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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Reformat results as: short short into frame detailed detailed into frame

_id	ecaade2020_137
id	ecaade2020_137
authors	Webb, Nicholas, Hillson, James, Peterson, John Robert, Buchanan, Alexandrina and Duffy, Sarah
year	2020
title	Documentation and Analysis of a Medieval Tracing Floor Using Photogrammetry, Reflectance Transformation Imaging and Laser Scanning
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. 209-218
doi	https://doi.org/10.52842/conf.ecaade.2020.2.209
summary	The fifteenth-century tracing floor at Wells cathedral is an extremely rare survival in European architecture. Located in the roof space above the north porch, this plaster floor was used as a drawing and design tool by medieval masons, the lines and arcs inscribed into its surface enabling them to explore their ideas on a 1:1 scale. Many of these marks are difficult to see with the naked eye and existing studies of its geometry are reliant on manual retracing of its lines. This paper showcases the potential of digital surveying and analytical tools, namely photogrammetry, reflectance transformation imaging (RTI) and laser scanning, to extend our knowledge of the tracing floor and its use in the cathedral. It begins by comparing the recording processes and outputs of all three techniques, followed by a description of the digital retracing of the tracing floor to highlight lines and arcs on the surface. Finally, it compares these with digital surveys of the architecture of the cathedral cloister.
keywords	digital heritage; photogrammetry; reflectance transformation imaging; laser scanning; medieval design
series	eCAADe
email
last changed	2022/06/07 07:58

_id	cdrf2019_93
id	cdrf2019_93
authors	Jiaxin Zhang , Tomohiro Fukuda , and Nobuyoshi Yabuki
year	2020
title	A Large-Scale Measurement and Quantitative Analysis Method of Façade Color in the Urban Street Using Deep Learning
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_9
summary	Color planning has become a significant issue in urban development, and an overall cognition of the urban color identities will help to design a better urban environment. However, the previous measurement and analysis methods for the facade color in the urban street are limited to manual collection, which is challenging to carry out on a city scale. Recent emerging dataset street view image and deep learning have revealed the possibility to overcome the previous limits, thus bringing forward a research paradigm shift. In the experimental part, we disassemble the goal into three steps: firstly, capturing the street view images with coordinate information through the API provided by the street view service; then extracting facade images and cleaning up invalid data by using the deep-learning segmentation method; finally, calculating the dominant color based on the data on the Munsell Color System. Results can show whether the color status satisfies the requirements of its urban plan for façade color in the street. This method can help to realize the refined measurement of façade color using open source data, and has good universality in practice.
series	cdrf
email
last changed	2022/09/29 07:51

_id	ecaade2020_290
id	ecaade2020_290
authors	Elesawy, Amr Alaaeldin, Signer, Mario, Seshadri, Bharath and Schlueter, Arno
year	2020
title	Aerial Photogrammetry in Remote Locations - A workflow for using 3D point cloud data in building energy modeling
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. 723-732
doi	https://doi.org/10.52842/conf.ecaade.2020.1.723
summary	Building energy modelling (BEM) results are highly affected by the surrounding environment, due to the impact of solar radiation on the site. Hence, modelling the context is a crucial step in the design process. This is challenging when access to the geometrical data of the built and natural environment is unavailable as in remote villages. The acquisition of accurate data through conventional surveying proves to be costly and time consuming, especially in areas with a steep and complex terrain. Photogrammetry using drone-captured aerial images has emerged as an innovative solution to facilitate surveying and modeling. Nevertheless, the workflow of translating the photogrammetry output from data points to surfaces readable by BEM tools proves to be tedious and unclear. This paper presents a streamlined and reproducible approach for constructing accurate building models from photogrammetric data points to use for architectural design and energy analysis in early design stage projects.
keywords	Building Energy Modeling; Photogrammetry; 3D Point Clouds; Low-energy architecture; Multidisciplinary design; Education
series	eCAADe
email
last changed	2022/06/07 07:55

