi	https://doi.org/10.52842/conf.ecaade.2021.2.419
summary	Daylight and solar access influence positively building occupants' wellbeing and students' learning performance. However, an excess of sunlight can harm the visual comfort of occupants through disturbing glare effects. This study investigated, through multi-objective optimization, the potential of static shading devices to reduce glare and to guarantee daylight provision in a university building. The results showed that the reduction of disturbing glare was up to more than twice the reduced daylight, which nevertheless, was provided in adequate levels. View out and energy performance were also analyzed. Detailed results of optimal shading types and classrooms layout indications are presented.
keywords	Daylight; Visual comfort; Shading; Multi-objective optimization
series	eCAADe
email
last changed	2022/06/07 07:55

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

_id	cdrf2021_275
id	cdrf2021_275
authors	E. Özdemir, L. Kiesewetter, K. Antorveza, T. Cheng, S. Leder, D. Wood, and A. Menges
year	2021
title	Towards Self-shaping Metamaterial Shells: A Computational Design Workflow for Hybrid Additive Manufacturing of Architectural Scale Double-Curved Structures
source	Proceedings of the 2021 DigitalFUTURES The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021)
doi	https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_26
summary	Double curvature enables elegant and material-efficient shell structures, but their construction typically relies on heavy machining, manual labor, and the additional use of material wasted as one-off formwork. Using a material’s intrinsic properties for self-shaping is an energy and resource-efficient solution to this problem. This research presents a fabrication approach for self-shaping double-curved shell structures combining the hygroscopic shape-changing and scalability of wood actuators with the tunability of 3D-printed metamaterial patterning. Using hybrid robotic fabrication, components are additively manufactured flat and self-shape to a pre-programmed configuration through drying. A computational design workflow including a lattice and shell-based finite element model was developed for the design of the metamaterial pattern, actuator layout, and shape prediction. The workflow was tested through physical prototypes at centimeter and meter scales. The results show an architectural scale proof of concept for self-shaping double-curved shell structures as a resource-efficient physical form generation method.
series	cdrf
email
last changed	2022/09/29 07:53

_id	caadria2021_086
id	caadria2021_086
authors	Eisenstadt, Viktor, Arora, Hardik, Ziegler, Christoph, Bielski, Jessica, Langenhan, Christoph, Althoff, Klaus-Dieter and Dengel, Andreas
year	2021
title	Exploring optimal ways to represent topological and spatial features of building designs in deep learning methods and applications for architecture
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 191-200
doi	https://doi.org/10.52842/conf.caadria.2021.1.191
summary	The main aim of this research is to harness deep learning techniques to support architectural design problems in early design phases, for example, to enable auto-completion of unfinished designs. For this purpose, we investigate the possibilities offered by established deep learning libraries such as TensorFlow. In this paper, we address a core challenge that arises, namely the transformation of semantic building information into a tensor format that can be processed by the libraries. Specifically, we address the representation of information about room types of a building and type of connection between the respective rooms. We develop and discuss five formats. Results of an initial evaluation based on a classification task show that all formats are suitable for training deep learning networks. However, a clear winner could be determined as well, for which a maximum value of 98% for validation accuracy could be achieved.
keywords	deep learning; spatial configuration; data representation; semantic building fingerprint
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	ecaade2021_115
id	ecaade2021_115
authors	Foged, Isak and Hilmer, Jacob
year	2021
title	Fiber Compositions - Development of wood and textile layered structures as a material strategy for sustainable design
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. 443-452
doi	https://doi.org/10.52842/conf.ecaade.2021.2.443
summary	This study examines composite compositions based on fiber-based materials. It focuses on organic textiles of Jute, Hemp, Wool, Flax, and Glass fiber as a synthetic textile, combined with the lightweight wood species Paulownia. By creating novel composites, the study aims to investigate methods and generate design knowledge for material strategies to improve and reduce material waste in the built environment, further enabled by the use of small elements that can be sourced from waste wood and reclaimed wood. Research is conducted as a hybrid material-computational methodology, developing and testing probes, prototypes and a full-scale demonstrator assembly in the form of a wall seating composition. The results find that the proposed method and resulting composites have significant potentials for both expressive and functional characteristics, allowing tectonic articulation to be made, while creating minimum material structures based on assembly of small elements to larger complex curvature building parts.
keywords	Wood; Textile; Composite; Computational Design; Environmental Design
series	eCAADe
email
last changed	2022/06/07 07:51

