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	caadria2020_161
id	caadria2020_161
authors	Kido, Daiki, Fukuda, Tomohiro and Yabuki, Nobuyoshi
year	2020
title	Mobile Mixed Reality for Environmental Design Using Real-Time Semantic Segmentation and Video Communication - Dynamic Occlusion Handling and Green View Index Estimation
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 681-690
doi	https://doi.org/10.52842/conf.caadria.2020.1.681
summary	Mixed reality (MR), that blends the real and virtual worlds, attracted attention for consensus-building among stakeholders in environmental design with the visualization of planned landscape onsite. One of the technical challenges in MR is the occlusion problem which occurs when virtual objects hide physical objects that should be rendered in front of virtual objects. This problem may cause inappropriate simulation. And the visual environmental assessment of present and proposed landscape with MR can be effective for the evidence-based design, such as urban greenery. Thus, this study aims to develop a MR-based environmental assessment system with dynamic occlusion handling and green view index estimation using semantic segmentation based on deep learning. This system was designed for the use on a mobile device with video communication over the Internet to implement a real-time semantic segmentation whose computational cost is high. The applicability of the developed system is shown through case studies.
keywords	Mixed Reality (MR); Environmental Design; Dynamic Occlusion Handling; Semantic Segmentation; Green View Index
series	CAADRIA
email
last changed	2022/06/07 07:52

_id	caadria2020_203
id	caadria2020_203
authors	Xiao, Yahan, Chen, Sen, Ikeda, Yasushi and Hotta, Kensuke
year	2020
title	Automatic Recognition and Segmentation of Architectural Elements from 2D Drawings by Convolutional Neural Network
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 843-852
doi	https://doi.org/10.52842/conf.caadria.2020.1.843
summary	The BIM modeling process is the most time-consuming aspect. This paper studies the possibility of applying the recognition and segmentation of architectural components by deep learning to assist automatic BIM modeling. The research has two parts: the first one is dataset preparing, that images with the labeled architectural components from an original CAD drawing are made for the network training, and second is training and testing, that a mature network which has been trained in hundreds of labeled images is used to make predictions. The utilization of the current study results is discussed and the optimization method as well.
keywords	BIM; CAD drawings; Recognition and Segmentation; Convolutional Neural Network; Computer vision
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	acadia20_228
id	acadia20_228
authors	Alawadhi, Mohammad; Yan, Wei
year	2020
title	BIM Hyperreality
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. 228-236.
doi	https://doi.org/10.52842/conf.acadia.2020.1.228
summary	Deep learning is expected to offer new opportunities and a new paradigm for the field of architecture. One such opportunity is teaching neural networks to visually understand architectural elements from the built environment. However, the availability of large training datasets is one of the biggest limitations of neural networks. Also, the vast majority of training data for visual recognition tasks is annotated by humans. In order to resolve this bottleneck, we present a concept of a hybrid system—using both building information modeling (BIM) and hyperrealistic (photorealistic) rendering—to synthesize datasets for training a neural network for building object recognition in photos. For generating our training dataset, BIMrAI, we used an existing BIM model and a corresponding photorealistically rendered model of the same building. We created methods for using renderings to train a deep learning model, trained a generative adversarial network (GAN) model using these methods, and tested the output model on real-world photos. For the specific case study presented in this paper, our results show that a neural network trained with synthetic data (i.e., photorealistic renderings and BIM-based semantic labels) can be used to identify building objects from photos without using photos in the training data. Future work can enhance the presented methods using available BIM models and renderings for more generalized mapping and description of photographed built environments.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	acadia20_406
id	acadia20_406
authors	Duong, Eric; Vercoe, Garrett; Baharlou, Ehsan
year	2020
title	Engelbart
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. 406-415.
doi	https://doi.org/10.52842/conf.acadia.2020.1.406
summary	The internet has long been viewed as a cyberspace of free and collective information, allowing for an increase in the diversity of ideas and viewpoints available to the general public. However, critics argue that the emergence of personalization algorithms on social media and other internet platforms instead reduces information diversity by forming “filter bubbles"" of viewpoints similar to the user’s own. The adoption of these personalization algorithms is due in part to advancements in natural language processing, which allow for textual analysis at unprecedented scales. This paper aims to utilize natural language processing and architectural spatial principles to present social media from a collective viewpoint rather than a personalized one. To accomplish this, the paper introduces Engelbart, a data-driven agent-based system, where real-time Twitter conversations are visualized within a two-dimensional environment. This environment is interacted with by the artificial intelligence (AI) agent, Engelbart, which summarizes crowdsourced thoughts and feelings about current trending topics. The functionality of this web application comes from the natural language processing of thousands of tweets per minute throughout several layers of operations, including sentiment analysis and word embeddings. Presented as an understandable interface, it incorporates the values of cybernetics, cyberspace, agent-based modeling, and data ethics to show the potential for social media to become a more transparent space for collective discussion.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2020_222
id	ecaade2020_222
authors	Ikeno, Kazunosuke, Fukuda, Tomohiro and Yabuki, Nobuyoshi
year	2020
title	Automatic Generation of Horizontal Building Mask Images by Using a 3D Model with Aerial Photographs for Deep Learning
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 271-278
doi	https://doi.org/10.52842/conf.ecaade.2020.2.271
summary	Information extracted from aerial photographs is widely used in urban planning and design. An effective method for detecting buildings in aerial photographs is to use deep learning for understanding the current state of a target region. However, the building mask images used to train the deep learning model are manually generated in many cases. To solve this challenge, a method has been proposed for automatically generating mask images by using virtual reality 3D models for deep learning. Because normal virtual models do not have the realism of a photograph, it is difficult to obtain highly accurate detection results in the real world even if the images are used for deep learning training. Therefore, the objective of this research is to propose a method for automatically generating building mask images by using 3D models with textured aerial photographs for deep learning. The model trained on datasets generated by the proposed method could detect buildings in aerial photographs with an accuracy of IoU = 0.622. Work left for the future includes changing the size and type of mask images, training the model, and evaluating the accuracy of the trained model.
keywords	Urban planning and design; Deep learning; Semantic segmentation; Mask image; Training data; Automatic design
series	eCAADe
email
last changed	2022/06/07 07:50