_id	acadia16_470
id	acadia16_470
authors	Sollazzo, Aldo; Baseta, Efilena; Chronis, Angelos
year	2016
title	Symbiotic Associations
source	ACADIA // 2016: POSTHUMAN FRONTIERS: Data, Designers, and Cognitive Machines [Proceedings of the 36th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-77095-5] Ann Arbor 27-29 October, 2016, pp. 470-477
doi	https://doi.org/10.52842/conf.acadia.2016.470
summary	Soil contamination poses a series of important health issues, following years of neglect, constant industrialization, and unsustainable agriculture. It is estimated that 30% of the total cultivated soil in the world will convert to degraded land by 2020 (Rashid et al. 2016). Finding suitable treatment technologies to clean up contaminated water and soil is not trivial, and although technological solutions are sought, many are both resource-expensive and potentially equally unsustainable in long term. Bacteria and fungi have proved efficient in contributing to the bioavailability of nutrients and in aggregating formation in degraded soils (Rashid et al. 2016). Our research aims to explore the possible implementation of physical computing, computational analysis, and digital fabrication techniques in the design and optimization of an efficient soil remediation strategy using mycelium. The study presented here is a first step towards an overarching methodology for the development of an automated soil decontamination process, using an optimized bio-cell fungus seed that can be remotely populated using aerial transportation. The presented study focuses on the development of a methodology for capturing and modeling the growth of the mycelium fungus using photogrammetry-based 3D scanning and computational analysis techniques.
keywords	computational design, photogrammetry, simulation, mycelium, 3d scanning, growth strategies
series	ACADIA
type	paper
email
last changed	2022/06/07 07:56

_id	caadria2020_106
id	caadria2020_106
authors	Tian, Jieren and Yu, Chuanfei
year	2020
title	Dynamic Translation of Real-world Environment Factors and Urban Design Operation in a Game Engine - A Case Study of Central District in Tiebei New Town, Nanjing
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. 11-20
doi	https://doi.org/10.52842/conf.caadria.2020.2.011
summary	The building and its urban environment are complex and dynamic data systems. Designers, who make design decisions, need the design tools to simulate the built environment, to estimate the feasibility of the design. However, the static modeling software, widely used nowadays, restricts the linkage relationship between the actual data environment and the simulation model, which lacks the dynamic constraint relationship and the construction of the loop order. Different from traditional modeling and analysis tools, simulation games, with dynamic constraint rules and real-time feedback operations, provide a new way of thinking and a perspective to observe the urban, which makes the simulation game be seen as a simplified analog system, to some extent. Therefore, this paper plan to builds a city model, based on an urban design project of an urban district of Nanjing as an example, by using the Cities: Skylines, a city simulation game with priority of traffic and zoning concept. Based on this dynamic model, the next step will evaluate the original project and carry out further optimization operations in real-time.
keywords	real-time interaction; dynamic process simulation; urban environment; city simulation system; simulated game
series	CAADRIA
email
last changed	2022/06/07 07:58

