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_031
id	caadria2020_031
authors	Kim, Nayeon and Lee, Hyunsoo
year	2020
title	Visual Attention in Retail Environments - Design Analysis using HMD based VR System Integrated Eye-Tracking
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. 631-640
doi	https://doi.org/10.52842/conf.caadria.2020.1.631
summary	The goal of this study is to understand the spatial experience of users in retail environments in an immersive virtual reality setting. This study measures the visual attention and visual merchandising cognition of users via a quantitative method. The study was conducted to assess users' visual perception arising from the visual merchandising in-store environment during virtual reality experiences. The experiment was conducted using eye-tracking methodology in a virtual reality environment. After the experiment, participants responded to questionnaire surveys to assess visual merchandising cognition in retail environments. The experiment stimuli were provided in the virtual simulation of a retail store. During the experiment, each participant wearing a head-mounted display device was asked to experience the virtual retail space. The result shows the quantitative analysis of user behavior in the retail space and which design elements attract their attention. Unlike the precedent eye-tracking studies, this research analyzes visual attention during the spatial experience of retailing in its use of virtual reality technology. The approach and findings of this research provide useful information and practical guidelines to retailers and designers who are interested in improving the retail environment in consideration of customer visual attention and spatial elements.
keywords	Visual Attention; Retail Environment; Eye-tracking ; Virtual Reality; HMD (Head-Mounted Display)
series	CAADRIA
email
last changed	2022/06/07 07:49

_id	ecaade2020_511
id	ecaade2020_511
authors	Maierhofer, Mathias, Ulber, Marie, Mahall, Mona, Serbest, Asli and Menges, Achim
year	2020
title	Designing (for) Change - Towards adaptivity-specific architectural design for situational open Environments
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. 575-584
doi	https://doi.org/10.52842/conf.ecaade.2020.2.575
summary	The introduction of cybernetic principles to the architectural discourse some 50 years ago stimulated a new notion of buildings as dynamic and under-specified systems. Although their traditional conception as static and deterministic objects has remained predominant to this day, concepts for adaptive architecture capable of interacting with their surroundings and occupants have gained renewed attention in recent decades. However, investigations so far have largely concentrated on small-scale applications or individual adaptation strategies. The notion of situational open Environments, as argued in this paper, provides a framework through which adaptivity can be conceived and explored more holistically as well as on an inhabitable scale. Environments reject deterministic design and adaptation solutions and hence call for integrative and interactive design strategies that not only allow for the exploration of particularly adaptable (i.e. underspecified) architectural morphologies, but also for the communication and negotiation during their further development beyond deployment. In respect thereof, this paper discusses the potentials and implications of computational (design) strategies, meaning the agencies of buildings, designers, residents, and surroundings. The presented research originates from the author's involvement in an interdisciplinary research project centered around the development of an adaptive high-rise building that incorporates various adaptation strategies.
keywords	Adaptive Architecture; Architectural Environment; Computational Design; Agent-based Modeling; Architecture Theory; Cybernetics
series	eCAADe
email
last changed	2022/06/07 07:59

_id	sigradi2020_260
id	sigradi2020_260
authors	Bhattacharya, Maharshi; Jung, Francisco
year	2020
title	Multi-Mission Space Exploration Vehicle (MMSEV) Nosecone Design Optimization
source	SIGraDi 2020 [Proceedings of the 24th Conference of the Iberoamerican Society of Digital Graphics - ISSN: 2318-6968] Online Conference 18 - 20 November 2020, pp. 260-266
summary	This paper addresses ergonomic drawbacks in NASA’s modular Multi-Mission Space Exploration Vehicle’s (MMSEV) latest prototype, 2B’s nosecone, to propose new iteration based on considerations such as mass minimization, visibility maximization, and structural integrity. With 2B as a benchmark, and using computational tools typically used in the AEC industry to carry out FEA analysis, comparisons are made with potential design changes. The numerical and visual data such as weight, and stress distribution, provided by the benchmark analysis, served as metrics for comparison and redesign. In turn, this design development exercise attempts to bring together the different design approaches to design, held by human- factors designers and structural engineers.
keywords	Form, Optimization, Finite Element Analysis, Space-Exploration Vehicle, Stress-Analysis
series	SIGraDi
email
last changed	2021/07/16 11:49