_id	caadria2020_107
id	caadria2020_107
authors	Meng, Leo Lin, Graham, Jeremy and Haeusler, M. Hank
year	2020
title	t-SNE: A Dimensionality Reduction Tool for Design Data Visualisation
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 629-638
doi	https://doi.org/10.52842/conf.caadria.2020.2.629
summary	One can argue that data is the 'new oil'. Yet more important than the sheer quantity of data is the question, in the context of architecture and design, how data is represented in design, as this is becoming a more relevant question to the architecture profession. We argue that data, in particular n-dimensional, is often hidden even in BIM models. Hence we propose a new way of understanding the space by (1) generate and integrate space analytics data using space syntax method as well as space usage data and (2) visualise the data using t-Distributed Stochastic Neighbour Embedding (t-SNE), an unsupervised learning and dimensionality reduction tool to help intuitively display high dimensions of data. This approach may help to discover the 'hidden layers' of the building information that may be otherwise omitted. This investigation, its proposed hypothesis, methodology, implications, significance and evaluation are presented in the paper.
keywords	Data-Driven Design; t-SNE; Machine Learning; Space Syntax
series	CAADRIA
email
last changed	2022/06/07 07:58

_id	caadria2020_028
id	caadria2020_028
authors	Xia, Yixi, Yabuki, Nobuyoshi and Fukuda, Tomohiro
year	2020
title	Development of an Urban Greenery Evaluation System Based on Deep Learning and Google Street View
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 783-792
doi	https://doi.org/10.52842/conf.caadria.2020.1.783
summary	Street greenery has long played a vital role in the quality of urban landscapes and is closely related to people's physical and mental health. In the current research on the urban environment, researchers use various methods to simulate and measure urban greenery. With the development of computer technology, the way to obtain data is more diverse. For the assessment of urban greenery quality, there are many methods, such as using remote sensing satellite images captured from above (antenna, space) sensors, to assess urban green coverage. However, this method is not suitable for the evaluation of street greenery. Unlike most remote sensing images, from a pedestrian perspective, urban street images are the most common view of green plants. The street view image presented by Google Street View image is similar to the captured by the pedestrian perspective. Thus it is more suitable for studying urban street greening. With the development of artificial intelligence, based on deep learning, we can abandon the heavy manual statistical work and obtain more accurate semantic information from street images. Furthermore, we can also measure green landscapes in larger areas of the city, as well as extract more details from street view images for urban research.
keywords	Green View Index; Deep Learning; Google Street View; Segmentation
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	acadia20_238
id	acadia20_238
authors	Zhang, Hang
year	2020
title	Text-to-Form
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. 238-247.
doi	https://doi.org/10.52842/conf.acadia.2020.1.238
summary	Traditionally, architects express their thoughts on the design of 3D architectural forms via perspective renderings and standardized 2D drawings. However, as architectural design is always multidimensional and intricate, it is difficult to make others understand the design intention, concrete form, and even spatial layout through simple language descriptions. Benefiting from the fast development of machine learning, especially natural language processing and convolutional neural networks, this paper proposes a Linguistics-based Architectural Form Generative Model (LAFGM) that could be trained to make 3D architectural form predictions based simply on language input. Several