_id	ijac202119205
id	ijac202119205
authors	Fukuda, Tomohiro; Marcos Novak, Hiroyuki Fujii, Yoann Pencreach
year	2021
title	Virtual reality rendering methods for training deep learning, analysing landscapes, and preventing virtual reality sickness
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 2, 190–207
summary	Virtual reality (VR) has been proposed for various purposes such as design studies, presentation, simulation and communication in the field of computer-aided architectural design. This paper explores new roles for VR; in particular, we propose rendering methods that consist of post-processing rendering, segmentation rendering and shadow-casting rendering for more-versatile approaches in the use of data. We focus on the creation of a dataset of annotated images, composed of paired foreground-background and semantic-relevant images, in addition to traditional immersive rendering for training deep learning neural networks and analysing landscapes. We also develop a camera velocity rendering method using a customised segmentation rendering technique that calculates the linear and angular velocities of the virtual camera within the VR space at each frame and overlays a colour on the screen according to the velocity value. Using this velocity information, developers of VR applications can improve the animation path within the VR space and prevent VR sickness. We successfully applied the developed methods to urban design and a design project for a building complex. In conclusion, the proposed method was evaluated to be both feasible and effective.
keywords	Virtual reality, rendering, shader, deep learning, landscape analytics, virtual reality sickness, Fourth Industrial Revolution, computer-aided architectural design
series	journal
email
last changed	2024/04/17 14:29

_id	caadria2021_118
id	caadria2021_118
authors	Huang, Chien-hua
year	2021
title	Reinforcement Learning for Architectural Design-Build - Opportunity of Machine Learning in a Material-informed Circular Design Strategy
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 171-180
doi	https://doi.org/10.52842/conf.caadria.2021.1.171
summary	This paper discusses the potentials of reinforcement learning in game engine for design, implementation, and construction of architecture. It inaugurates a new design tool that promotes a material-informed design-build workflow for architectural design and construction industries that achieves a comprehensive circular economy. As a proof of concept, it uses the project Reform Standard, a machine-learning-based searching system that designs new shell structures composed of existing wasted materials, as a demonstration to discuss how reinforcement learning, machine vision and automated searching algorithm in the game engine can promote a material-aware design and converts wastes into construction materials. The demonstrator project sorts and transforms irregular chunks of wasted broken plastics into a new form. Instead of recycling those wastes in an energy-intensive process, the game engine is capable of finding the intricacy and new machine-oriented aesthetics in those otherwise neglected wastes. Furthermore, future research directions such as robotic-aided construction are discussed by exposing the potentials and problems in the demonstrated project. Finally, the future circular strategy is discussed beyond the demonstrated tests and local uses. The standardization of material, legislation and material lifecycle needs to be comprehensively considered and designed by architects and designers during conceptual design phase.
keywords	Reinforcement Learning; ML-Agents; Unity3D; circular design; geometric analysis
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	caadria2021_117
id	caadria2021_117
authors	Ikeno, Kazunosuke, Fukuda, Tomohiro and Yabuki, Nobuyoshi
year	2021
title	Can a Generative Adversarial Network Remove Thin Clouds in Aerial Photographs? - Toward Improving the Accuracy of Generating Horizontal Building Mask Images for Deep Learning in Urban Planning and Design
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 377-386
doi	https://doi.org/10.52842/conf.caadria.2021.2.377
summary	Information extracted from aerial photographs is widely used in the fields of urban planning and architecture. An effective method for detecting buildings in aerial photographs is to use deep learning to understand the current state of a target region. However, the building mask images used to train the deep learning model must be manually generated in many cases. To overcome this challenge, a method has been proposed for automatically generating mask images by using textured 3D virtual models with aerial photographs. Some aerial photographs include thin clouds, which degrade image quality. In this research, the thin clouds in these aerial photographs are removed by using a generative adversarial network, which leads to improvements in training accuracy. Therefore, the objective of this research is to propose a method for automatically generating building mask images by using 3D virtual models with textured aerial photographs to enable the removable of thin clouds so that the image can be used for deep learning. A model trained on datasets generated by the proposed method was able to detect buildings in aerial photographs with an accuracy of IoU = 0.651.
keywords	Urban planning and design; Deep learning; Generative Adversarial Network (GAN); Semantic segmentation; Mask image
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	ecaade2021_291
id	ecaade2021_291
authors	Mondal, Joy
year	2021
title	Differences between Architects' and Non-architects' Visual Perception of Originality of Tower Typology - Quantification of subjective evaluation using Deep Learning
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 65-74
doi	https://doi.org/10.52842/conf.ecaade.2021.1.065
summary	The paper presents a computational methodology to quantify the differences in visual perception of originality of the rotating tower typology between architects and non-architects. A parametric definition of the Absolute Tower Building D with twelve variables is used to generate 250 design variants. Subsequently, sixty architects and sixty non-architects were asked to rate the design variants, in comparison to the original design, on a Likert scale of 'Plagiarised' to 'Original'. With the crowd-sourced evaluation data, two neural networks - one each for architects and non-architects - were trained to predict the originality score of 15,000 design variants. The results indicate that architects are more lenient at seeing design variants as original. The average originality score by architects is 27.74% higher than the average originality score by non-architects. Compared to a non-architect, an architect is 1.93 times likelier to see a design variant as original. In 92.01% of the cases, architects' originality score is higher than non-architects'. The methodology can be used to capture and predict any subjective opinion.
keywords	Originality; Visual perception; Crowd-sourced; Subjective evaluation; Deep learning; Neural network
series	eCAADe
email
last changed	2022/06/07 07:58