_id	caadria2020_446
id	caadria2020_446
authors	Cho, Dahngyu, Kim, Jinsung, Shin, Eunseo, Choi, Jungsik and Lee, Jin-Kook
year	2020
title	Recognizing Architectural Objects in Floor-plan Drawings Using Deep-learning Style-transfer Algorithms
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. 717-725
doi	https://doi.org/10.52842/conf.caadria.2020.2.717
summary	This paper describes an approach of recognizing floor plans by assorting essential objects of the plan using deep-learning based style transfer algorithms. Previously, the recognition of floor plans in the design and remodeling phase was labor-intensive, requiring expert-dependent and manual interpretation. For a computer to take in the imaged architectural plan information, the symbols in the plan must be understood. However, the computer has difficulty in extracting information directly from the preexisting plans due to the different conditions of the plans. The goal is to change the preexisting plans to an integrated format to improve the readability by transferring their style into a comprehensible way using Conditional Generative Adversarial Networks (cGAN). About 100-floor plans were used for the dataset which was previously constructed by the Ministry of Land, Infrastructure, and Transport of Korea. The proposed approach has such two steps: (1) to define the important objects contained in the floor plan which needs to be extracted and (2) to use the defined objects as training input data for the cGAN style transfer model. In this paper, wall, door, and window objects were selected as the target for extraction. The preexisting floor plans would be segmented into each part, altered into a consistent format which would then contribute to automatically extracting information for further utilization.
keywords	Architectural objects; floor plan recognition; deep-learning; style-transfer
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	caadria2020_426
id	caadria2020_426
authors	Goepel, Garvin and Crolla, Kristof
year	2020
title	Augmented Reality-based Collaboration - ARgan, a bamboo art installation case study
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. 313-322
doi	https://doi.org/10.52842/conf.caadria.2020.2.313
summary	ARgan is a geometrically complex bamboo sculpture that relied on Mixed Reality (MR) for its joint creation by multiple sculptors and used latest Augmented Reality (AR) technology to guide manual fabrication actions. It was built at the Chinese University of Hong Kong in the fall of 2019 by thirty participants of a design-and-build workshop on the integration of AR in construction. As part of its construction workflow, holographic setups were created on multiple devices, including a series of Microsoft HoloLenses and several handheld Smartphones, all linked simultaneously to a single digital base model to interactively guide the manufacturing process. This paper critically evaluates the experience of extending recent AR and MR tool developments towards applications that centre on creative collaborative production. Using ARgan as a demonstrator project, its developed workflow is assessed on its ability to transform a geometrically complex digitally drafted design to its final physically built form, highlighting the necessary strategic integration of variability as an opportunity to relax notions on design precision and exact control. The paper concludes with a plea for digital technology's ability to stimulate dialogue and collaboration in creative production and augment craftsmanship, thus providing greater agency and more diverse design output.
keywords	Augmented-Reality; Mixed-Reality; Post-digital; High-tech vs low-tech; Bamboo
series	CAADRIA
email
last changed	2022/06/07 07:51

_id	caadria2020_222
id	caadria2020_222
authors	Sun, Chengyu and Hu, Wei
year	2020
title	A Rapid Building Density Survey Method Based on Improved Unet
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. 649-658
doi	https://doi.org/10.52842/conf.caadria.2020.2.649
summary	How to rapidly obtain building density information in a large range is a key problem for architecture and planning. This is because architectural design or urban planning is not isolated, and the environment of the building is influenced by the distribution of other buildings in a larger area. For areas where building density data are not readily available, the current methods to estimate building density are more or less inadequate. For example, the manual survey method is relatively slow and expensive, the traditional satellite image processing method is not very accurate or needs to purchase high-precision multispectral remote sensing image from satellite companies. Based on the deep neural network, this paper proposes a method to quickly extract large-scale building density information by using open satellite images platforms such as Baidu map, Google Earth, etc., and optimizes the application in the field of building and planning. Compared with the traditional method, it has the advantages of less time and money, higher precision, and can provide data support for architectural design and regional planning rapidly and conveniently.
keywords	building density; rapidly and conveniently; neural network
series	CAADRIA
email
last changed	2022/06/07 07:56

_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	ecaade2020_075
id	ecaade2020_075
authors	Yoffe, Hatzav, Plaut, Pnina, Fried, Shaked and J. Grobman, Yasha
year	2020
title	Enriching the Parametric Vocabulary of Urban Landscapes - A framework for computer-aided performance evaluation of sustainable development design models
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. 47-56
doi	https://doi.org/10.52842/conf.ecaade.2020.1.047
summary	Three decades past since the adoption of sustainability rating systems (SRS) by the Architecture Engineering and Construction industry (AEC) as standard methods for sustainable development evaluation. Nevertheless, these methods still suffer from a low adoption and implementation rate due to their manual, labor-intensive, expert dependent, and time-demanding process. The partial success of urban development evaluation puts forth the question: Are there faster, more accurate quantitative methods for advancing sustainability evaluation? The paper describes a prototype workflow for evaluating the performance of urban landscape design in a single digital workflow, based on ecological key indicator criteria. Grasshopper and Python parametric platforms were used to translate the criteria into quantitative spatial metrics. This study demonstrates optimized biomass measurement in two urban scales in line with the SITES rating system for landscape development: (XS) site development and (XL) neighborhood scale. The measured biomass density is used as a positive indication of ecosystem services capacity in the development site. The framework's quantitative workflow contributes to additional spatial feedbacks compared to the original numeric-based rating system method. Through these, composition and configuration metrics such as ecological connectivity, edge contrast, and patch shape can be visualized, measured, and compared. The metrics, which indicate performance characteristics of the design, generate new opportunities for data-rich sustainability evaluations of urban landscapes, using a single computer-aided workflow.
keywords	Sustainable development; Urban landscape
series	eCAADe
email
last changed	2022/06/07 07:57