_id	ecaade2020_348
id	ecaade2020_348
authors	Chiujdea, Ruxandra Stefania and Nicholas, Paul
year	2020
title	Design and 3D Printing Methodologies for Cellulose-based Composite Materials
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. 547-554
doi	https://doi.org/10.52842/conf.ecaade.2020.1.547
summary	A growing awareness of architecture's environmental responsibility is encouraging a shift from an industrial age to an ecological one. This shift emphasises a new era of materiality, characterised by a special focus on bio-polymers. The potential of these materials is to address unsustainable modes of resource consumption, and to rebalance our relationship with the natural. However, bio-polymers also challenge current design and manufacturing practices, which rely on highly manufactured and standardized materials. In this paper, we present material experiments and digital design and fabrication methodologies for cellulose-based composites, to create porous biodegradable panels. Cellulose, the most abundant bio-polymer on Earth, has potential for differentiated architectural applications. A key limit is the critical role of additive fabrication methods for larger scale elements, which are a subject of ongoing research. In this paper, we describe how controlling the interdependent relationship between the additive manufacturing process and the material grading enables the manipulation of the material's performance, and the related control aspects including printing parameters such as speed, nozzle diameter, air flow, etc., as well as tool path trajectory. Our design exploration responds to the emerging fabrication methods to achieve different levels of porosity and depth which define the geometry of a panel.
keywords	cellulose-based composite material; additive manufacturing; material grading; digital fabrication; spatial print trajectory; porous panels
series	eCAADe
email
last changed	2022/06/07 07:56

_id	ecaade2020_432
id	ecaade2020_432
authors	Fragkia, Vasiliki and Worre Foged, Isak
year	2020
title	Methods for the Prediction and Specification of Functionally Graded Multi-Grain Responsive Timber Composites
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. 585-594
doi	https://doi.org/10.52842/conf.ecaade.2020.2.585
summary	The paper presents design-integrated methods for high-resolution specification and prediction of functionally graded wood-based thermal responsive composites, using machine learning. The objective is the development of new circular design workflow, employing robotic fabrication, in order to predict fabrication files linked to material performance and design requirements, focused on application for intrinsic responsive and adaptive architectural surfaces. Through an experimental case study, the paper explores how machine learning can form a predictive design framework where low-resolution data can solve material systems at high resolution. The experimental computational and prototyping studies show that the presented image-based machine learning method can be adopted and adapted across various stages and scales of architectural design and fabrication. This in turn allows for a design-per-requirement approach that optimizes material distribution and promotes material economy.
keywords	material specification; responsive timber composites; machine learning; robotic fabrication; building envelopes
series	eCAADe
email
last changed	2022/06/07 07:50

_id	artificial_intellicence2019_207
id	artificial_intellicence2019_207
authors	Hao Zheng
year	2020
title	Form Finding and Evaluating Through Machine Learning: The Prediction of Personal Design Preference in Polyhedral Structures
source	Architectural Intelligence Selected Papers from the 1st International Conference on Computational Design and Robotic Fabrication (CDRF 2025)
doi	https://doi.org/https://doi.org/10.1007/978-981-15-6568-7_13
summary	3D Graphic Statics (3DGS) is a geometry-based structural design and analysis method, helping designers to generate 3D polyhedral forms by manipulating force diagrams with given boundary conditions. By subdividing 3D force diagrams with different rules, a variety of forms can be generated, resulting in more members with shorter lengths and richer overall complexity in forms. However, it is hard to evaluate the preference toward different forms from the aspect of aesthetics, especially for a specific architect with his own scene of beauty and taste of forms. Therefore, this article proposes a method to quantify the design preference of forms using machine learning and find the form with the highest score based on the result of the preference test from the architect. A dataset of forms was firstly generated, then the architect was asked to keep picking a favorite form from a set of forms several times in order to record the preference. After being trained with the test result, the neural network can evaluate a new inputted form with a score from 0 to 1, indicating the predicted preference of the architect, showing the possibility of using machine learning to quantitatively evaluate personal design taste.
series	Architectural Intelligence
email
last changed	2022/09/29 07:28