related works exist that focus on learning text-to-image generation, while others have taken a further step by generating simple shapes from the descriptions. However, the text parsing and output of these works still remain either at the 2D stage or confined to a single geometry. On the basis of these works, this paper used both Stanford Scene Graph Parser (Sebastian et al. 2015) and graph convolutional networks (Kipf and Welling 2016) to compile the analytic semantic structure for the input texts, then generated the 3D architectural form expressed by the language descriptions, which is also aided by several optimization algorithms. To a certain extent, the training results approached the 3D form intended in the textual description, not only indicating the tremendous potential of LAFGM from linguistic input to 3D architectural form, but also innovating design expression and communication regarding 3D spatial information.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	acadia20_426
id	acadia20_426
authors	Zohier, Islam; EL Antably, Ahmed; S. Madani, Ahmed
year	2020
title	An AI Lens on Historic Cairo
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. 426-434.
doi	https://doi.org/10.52842/conf.acadia.2020.1.426
summary	Reports show that numerous heritage sites are in danger due to conflicts and heritage mismanagement in many parts of the world. Experts have resorted to digital tools to attempt to conserve and preserve endangered and damaged sites. To that end, in this applied research, we aim to develop a deep learning framework applied to the decaying tangible heritage of Historic Cairo, known as “The City of a Thousand Minarets.” The proposed framework targets Cairo’s historic minaret styles as a test case study for the broader applications of deep learning in digital heritage. It comprises recognition and segmentation tasks, which use a deep learning semantic segmentation model trained on two data sets representing the two most dominant minaret styles in the city, Mamluk (1250–1517 CE) and Ottoman (1517–1952 CE). The proposed framework aims to classify these two types using images. It can help create a multidimensional model from just a photograph of a historic building, which can quickly catalog and document a historic building or element. The study also sheds light on the obstacles preventing the exploration and implementation of deep learning techniques in digital heritage. The research presented in this paper is a work-in-progress of a larger applied research concerned with implementing deep learning techniques in the digital heritage domain.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2020_064
id	ecaade2020_064
authors	Agirbas, Asli
year	2020
title	Building Energy Performance of Complex Forms - Test simulation of minimal surface-based form optimization
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 259-268
doi	https://doi.org/10.52842/conf.ecaade.2020.1.259
summary	Many optimization tools are developed in line with the form-energy relationship to ensure energy efficiency in buildings. However, such studies with complex forms are very limited. Therefore, the MSO-2 model was developed. In this model, on the roof of the conceptual form, minimal surface is used, thus complex forms can be created. In this model, the conceptual form can be optimized (for one day) according to these objectives: increasing daylight in the space with maximum value limitation, reducing radiation on the roof, and enlarging floor surface area of the conceptual form with minimum value limitation. A test simulation was performed with this model. Thus, in order to find the most optimized form in multi-objective optimization, more generations could be produced in a short time and optimized conceptual forms, which were produced, could be tested for energy efficiency.
keywords	Multi-Objective Optimization; Radiation Analysis; Building energy performance; Daylighting Analysis
series	eCAADe
email
last changed	2022/06/07 07:54

