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

PDF papers
References

Hits 1 to 20 of 614

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

_id	ecaadesigradi2019_550
id	ecaadesigradi2019_550
authors	Rhee, Jinmo, Cardoso Llach, Daniel and Krishnamurti, Ramesh
year	2019
title	Context-rich Urban Analysis Using Machine Learning - A case study in Pittsburgh, PA
doi	https://doi.org/10.52842/conf.ecaade.2019.3.343
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 3, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 343-352
summary	This paper reports on the analytical potential of machine learning methods for urban analysis. It documents a new method for data-driven urban analysis based on diagrammatic images describing each building in a city in relation to its immediate urban context. By statistically analyzing architectural and contextual features in this new dataset, the method can identify clusters of similar urban conditions and produce a detailed picture of a city's morphological structure. Remapping the clusters from data to 2D space, our method enables a new kind of urban plan that displays gradients of urban similarity. Taking Pittsburgh as a case study we demonstrate this method, and propose "morphological types" as a new category of urban analysis describing a given city's specific set of distinct morphological conditions. The paper concludes with a discussion of the implications of this method and its limitations, as well as its potentials for architecture, urban studies, and computation.
keywords	Urban Morphology; Machine Learning; Architectural Contexts; Urban Analysis; GIS
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:56

_id	ecaadesigradi2019_084
id	ecaadesigradi2019_084
authors	Lima, Fernando, Vallone, Luiza, Costa, Carlos Frederico and Rosa, Ashiley
year	2019
title	(Para)metric Evaluation of Walkability, Diversity and Density in Low-income Neighborhoods - Using the CityMetrics toolbox
doi	https://doi.org/10.52842/conf.ecaade.2019.3.257
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 3, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 257-266
summary	This paper describes an implementation of the CityMetrics toolbox, in order to provide a dynamic assessment of metrics related to walkability, diversity and density in remote and low-income urban areas. The applied methodology was used in two remote neighborhoods of Juiz de Fora, which is a Brazilian city, in a case study. The objective was to identify and to evaluate a set of weaknesses in the addressed areas and to propose some improvements in the neighborhoods´ arrangements. The ultimate goal is to contribute to a better understanding of urban problems according to walkability, diversity and density, as well as to contribute to the discussion on the design and implementation of low-income real estate developments, facilitating the management of solutions in urban planning processes in this context.
keywords	Urban analysis; Low-income urban areas; CityMetrics; Walkability; Diversity; Density
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:59

_id	caadria2021_053
id	caadria2021_053
authors	Rhee, Jinmo and Veloso, Pedro
year	2021
title	Generative Design of Urban Fabrics Using Deep Learning
doi	https://doi.org/10.52842/conf.caadria.2021.1.031
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 31-40
summary	This paper describes the Urban Structure Synthesizer (USS), a research prototype based on deep learning that generates diagrams of morphologically consistent urban fabrics from context-rich urban datasets. This work is part of a larger research on computational analysis of the relationship between urban context and morphology. USS relies on a data collection method that extracts GIS data and converts it to diagrams with context information (Rhee et al., 2019). The resulting dataset with context-rich diagrams is used to train a Wasserstein GAN (WGAN) model, which learns how to synthesize novel urban fabric diagrams with the morphological and contextual qualities present in the dataset. The model is also trained with a random vector in the input, which is later used to enable parametric control and variation for the urban fabric diagram. Finally, the resulting diagrams are translated to 3D geometric entities using computer vision techniques and geometric modeling. The diagrams generated by USS suggest that a learning-based method can be an alternative to methods that rely on experts to build rule sets or parametric models to grasp the morphological qualities of the urban fabric.
keywords	Deep Learning; Urban Fabric; Generative Design; Artificial Intelligence; Urban Morphology
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	ecaadesigradi2019_627
id	ecaadesigradi2019_627
authors	Yang, Yang, Samaranayake, Samitha and Dogan, Timur
year	2019
title	Using Open Data to Derive Local Amenity Demand Patterns for Walkability Simulations and Amenity Utilization Analysis
doi	https://doi.org/10.52842/conf.ecaade.2019.2.665
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 665-674
summary	Understanding human behavior and preferences are important for urban planning and the design of walkable neighborhoods. However, it remains challenging to study human activity patterns because significant efforts are required to collect the relevant data, convert unstructured data into useful knowledge, and take into consideration different urban contexts. In the context of the heated discussion about urban walkability and amenities, as well as the need of identifying a feasible approach to analyze human activities, this paper proposes a simple and effective metric of the amenity demand patterns, which demonstrates the spatiotemporal distribution of human activities according to the activeness in urban amenities. Such metric has the potential to support the urban study about people, mobility, and built environment, as well as other relevant design thinking. Further, a case study illustrates the data and the new metric can be used in walkability simulations and amenity utilization analysis, thus informing the design decision-making process.
keywords	Big Data; Urban Amenity; Walkability; Human Activity
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:57