_id	ecaade2022_161
id	ecaade2022_161
authors	Kharbanda, Kritika, Papadopoulou, Iliana, Pouliou, Panagiota, Daw, Karim, Belwadi, Anirudh and Loganathan, Hariprasath
year	2022
title	LearnCarbon - A tool for machine learning prediction of global warming potential from abstract designs
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. 601–610
doi	https://doi.org/10.52842/conf.ecaade.2022.2.601
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 learn the relationship between building geometry, typology, and structure with the Global Warming potential in tCO2e. The first one, a regression model, is able to 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	Machine Learning, Carbon Emissions, LCA, Rhino Plug-in
series	eCAADe
email
last changed	2024/04/22 07:10

_id	acadia20_84
id	acadia20_84
authors	Kirova, Nikol; Markopoulou, Areti
year	2020
title	Pedestrian Flow: Monitoring and Prediction
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. 84-93.
doi	https://doi.org/10.52842/conf.acadia.2020.1.084
summary	The worldwide lockdowns during the first wave of the COVID-19 pandemic had an immense effect on the public space. The events brought up an opportunity to redesign mobility plans, streets, and sidewalks, making cities more resilient and adaptable. This paper builds on previous research of the authors that focused on the development of a graphene-based sensing material system applied to a smart pavement and utilized to obtain pedestrian spatiotemporal data. The necessary steps for gradual integration of the material system within the urban fabric are introduced as milestones toward predictive modeling and dynamic mobility reconfiguration. Based on the capacity of the smart pavement, the current research presents how data acquired through an agent-based pedestrian simulation is used to gain insight into mobility patterns. A range of maps representing pedestrian density, flow, and distancing are generated to visualize the simulated behavioral patterns. The methodology is used to identify areas with high density and, thus, high risk of transmitting airborne diseases. The insights gained are used to identify streets where additional space for pedestrians is needed to allow safe use of the public space. It is proposed that this is done by creating a dynamic mobility plan where temporal pedestrianization takes place at certain times of the day with minimal disruption of road traffic. Although this paper focuses mainly on the agent-based pedestrian simulation, the method can be used with real-time data acquired by the sensing material system for informed decision-making following otherwise-unpredictable pedestrian behavior. Finally, the simulated data is used within a predictive modeling framework to identify further steps for each agent; this is used as a proof-of-concept through which more insights can be gained with additional exploration.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2020_259
id	caadria2020_259
authors	Rhee, Jinmo, Veloso, Pedro and Krishnamurti, Ramesh
year	2020
title	Integrating building footprint prediction and building massing - an experiment in Pittsburgh
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. 669-678
doi	https://doi.org/10.52842/conf.caadria.2020.2.669
summary	We present a novel method for generating building geometry using deep learning techniques based on contextual geometry in urban context and explore its potential to support building massing. For contextual geometry, we opted to investigate the building footprint, a main interface between urban and architectural forms. For training, we collected GIS data of building footprints and geometries of parcels from Pittsburgh and created a large dataset of Diagrammatic Image Dataset (DID). We employed a modified version of a VGG neural network to model the relationship between (c) a diagrammatic image of a building parcel and context without the footprint, and (q) a quadrilateral representing the original footprint. The option for simple geometrical output enables direct integration with custom design workflows because it obviates image processing and increases training speed. After training the neural network with a curated dataset, we explore a generative workflow for building massing that integrates contextual and programmatic data. As trained model can suggest a contextual boundary for a new site, we used Massigner (Rhee and Chung 2019) to recommend massing alternatives based on the subtraction of voids inside the contextual boundary that satisfy design constraints and programmatic requirements. This new method suggests the potential that learning-based method can be an alternative of rule-based design methods to grasp the complex relationships between design elements.
keywords	Deep Learning; Prediction; Building Footprint; Massing; Generative Design
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	cdrf2022_209
id	cdrf2022_209
authors	Yecheng Zhang, Qimin Zhang, Yuxuan Zhao, Yunjie Deng, Feiyang Liu, Hao Zheng
year	2022
title	Artificial Intelligence Prediction of Urban Spatial Risk Factors from an Epidemic Perspective
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_18
summary	From the epidemiological perspective, previous research methods of COVID-19 are generally based on classical statistical analysis. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. We take the Spatio-temporal data of people infected with new coronary pneumonia before February 28 in Wuhan in 2020 as the research object. We use kriging spatial interpolation technology and core density estimation technology to establish the epidemic heat distribution on fine grid units. We further examine the distribution of nine main spatial risk factors, including agencies, hospitals, park squares, sports fields, banks, hotels, Etc., which are tested for the significant positive correlation with the heat distribution of the epidemic. The weights of the spatial risk factors are used for training Generative Adversarial Network models, which predict the heat distribution of the outbreak in a given area. According to the trained model, optimizing the relevant environment design in urban areas to control risk factors effectively prevents and manages the epidemic from dispersing. The input image of the machine learning model is a city plan converted by public infrastructures, and the output image is a map of urban spatial risk factors in the given area.
series	cdrf
email
last changed	2024/05/29 14:02