_id	ijac202018205
id	ijac202018205
authors	Ahlquist, Sean
year	2020
title	Negotiating human engagement and the fixity of computational design: Toward a performative design space for the differently-abled bodymind
source	International Journal of Architectural Computing vol. 18 - no. 2, 174-193
summary	Computational design affords agency: the ability to orchestrate the material, spatial, and technical architectural system. In this specific case, it occurs through enhanced, authored means to facilitate making and performance—typically driven by concerns of structural optimization, material use, and responsivity to environmental factors—of an atmospheric rather than social nature. At issue is the positioning of this particular manner of agency solely with the architect auteur. This abruptly halts—at the moment in which fabrication commences—the ability to amend, redefine, or newly introduce fundamentally transformational constituents and their interrelationships and, most importantly, to explore the possibility for extraordinary outcomes. When the architecture becomes a functional, social, and cultural entity, in the hands of the idealized abled-bodied user, agency—especially for one of an otherly body or mind—is long gone. Even an empathetic auteur may not be able to access the motivations of the differently-abled body and neuro- divergent mind, effectively locking the constraints of the design process, which creates an exclusionary system to those beyond the purview of said auteur. It can therefore be deduced that the mechanisms or authors of a conventional computational design process cannot eradicate the exclusionary reality of an architectural system. Agency is critical, yet a more expansive terminology for agent and agency is needed. The burden to conceive of capacities that will always be highly temporal, social, unpredictable, and purposefully unknown must be shifted far from the scope of the traditional directors of the architectural system. Agency, and who it is conferred upon, must function in a manner that dissolves the distinctions between the design, the action of designing, the author of design, and those subjected to it.
keywords	Adaptive environments, neurodiversity, inclusion, systems thinking, computational design, disability theory, material systems, design agency
series	journal
email
last changed	2020/11/02 13:34

_id	ecaade2020_390
id	ecaade2020_390
authors	Ahmadzadeh Bazzaz, Siamak, Fioravanti, Antonio and Coraglia, Ugo Maria
year	2020
title	Depth and Distance Perceptions within Virtual Reality Environments - A Comparison between HMDs and CAVEs in Architectural Design
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 375-382
doi	https://doi.org/10.52842/conf.ecaade.2020.1.375
summary	The Perceptions of Depth and Distance are considered as two of the most important factors in Virtual Reality Environments, as these environments inevitability impact the perception of the virtual content compared with the one of real world. Many studies on depth and distance perceptions in a virtual environment exist. Most of them were conducted using Head-Mounted Displays (HMDs) and less with large screen displays such as those of Cave Automatic Virtual Environments (CAVEs). In this paper, we make a comparison between the different aspects of perception in the architectural environment between CAVE systems and HMD. This paper clarifies the Virtual Object as an entity in a VE and also the pros and cons of using CAVEs and HMDs are explained. Eventually, just a first survey of the planned case study of the artificial port of the Trajan emperor near Fiumicino has been done as for COVID-19 an on-field experimentation could not have been performed.
keywords	Visual Perception; Depth and Distance Perception; Virtual Reality; HMD; CAVE; Trajan’s port
series	eCAADe
email
last changed	2022/06/07 07:54