_id	ijac202018304
id	ijac202018304
authors	Aagaard, Anders Kruse and Niels Martin Larsen
year	2020
title	Developing a fabrication workflow for irregular sawlogs
source	International Journal of Architectural Computing vol. 18 - no. 3, 270-283
summary	In this article, we suggest using contemporary manufacturing technologies to integrate material properties with architectural design tools, revealing new possibilities for the use of wood in architecture. Through an investigative approach, material capacities and fabrication methods are explored and combined towards establishing new workflows and architectural expressions, where material, fabrication and result are closely interlinked. The experimentation revolves around discarded, crooked oak logs, doomed to be used as firewood due to their irregularity. This project treats their diverging shapes differently by offering unique processing to each log informed by its particularities. We suggest here a way to use the natural forms and properties of sawlogs to generate new structures and spatial conditions. In this article, we discuss the scope of this approach and provide an example of a workflow for handling the discrete shapes of natural sawlogs in a system that involve the collection of material, scanning/digitisation, handling of a stockpile, computer analysis, design and robotic manufacturing. The creation of this specific method comes from a combination of investigation of wood as a material, review of existing research in the field, studies of the production lines in the current wood industry and experimentation through our in-house laboratory facilities. As such, the workflow features several solutions for handling the complex and different shapes and data of natural wood logs in a highly digitised machining and fabrication environment. This up-cycling of discarded wood supply establishes a non-standard workflow that utilises non-standard material stock and leads to a critical articulation of today’s linear material economy. The project becomes part of an ambition to reach sustainable development goals and technological innovation in global and resource-intensive architecture and building industry.
keywords	Natural wood, robotic fabrication, computation, fabrication, research by design
series	journal
email
last changed	2020/11/02 13:34

_id	ecaade2020_089
id	ecaade2020_089
authors	Ardic, Sabiha Irem, Kirdar, Gulce and Lima, Angela Barros
year	2020
title	An Exploratory Urban Analysis via Big Data Approach: Eindhoven Case - Measuring popularity based on POIs, accessibility and perceptual quality parameters
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. 309-318
doi	https://doi.org/10.52842/conf.ecaade.2020.2.309
summary	The cities are equipped with the data as a result of the individuals' sharings and application usage. This significant amount of data has the potential to reveal relations and support user-centric decision making. The focus of the research is to examine the relational factors of the neighborhoods' popularity by implementing a big data approach to contribute to the problem of urban areas' degradation. This paper presents an exploratory urban analysis for Eindhoven at the neighborhood level by considering variables of popularity: density and diversity of points of interest (POI), accessibility, and perceptual qualities. The multi-sourced data are composed of geotagged photos, the location and types of POIs, travel time data, and survey data. These different datasets are evaluated using BBN (Bayesian Belief Network) to understand the relationships between the parameters. The results showed a positive and relatively high connection between popularity - population change, accessibility by walk - density of POIs, and the feeling of safety - social cohesion. For further studies, this approach can contribute to the decision-making process in urban development, specifically in real estate and tourism development decisions to evaluate the land prices or the hot-spot touristic places.
keywords	big data approach; neighborhood analysis; popularity; point of interest (POI); accessibility; perceptual quality
series	eCAADe
email
last changed	2022/06/07 07:54