_id	caadria2020_045
id	caadria2020_045
authors	Zheng, Hao and Ren, Yue
year	2020
title	Machine Learning Neural Networks Construction and Analysis in Vectorized Design Drawings
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. 707-716
doi	https://doi.org/10.52842/conf.caadria.2020.2.707
summary	Machine Learning, a recently prevalent research domain in data prediction and analysis, has been widely used in a variety of fields. In the design field, especially for architectural design, a machine learning method to learn and generate design data as pixelized images has been developed in previous researches. However, proceeding pixelized image data will cause the problems of precision loss and calculation waste, since the geometric architectural design data is efficiently stored and presented as vectorized CAD files. Thus, in this article, the author developed a specific machine learning neural network to learn and predict design drawings as vectorized data, speeding up the learning and predicting process, while improving the accuracy. First, two necessary geometric tests have been successfully done, which shows the central concept of neural network construct. Then, a design rule prediction model was built to demonstrate the methods to optimize the neural network and data structure. Lastly, a generation model based on human-made design data was constructed, which can be used to predict and generate the bedroom furniture positions by inputting the boundary data of the room, door, and window.
keywords	Machine Learning; Artificial Intelligence; Generative Design; Geometric Design
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	caadria2020_115
id	caadria2020_115
authors	Zhong, Jia Ding, Chao, Sara, Ming Chun and Tsou, Jin Yeu
year	2020
title	Establishing a Prediction Model for Better Decision Making Regarding Urban Green Planning in a High-density Urban Context
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. 517-526
doi	https://doi.org/10.52842/conf.caadria.2020.1.517
summary	This paper presents a prototype of a prediction model. The model helps to improve decision making regarding urban green patch planning. This process is achieved by the model predicting the response of thermal comfort conditions in an urban green patch to different planning decisions. This process is demonstrated via an investigation of variations in urban density. The model features a surface temperature mapping approach, which assigns surface temperature data acquired through field-measurement to solid surfaces in CFD simulations based on the shading state. Besides, trees are simulated in a systematic way, and the model combines CFD simulations with PET values, the processes of which are also demonstrated in this paper.
keywords	Urban Green Planning; Decision Making; Thermal Comfort; CFD
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	ecaade2020_267
id	ecaade2020_267
authors	Argin, Gorsev, Pak, Burak and Turkoglu, Handan
year	2020
title	Through the Eyes of (Post-)Flâneurs - Altering rhythm and visual attention in public space in the era of smartphones
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. 239-248
doi	https://doi.org/10.52842/conf.ecaade.2020.1.239
summary	In the last decade, rapid penetration of smartphones into our everyday life introduced a new kind of urban wanderer named as the 'post-flâneur'. By navigating through the virtual and physical space with a smartphone, and taking and sharing photographs, post-flâneur walks and experiences the city in novel ways. This paper aims to investigate the effects of smartphone use on the human-environment relationship by comparing post-flânerie with flânerie in public space with a focus on two key indicators: alteration of 1) the visual attention and 2) the walking rhythm. In this regard, ten postgraduate Architecture students are asked to perform flânerie and post-flânerie consecutively in the historical city center of Ghent with an eye-tracker and a smartphone. During the flânerie condition, they walked and experienced the city without using a smartphone. In the post-flânerie condition, they used a smartphone, took pictures and uploaded them to an application. By analyzing the eye-tracker (number and duration of fixations) and the smartphone (location data and geolocated photographs) data, altering rhythm and visual attention during the flânerie and post-flânerie were compared. Preliminary results indicate that flânerie and post-flânerie differ in terms of rhythm and visual attention. The average duration of fixations on the environment were significantly lower in the post-flânerie condition while the average walking rhythm was faster but impeded from time to time. In addition, post-flâneurs' visual attention was on the smartphone during a significant part of the stationary activities which point out to an altered state of public space appropriation. The findings are significant because they reveal the novel spatial appropriations and experiences of the (post)public space -particularly "the honeypot effect" which was more significant in the post-flânerie condition. These observations evoke questions on how designers can rethink public space as a hybrid construct integrating the virtual and the physical.
keywords	post-flâneur; rhythm; visual attention; smartphone; eye-tracking
series	eCAADe
email
last changed	2022/06/07 07:54