_id	ecaade2020_193
id	ecaade2020_193
authors	Alymani, Abdulrahman, Jabi, Wassim and Corcoran, Padraig
year	2020
title	Machine Learning Methods for Clustering Architectural Precedents - Classifying the relationship between building and ground
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 643-652
doi	https://doi.org/10.52842/conf.ecaade.2020.1.643
summary	Every time an object is built, it creates a relationship with the ground. Architects have a full responsibility to design the building by taking the ground into consideration. In the field of architecture, using data mining to identify any unusual patterns or emergent architectural trends is a nascent area that has yet to be fully explored. Clustering techniques are an essential tool in this process for organising large datasets. In this paper, we propose a novel proof-of-concept workflow that enables a machine learning computer system to cluster aspects of an architect's building design style with respect to how the buildings in question relate to the ground. The experimental workflow in this paper consists of two stages. In the first stage, we use a database system to collect, organise and store several significant architectural precedents. The second stage examines the most well-known unsupervised learning algorithm clustering techniques which are: K-Means, K-Modes and Gaussian Mixture Models. Our experiments demonstrated that the K-means clustering algorithm method achieves a level of accuracy that is higher than other clustering methods. This research points to the potential of AI in helping designers identify the typological and topological characteristics of architectural solutions and place them within the most relevant architectural canons
keywords	Machine Learning; Building and Ground Relationship; Clustering Algorithms; K-means cluster Algorithms
series	eCAADe
email
last changed	2022/06/07 07:54

_id	cdrf2019_199
id	cdrf2019_199
authors	Ana Herruzo and Nikita Pashenkov
year	2020
title	Collection to Creation: Playfully Interpreting the Classics with Contemporary Tools
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_19
summary	This paper details an experimental project developed in an academic and pedagogical environment, aiming to bring together visual arts and computer science coursework in the creation of an interactive installation for a live event at The J. Paul Getty Museum. The result incorporates interactive visuals based on the user’s movements and facial expressions, accompanied by synthetic texts generated using machine learning algorithms trained on the museum’s art collection. Special focus is paid to how advances in computing such as Deep Learning and Natural Language Processing can contribute to deeper engagement with users and add new layers of interactivity.
series	cdrf
email
last changed	2022/09/29 07:51

_id	cdrf2019_3
id	cdrf2019_3
authors	Andrej Radman
year	2020
title	Machinic Phylum and Architecture
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_1
summary	The chapter draws on the anti-substantivist and anti-hylomorphic legacy of two significant Deleuze and Guattari’s interlocutors: Raymond Ruyer and Gilbert Simondon. Ruyer vehemently opposed the logic of mechanicism without regressing to (active) vitalism. His masterpiece Neofinalism, yet to be fully appreciated in architectural circles, is an ode to multiplicity or ‘absolute form’. The title is to be read as a challenge to the hegemony of the step-by-step causation and partes-extra-partes mereology. According to Ruyer, non-locality is the key,not only to the question of subjectivity, but to the problem of life itself. Simondon too shies away from the metaphysics of presence. For him, the process of individuation cannot be grasped on the basis of the fully formed individual. In other words, the knowledge of individuation is the individuation of knowledge. Simondon’s highest ambition in On the Mode of Existence of Technical Objects was to integrate culture and technics (tekhne). The conviction that culture need not be antagonistic to technology is particularly pertinent to the ecologies of architecture. In the second half of the chapter, the affordance theory meets contemporary neurosciences.
series	cdrf
email
last changed	2022/09/29 07:51