_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	sigradi2021_302
id	sigradi2021_302
authors	Bueno, Ernesto, Reis Balsini, André and Verde Zein, Ruth
year	2021
title	Analysis by Algorithmic Modeling of Historiographical Data on Modern and Contemporary Brazilian Architecture
source	Gomez, P and Braida, F (eds.), Designing Possibilities - Proceedings of the XXV International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2021), Online, 8 - 12 November 2021, pp. 737–748
summary	Are historiographic diagrams valid instruments for gauging the main constituent aspects of historiographic documentation of a body of architectural production? The paper aims to discuss the results obtained by algorithmic modeling and three-dimensional visualization of historiographic data. The analysis method proposes a diagrammatic approach to the research object, established from the fundamentals originally described by Zein (2020). The diagrams were created using the algorithmic modeling software Grasshopper, which allowed us to combine a precise recording of data with an original approach to its interpretation. From the data collected, Cartesian coordinates were established for the generation of curves and interpolation surfaces representative of the computed aspects of certain historiographic narratives. With wide application possibilities, the resulting algorithmic diagrams establish a new model for data analysis and visualization, which stands as a consistent alternative to other more commonly used digital bibliometric tools.
keywords	Análise de dados, Big Data, Visualizaçao de dados, Historiografia, Arquitetura moderna brasileira
series	SIGraDi
email
last changed	2022/05/23 12:11

_id	caadria2020_444
id	caadria2020_444
authors	Higgs, Baptiste and Doherty, Ben
year	2020
title	Sanitary Sanity: Evaluating Privacy Preserving Machine Learning Methods for Post-occupancy Evaluation
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. 697-706
doi	https://doi.org/10.52842/conf.caadria.2020.2.697
summary	Traditional post-occupancy evaluation (POE) of building performance has typically privileged physical building attributes over human behavioural data. This is due to a lack of capability and is especially the case for private spaces such as Sanitary Facilities (SFs). A privacy-preserving sensor-based system using Machine Learning (ML) was previously developed, however it was limited to basic body position classification. Yet, SF usage behaviour can be significantly more complex. This research accordingly builds on the aforementioned work to expand behavioural classifications using a sensor-based ML system. Specifically, the case study uses a GridEYE thermal sensor array, which is trained on a cubicle location within a workplace SF. A variety of ML algorithms are then evaluated on their behaviour-classifying ability. A detailed analysis of behaviour-classification performance is then provided. A system with greater fidelity is thus demonstrated, albeit hampered by imprecise behaviour definitions. Regardless, this contributes to the capability of the broader field of research that is investigating Evidence Based Design (EBD) by extending the ability to examine human behaviour, especially in private spaces. This further contributes to the growing body of work surrounding SF provision.
keywords	EBD; Data; Internet of Things; Machine Learning; Post Occupancy Evaluation
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	ecaade2020_497
id	ecaade2020_497
authors	Kim, Eunsu, Rosenwasser, David and Garcia del Castillo Lopez, Jose Luis
year	2020
title	Urban Emotion - The interrogation of social media and its implications within urban context
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. 475-482
doi	https://doi.org/10.52842/conf.ecaade.2020.2.475
summary	This paper presents social media as an analytical tool, helping to transform public policy-making, alongside urban needs by dissecting and evaluating human perception. Using emotion analysis on data gathered from a social media platform, experiments are developed to bring new value to architectural and civic narratives. Emotions from texts collected within social media platforms are extracted and mapped alongside tagged locations to gain a greater understanding of how public spaces are utilized. This project develops a new analytical layer within our built environment, working alongside the urban fabric, mechanical systems, and digital infrastructure. It is offered as an interactive tool for policymakers and designers to glean feedback, creating an informed conversation between citizens and decision-makers. Whereas social media platforms such as Twitter and Yelp have been referenced in past academic contexts, this project moves further by producing quantified emotions, painting a differentiated result from what purely semantic data could deliver.
keywords	Social Media; Mapping; Natural Language Processing
series	eCAADe
email
last changed	2022/06/07 07:52