_id	ecaade2020_515
id	ecaade2020_515
authors	Chadha, Kunaljit, Dubor, Alexandre, Puigpinos, Laura and Rafols, Irene
year	2020
title	Space Filling Curves for Optimising Single Point Incremental Sheet Forming using Supervised Learning Algorithms
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. 555-562
doi	https://doi.org/10.52842/conf.ecaade.2020.1.555
summary	Increasing use of computational design tools have led to an increase in the demand for mass customised fabrication, rendering decades old industrial CAD-CAM protocols limiting for such fabrication processes. This bespoke demand of components has led to a unified workflow between design strategies and production techniques. Recent advances in computation have allowed us to predict and register the tolerances of fabrication before and while being fabricated. Procedural algorithms are a set of novel problem-solving methods and have been attracting considerable attention for their good performance.They follow a procedural way of iteration with an established way of behavior.In the particular case of Incremental Sheet forming (ISF), these algorithms can realize several functions such as edge detection and segmentation required for optimizing machining time and accuracy.In this context, this paper presents a methodology to optimize long-drawn-out ISF operation by using geometrical intervention informed by supervised machine learning algorithms.
keywords	Procedural Algorithms; Incremental Sheet Forming; Robotic Cold forming; Mass Customization
series	eCAADe
email
last changed	2022/06/07 07:55

_id	caadria2020_231
id	caadria2020_231
authors	Doe, Robert
year	2020
title	sensMOD - Computational Design through the lens of Henri Lefebvre's Spatial Theory
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. 701-710
doi	https://doi.org/10.52842/conf.caadria.2020.1.701
summary	Spatial productivity is the first of the elements comprising sensMOD, a student elective that implemented a methodology addressing the exigent need of our time for transformation in the architecture, engineering and construction (AEC) sector. The second and third elements of sensMOD are parts and interaction which focus attention on the nature of complexity and connectivity in our networked world. The paper proposes a methodology that was used to guide the teaching of an elective for third year architecture students at a UK university. Its wider purpose is to contribute to discussion concerning the dysfunctional state of an AEC sector that needs to consider its productivity as projections of wider networks of resource and energy relationships. Henri Lefebvre's spatial theory (1991) guides the narrative and formulation of sensMOD.
keywords	computational design; spatial productivity; modularity; interaction design
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	acadia20_136p
id	acadia20_136p
authors	López Lobato, Déborah; Charbel, Hadin
year	2020
title	Foll(i)cle
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. 136-141
summary	In the early months of 2019, air pollution in Bangkok reached a record high, bringing national and international attention to the air quality in the South East Asian cosmopolitan. Although applications such as real-time pollution maps provide an environmental reading from the exterior, such information reveals the ‘here and now,’ where its record is inevitably lost through the ‘refreshing’ process of the live update and does not take increment and accumulation as factors to consider. The project was conceived around understanding the human body as precisely that medium that resists classification as either an interior or exterior environment that inherently performs as an impressionable record of its surroundings. Can a city’s toxicity be read through its living constituents? Can the living bodies that dwell, navigate, breathe, and process habitable environments be accessed? Can architecture retain a degree of independence while also performing as a beacon for the collective? Along this line of questioning, it was found that human hair can be transformed from a material that is effortlessly and continuously grown, cut, stylized, and discarded, and instead be intercepted and used in the production of public information gathering. Foll(i)cle is a collective being made of discarded human hair. Performing as a parliament for collectivity embedded with a protocol; the hairy pavilion invites the public in and presents them with a device at the center that hosts all the necessary equipment and information for anonymously and voluntarily providing hair samples for heavy metal analysis, the data of which is used in making a publically accessible toxi-cartography. Although humans are the primary subject for this study, the results suggest that extending the methodology to non-humans could prove useful in reading urban toxicity through various life forms.
series	ACADIA
type	project
email
last changed	2021/10/26 08:03