_id	acadia20_236p
id	acadia20_236p
authors	Anton, Ana; Jipa, Andrei; Reiter, Lex; Dillenburger, Benjamin
year	2020
title	Fast Complexity
source	ACADIA 2020: Distributed Proximities / Volume II: Projects [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95253-6]. Online and Global. 24-30 October 2020. edited by M. Yablonina, A. Marcus, S. Doyle, M. del Campo, V. Ago, B. Slocum. 236-241
summary	The concrete industry is responsible for 8% of the global CO2 emissions. Therefore, using concrete in more complex and optimized shapes can have a significant benefit to the environment. Digital fabrication with concrete aims to overcome the geometric limitations of standardized formworks and thereby reduce the ecological footprint of the building industry. One of the most significant material economy potentials is in structural slabs because they represent 85% of the weight of multi-story concrete structures. To address this opportunity, Fast Complexity proposes an automated fabrication process for highly optimized slabs with ornamented soffits. The method combines reusable 3D-printed formwork (3DPF) and 3D concrete printing (3DCP). 3DPF uses binder-jetting, a process with submillimetre resolution. A polyester coating is applied to ensure reusability and smooth concrete surfaces otherwise not achievable with 3DCP alone. 3DPF is selectively used only where high-quality finishing is necessary, while all other surfaces are fabricated formwork-free with 3DCP. The 3DCP process was developed interdisciplinary at ETH Zürich and employs a two-component material system consisting of Portland cement mortar and calcium aluminate cement accelerator paste. This fabrication process provides a seamless transition from digital casting to 3DCP in a continuous automated process. Fast Complexity selectively uses two complementary additive manufacturing methods, optimizing the fabrication speed. In this regard, the prototype exhibits two different surface qualities, reflecting the specific resolutions of the two digital processes. 3DCP inherits the fine resolution of the 3DPF strictly for the smooth, visible surfaces of the soffit, for which aesthetics are essential. In contrast, the hidden parts of the slab use the coarse resolution specific to the 3DCP process, not requiring any formwork and implicitly achieving faster fabrication. In the context of an increased interest in construction additive manufacturing, Fast Complexity explicitly addresses the low resolution, lack of geometric freedom, and limited reinforcement options typical to layered extrusion 3DCP, as well as the limited customizability in concrete technology.
series	ACADIA
type	project
email
last changed	2021/10/26 08:08

_id	ecaade2020_499
id	ecaade2020_499
authors	Ashour, Ziad and Yan, Wei
year	2020
title	BIM-Powered Augmented Reality for Advancing Human-Building Interaction
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 169-178
doi	https://doi.org/10.52842/conf.ecaade.2020.1.169
summary	The shift from computer-aided design (CAD) to building information modeling (BIM) has made the adoption of augmented reality (AR) promising in the field of architecture, engineering and construction. Despite the potential of AR in this field, the industry and professionals have still not fully adopted it due to registration and tracking limitations and visual occlusions in dynamic environments. We propose our first prototype (BIMxAR), which utilizes existing buildings' semantically rich BIM models and contextually aligns geometrical and non-geometrical information with the physical buildings. The proposed prototype aims to solve registration and tracking issues in dynamic environments by utilizing tracking and motion sensors already available in many mobile phones and tablets. The experiment results indicate that the system can support BIM and physical building registration in outdoor and part of indoor environments, but cannot maintain accurate alignment indoor when relying only on a device's motion sensors. Therefore, additional computer vision and AI (deep learning) functions need to be integrated into the system to enhance AR model registration in the future.
keywords	Augmented Reality; BIM; BIM-enabled AR; GPS; Human-Building Interactions; Education
series	eCAADe
email
last changed	2022/06/07 07:54

_id	caadria2020_043
id	caadria2020_043
authors	Bai, Nan, Nourian, Pirouz, Xie, Anping and Pereira Roders, Ana
year	2020
title	Towards a Finer Heritage Management - Evaluating the Tourism Carrying Capacity using an Agent-Based Model
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 305-314
doi	https://doi.org/10.52842/conf.caadria.2020.1.305
summary	As one of the most important areas in the Palace Museum, Beijing, China, the Hall of Mental Cultivation had suffered from overcrowding of visitors before it was closed in 2016 for conservation. Preparing for the reopening in 2020, the Palace Museum decided to take the chance and initiate finer-grained tourism management in the Hall. This research intends to provide an audio-guided touring program by dynamically evaluating the Tourism Carrying Capacity (TCC) with the highlight spots in the Hall, to operate the touring program spatiotemporally. Framing an optimization problem for the touring program, an agent-based simulator, Thunderhead Pathfinder, originally developed for evacuation in the emergency, is utilized to verify the performance of the touring system. The simulation shows that the proposed touring program could precisely fit all the key requirements to improve the visitors' experience, to guarantee heritage safety, and to ensure more efficient management.
keywords	Tourism Carrying Capacity; Agent-Based Simulation; Operations Research; Heritage Management
series	CAADRIA
email
last changed	2022/06/07 07:54