_id	ecaadesigradi2019_478
id	ecaadesigradi2019_478
authors	Nardelli, Eduardo Sampaio
year	2019
title	BIM training in Brazil - Preparing professionals for BIM adoption by public administration
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 305-314
doi	https://doi.org/10.52842/conf.ecaade.2019.2.305
summary	On May 2018 the Brazilian federal government published the Decree 9.377 setting a National Strategy for Information and Dissemination of Building Information Modelling - BIM to enable its adoption by public administration. This strategy has nine targets and among them the task of training professionals in BIM to support the demand that should be generated. A period between 2018 and 2021 has been planned to establish learning objectives and develop model disciplines, a process that, however, should not start from scratch because there are already some BIM training initiatives being performed in the country since the early 2000s. This paper has done an overview on this production highlighting some relevant conceptual contributions to this debate aiming to address challenges and possible ways to support the expected Architectural and Engineering courses restructuring.
keywords	BIM, Education, Architecture, Engineering and Construction
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:58

_id	ecaade2021_150
id	ecaade2021_150
authors	Song, Yanan and Yuan, Philip F.
year	2021
title	A Research On Building Cluster Morphology Formation Based On Wind Environmental Performance And Deep Reinforcement Learning
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 335-344
doi	https://doi.org/10.52842/conf.ecaade.2021.1.335
summary	Nowadays, numerous researchers emphasize the significance of the environmen-tal performance-driven generative methodology. However, due to the complex coupling mechanism of environmental regulation factors, the existing optimiza-tion engines and applications are time-consuming and cumbersome. In this re-search, we propose a novel design methodology based on Deep Reinforcement Learning (DRL). This paper is divided into 3 sections, including theoretical framework, design strategy, and practical application. It first introduces an over-view of basic principles, illustrating the potential advantages of DRL in perfor-mance data-driven design. Based on this, the paper proposes a DRL-based gener-ative method. We point out a more specific discussion about the application and workflow of core DRL elements in architectural design. Finally, taking a grid-form urban space composed by multitude high-rise building blocks as an exam-ple, we present a application through a DRL agent to conduct numerous active wind environmental performance-based design tests. It is an interactive and gen-erative design method, owning multiple advantages of timeliness, convenience, and intelligence.
keywords	Deep Reinforcement Learning; Environmental Performance Design; Generative Design; Building Cluster Formation
series	eCAADe
email
last changed	2022/06/07 07:56

_id	caadria2021_042
id	caadria2021_042
authors	Sun, Chengyu, Lin, Yinshan and Li, Shuyang
year	2021
title	Automatic Generation of Signboards in Large-Scale Transportation Building Driven by Passengers' Paths
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 11-20
doi	https://doi.org/10.52842/conf.caadria.2021.1.011
summary	The signage design of any large-scale transportation building is vital to its passengers wayfinding experiences. Firstly, a set of passengers paths should be re-designed by signage designers according to the latest requirements, which always deviates from the initial ones in large-scale projects or inevitably updates during a long-term running. Afterwards, the path design has to be transformed into the layout and content of signboards manually. It is a time-consuming and error-prone process. This study introduces a human-computer hybrid workflow keeping the flexible path design in the hands of designers and leaving the following procedures to an algorithm, which automatically generates signboard contents ready for construction. It is proved efficient with more than 3000 signboards in the project of PVG Airport, Shanghai. Furthermore, the designer got an opportunity to optimize his path design through various alternatives, which impossible traditionally.
keywords	Design Automation; Human-Computer Hybrid; Signboard; Passenger Path; Transportation Building
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	ascaad2021_153
id	ascaad2021_153
authors	Valitabar, Mahdi; Mohammadjavad Mahdavinejad, Henry Skates, Peiman Pilechiha
year	2021
title	Data-Driven Design of Adaptive Façades: View, Glare, Daylighting and Energy Efficiency
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 699-711
summary	This paper attempts to increase occupants’ view to outside through Adaptive facades by employing a parametric design method. Reaching a balance between occupants’ requirements and the building energy criteria is the main objective of this research. To this end, a multi-objective optimization is done to generate some optimum models. The method, indeed, was used to optimize the shading size of a dynamic vertical shading system utilized on the south façade of a single office room located in Tehran. The shading system was defined by five parameters and a combination of Cut-off and a glare protection strategy is used to control dynamic shadings. The size-optimisation objectives are minimum DGP, cooling load and maximum illuminance, which were analysed by Ladybug Tools. Then, Octopus was used for multi-objective optimistion to find new optimum forms. Along with the openness factor, a new index is presented to evaluate the outside view in multiple louver shading systems, named “Openness Curvature Factor” (OCF). Thanks to this method, the size and shape of some optimum generated models were modified to increase the amount of OCF. Following that the Honeybee Plus is used to simulate the visual performance of modified models which shows a significant improvement. The modified models could provide about 4 times more outside view than generated models whilst keeping the DGP value in imperceptible range. Geometric or even complex non-geometric shading forms can be studied by this method to find optimum adaptive facades.
series	ASCAAD
email
last changed	2021/08/09 13:14

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