_id	acadia23_v3_111
id	acadia23_v3_111
authors	Markopoulou, Areti
year	2023
title	Urban Mining: Material Resources for Circular Construction
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 3: Proceedings of the 43rd Annual Conference for the Association for Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9891764-1-0]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 24-32.
summary	The material balance of the Earth is being challenged. The year 2020 was marked as the year when the total weight of human-made materials globally surpassed the weight of all life on Earth, while it is estimated that in the years to come the growth rate of mass added to the anthroposphere will increase exponentially (Elhacham et al., 2020). In this context of hypergrowth coupled with the climate emergency, the growing rate of urbanization and the increasing social and political awareness on the matters of the Anthropocene, the topics of resource depletion or insufficiency are being reframed. This keynote lecture at ACADIA 2023 highlights the importance of redefining resources and is introducing a new cultural, design and construction paradigm. Operating from an abundance mindset rather than from scarcity (Gausa et al., 2020) presents a new paradigm, particularly relevant in the design and production of the built environment. This approach expands the definition of resources, encompassing raw, non-raw, renewable, and recyclable materials. Shifting attention to the Anthroposphere as a source rather than just a destination for processed goods has the potential to disrupt linear design patterns and enhance circularity in cities and the built environment.
series	ACADIA
type	keynote
email
last changed	2024/04/17 13:59

_id	caadria2020_254
id	caadria2020_254
authors	Pei, Wanyu, LO, TianTian and Guo, Xiangmin
year	2020
title	A Biofeedback Process: Detecting Architectural Space with the Integration of Emotion Recognition and Eye-tracking Technology
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. 263-272
doi	https://doi.org/10.52842/conf.caadria.2020.2.263
summary	This paper coincides with the conference theme that people have gradually become a vital force influencing the environmental system. In the future, it is necessary to study the influence of not only the built environment on people but also people's feedback on environmental design. This study explores the â€Žprocesses of interactive design using both emotion recognition and eye-tracking of users. By putting on wearable devices to roam and perceive in a virtual reality space, the physiological data of the users are collected in real-time and used to analyze their emotional responses and visual attention to the spaces. This method will provide an auxiliary way for non-architectural professional users to participate in architectural space design. At present, there is a lack of research on the comprehensive application of eye movement knowledge and emotional feedback in architectural space design. This integration will help professional designers to optimize the design of architectural space. For this paper, we review existing research and proposing an interactive design workflow that integrates eye tracking and emotion recognition. This workflow will help with the next stage of research to understand the design of a new International School of Design building.
keywords	Perception detection; Architectural space environment; Interactive design; Virtual reality
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	caadria2020_443
id	caadria2020_443
authors	Abuzuraiq, Ahmed M. and Erhan, Halil
year	2020
title	The Many Faces of Similarity - A Visual Analytics Approach for Design Space Simplification
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. 485-494
doi	https://doi.org/10.52842/conf.caadria.2020.1.485
summary	Generative design methods may involve a complex design space with an overwhelming number of alternatives with their form and design performance data. Existing research addresses this complexity by introducing various techniques for simplification through clustering and dimensionality reduction. In this study, we further analyze the relevant literature on design space simplification and exploration to identify their potentials and gaps. We find that the potentials include: alleviating the choice overload problem, opening up new venues for interrelating design forms and data, creating visual overviews of the design space and introducing ways of creating form-driven queries. Building on that, we present the first prototype of a design analytics dashboard that combines coordinated and interactive visualizations of design forms and performance data along with the result of simplifying the design space through hierarchical clustering.
keywords	Visual Analytics; Design Exploration; Dimensionality Reduction; Clustering; Similarity-based Exploration
series	CAADRIA
email
last changed	2022/06/07 07:54

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