_id	ecaade2022_16
id	ecaade2022_16
authors	Bailey, Grayson, Kammler, Olaf, Weiser, Rene, Fuchkina, Ekaterina and Schneider, Sven
year	2022
title	Performing Immersive Virtual Environment User Studies with VREVAL
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 437–446
doi	https://doi.org/10.52842/conf.ecaade.2022.2.437
summary	The new construction that is projected to take place between 2020 and 2040 plays a critical role in embodied carbon emissions. The change in material selection is inversely proportional to the budget as the project progresses. Given the fact that early-stage design processes often do not include environmental performance metrics, there is an opportunity to investigate a toolset that enables early-stage design processes to integrate this type of analysis into the preferred workflow of concept designers. The value here is that early-stage environmental feedback can inform the crucial decisions that are made in the beginning, giving a greater chance for a building with better environmental performance in terms of its life cycle. This paper presents the development of a tool called LearnCarbon, as a plugin of Rhino3d, used to educate architects and engineers in the early stages about the environmental impact of their design. It facilitates two neural networks trained with the Embodied Carbon Benchmark Study by Carbon Leadership Forum, which learns the relationship between building geometry, typology, and construction type with the Global Warming potential (GWP) in tons of C02 equivalent (tCO2e). The first one, a regression model, can predict the GWP based on the massing model of a building, along with information about typology and location. The second one, a classification model, predicts the construction type given a massing model and target GWP. LearnCarbon can help improve the building life cycle impact significantly through early predictions of the structure’s material and can be used as a tool for facilitating sustainable discussions between the architect and the client.
keywords	Pre-Occupancy Evaluation, Immersive Virtual Environment, Wayfinding, User Centered Design, Architectural Study Design
series	eCAADe
email
last changed	2024/04/22 07:10

_id	acadia20_208p
id	acadia20_208p
authors	Bernier-Lavigne, Samuel
year	2020
title	Object-Field
source	ACADIA 2020: Distributed Proximities / Volume II: Projects [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95253-6]. Online and Global. 24-30 October 2020. edited by M. Yablonina, A. Marcus, S. Doyle, M. del Campo, V. Ago, B. Slocum. 208-213
summary	This project aims to continue the correlative study between two fundamental entities of digital architecture: the object and the field. Following periods of experimentations on the ""field"" (materialization of flows of data through animation), the ""field of objects"" (parametricism), the ""object"" (OOO), we investigate the last possible interaction remaining: the ""object-field,"" by merging the formal characteristics of the object with the structural flow of its internal field. This investigation is achieved by exploring the high-resolution features of 3d printing in the design of autonomous architectural objects expressing materiality through topological optimization. The objects are generated by an iterative process of volumetric reduction, resulting in an ensemble of monoliths. Four of them are selected and analyzed through topological optimization in order to extract their internal fields. Next, a series of high-resolution algorithmic systems translate the structural information into 3d printed materiality. Of the four object-fields, one materializes, close to identical, the result of the optimization, giving the keystone to understanding the others. The second one expresses the structural flow through a 1mm voxel system, informed by the optimization, having the effect of stiffening the structure where it is needed and thus generating a new topography on the object. The last two explore the blur that this high-resolution can paradoxically create, with complete integration of the optimal structure in a transparent monolith. This is achieved by a vertex displacement algorithm, and the dissolution of the formal data of the monolith and the structural flows, through the mereological assembly of simple linear elements. For each object-field, a series of drawings was developed using specific algorithmic procedures derived from the peculiarities of their complex geometry. The drawings aim to catalyze coherence throughout the project, where similarities, hitherto kept apart by the multiple materialities, begin to dialogue.
series	ACADIA
type	project
email
last changed	2021/10/26 08:08

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