_id	acadia20_170
id	acadia20_170
authors	Li, Peiwen; Zhu, Wenbo
year	2020
title	Clustering and Morphological Analysis of Campus Context
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. 170-177.
doi	https://doi.org/10.52842/conf.acadia.2020.2.170
summary	“Figure-ground” is an indispensable and significant part of urban design and urban morphological research, especially for the study of the university, which exists as a unique product of the city development and also develops with the city. In the past few decades, methods adapted by scholars of analyzing the figure-ground relationship of university campuses have gradually turned from qualitative to quantitative. And with the widespread application of AI technology in various disciplines, emerging research tools such as machine learning/deep learning have also been used in the study of urban morphology. On this basis, this paper reports on a potential application of deep clustering and big-data methods for campus morphological analysis. It documents a new framework for compressing the customized diagrammatic images containing a campus and its surrounding city context into integrated feature vectors via a convolutional autoencoder model, and using the compressed feature vectors for clustering and quantitative analysis of campus morphology.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ijac202018402
id	ijac202018402
authors	Mette Ramsgaard Thomsen, Paul Nicholas, Martin Tamke, Sebastian Gatz, Yuliya Sinke and Gabriella Rossi
year	2020
title	Towards machine learning for architectural fabrication in the age of industry 4.0
source	International Journal of Architectural Computing vol. 18 - no. 4, 335–352
summary	Machine Learning (ML) is opening new perspectives for architectural fabrication, as it holds the potential for the profession to shortcut the currently tedious and costly setup of digital integrated design to fabrication workflows and make these more adaptable. To establish and alter these workflows rapidly becomes a main concern with the advent of Industry 4.0 in building industry. In this article we present two projects, which presents how ML can lead to radical changes in generation of fabrication data and linking these directly to design intent. We investigate two different moments of implementation: linking performance to the generation of fabrication data (KnitCone) and integrating the ability to adapt fabrication data in realtime as response to fabrication processes (Neural-Network Steered Robotic Fabrication). Together they examine how models can employ design information as training data and be trained to by step processes within the digital chain. We detail the advantages and limitations of each experiment, we reflect on core questions and perspectives of ML for architectural fabrication: the nature of data to be used, the capacity of these algorithms to encode complexity and generalize results, their task-specificness versus their adaptability and the tradeoffs of using them with respect to conventional explicit analytical modelling.
keywords	Machine learning, architectural design, industry 4.0, digital fabrication, robotic fabrication, CNC knit, neural networks
series	journal
email
last changed	2021/06/03 23:29

_id	sigradi2020_930
id	sigradi2020_930
authors	Montás Laracuente, Nelson; Barinas Uribe, Marcos
year	2020
title	In-Situ & Computational Façade Performance Analysis: The B1- Campus A University Building Case in Sto. Dgo., Dom. Rep.
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. 930-938
summary	This paper presents experimental and simulated façade thermal and humidity performance assessments concerning three (3) types of widely used façade systems in the Dominican construction market: 8” block wall, ventilated façade & curtain wall. Using indoor and outdoor temperature (/1T) and humidity differences (/1H) as indicators in order to compare said performances between the systems and, in turn, with environmental simulations approximating them, we try to diagnose weaknesses and foresee improvement avenues for sustainable façade systems in the Dominican context. The data was obtained by on-site measurements using eight (8) temperature and relative humidity sensors in a twelve (12) storey building in Santo Domingo, Dominican Republic.
keywords	Façade performance, Temperature, Relative humidity, Environmental simulation, Sensors
series	SIGraDi
email
last changed	2021/07/16 11:53

_id	ecaade2020_167
id	ecaade2020_167
authors	Newton, David, Piatkowski, Dan, Marshall, Wesley and Tendle, Atharva
year	2020
title	Deep Learning Methods for Urban Analysis and Health Estimation of Obesity
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. 297-304
doi	https://doi.org/10.52842/conf.ecaade.2020.1.297
summary	In the 20th and 21st centuries, urban populations have increased dramatically with a whole host of impacts to human health that remain unknown. Research has shown significant correlations between design features in the built environment and human health, but this research has remained limited. A better understanding of this relationship could allow urban planners and architects to design healthier cities and buildings for an increasingly urbanized population. This research addresses this problem by using discriminative deep learning in combination with satellite imagery of census tracts to estimate rates of obesity. Data from the California Health Interview Survey is used to train a Convolutional Neural Network that uses satellite imagery of selected census tracts to estimate rates of obesity. This research contributes knowledge on methods for applying deep learning to urban health estimation, as well as, methods for identifying correlations between urban morphology and human health.
keywords	Deep Learning; Artificial Intelligence; Urban Planning; Health; Remote Sensing
series	eCAADe
email
last changed	2022/06/07 07:58

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