_id	cf2019_048
id	cf2019_048
authors	Argota Sanchez-Vaquerizo, Javier and Daniel Cardoso Llach
year	2019
title	The Social Life of Small Urban Spaces 2.0 Three Experiments in Computational Urban Studies
source	Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, p. 430
summary	This paper introduces a novel framework for urban analysis that leverages computational techniques, along with established urban research methods, to study how people use urban public space. Through three case studies in different urban locations in Europe and the US, it demonstrates how recent machine learning and computer vision techniques may assist us in producing unprecedently detailed portraits of the relative influence of urban and environmental variables on people’s use of public space. The paper further discusses the potential of this framework to enable empirically-enriched forms of urban and social analysis with applications in urban planning, design, research, and policy.
keywords	Data Analytics, Urban Design, Machine Learning, Artificial Intelligence, Big Data, Space Syntax
series	CAAD Futures
email
last changed	2019/07/29 14:18

_id	caadria2019_143
id	caadria2019_143
authors	Kato, Yuri and Matsukawa, Shohei
year	2019
title	Development of Generating System for Architectural Color Icons Using Google Map Platform and Tensorflow-Segmentation
doi	https://doi.org/10.52842/conf.caadria.2019.2.081
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 2, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 81-90
summary	In this research, the goal is to develop a generating system for architectural color icons using Google Map Platform and Tensorflow-Segmentation. There has been no case of developing a system that allows users to visualize the color tendency of buildings as architectural color icons for each building element from images of various regions. It is considered meaningful to be able to create criteria for decision making in architecture and the urban design by developing a system to clarify the current state of the architectural colors. It will contribute a rise in the consciousness of landscape conservation and be essential for the design of architectures and public objects. This paper includes the explanation of development method, use experiments, and consideration of five problems among architectural color icons creation. It is assumed that the accuracy of the present system will be better as the technology improves.
keywords	Google street view; machine learning; image segmentation; color palette; color analysis
series	CAADRIA
email
last changed	2022/06/07 07:52

_id	cf2019_014
id	cf2019_014
authors	Ferrando, Cecilia; Niccolo Dalmasso, Jiawei Mai, Daniel Cardoso Llach
year	2019
title	Architectural Distant Reading Using Machine Learning to Identify Typological Traits Across Multiple Buildings
source	Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, pp. 114-127
summary	This paper introduces an approach to architectural “distant reading”: the use of computational methods to analyze architectural data in order to derive spatial insights from—and explore new questions concerning—large collections of architectural work. Through a case study comprising a dataset of religious buildings, we show how we may use machine learning techniques to identify typological and functional traits from building plans. We find that spatial structure, rather than local features, is particularly effective in supporting this type of analysis. Further, we speculate on the potential of this computational method to enrich architectural design, research, and criticism by, for example, enabling new ways of thinking about architectural concepts such as typology in ways that reflect gradual variations, rather than sharp distinctions.
keywords	Architectural Analytics, Machine Learning, Classification, Religious buildings, Space Syntax
series	CAAD Futures
email
last changed	2019/07/29 14:08

_id	ecaadesigradi2019_405
id	ecaadesigradi2019_405
authors	da Cunha Teixeira, Luísa and Cury Paraizo, Rodrigo
year	2019
title	Caronae - ridesharing and first steps into commuting opportunitie of academic exchange
doi	https://doi.org/10.52842/conf.ecaade.2019.1.805
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 1, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 805-816
summary	Location-based mobile applications have been a rising theme for academics in the field of urbanism and in urban and transportation, because of the potential of transformation they might bring to the urban landscape (De Souza e Silva, 2013). One of the possibilities we study here is to observe social encounters fostered by commuting rides. In this paper, we try to examine the practice from the broad perspective of estimating the environmental benefits, in a context where digital information technology is wielded to address problems old and new (Townsend, 2014). This paper aims to analyze the potential of transformations that new ICTs bring to urban mobility, using as case study the official ridesharing system of the Federal University of Rio de Janeiro, the Carona? project. The system was developed focusing on the reduction of the number of motorized trips to the University, as well as the amount of CO2 generated by them. Here we analyze the dynamics of ridesharing, using the system data, and also try to observe the role it may play towards the promotion of integration in the UFRJ community.
keywords	mobile apps; urban mobility; ridesharing; caronae ufrj
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:56

_id	ecaadesigradi2019_273
id	ecaadesigradi2019_273
authors	Hadighi, Mahyar and Duarte, Jose
year	2019
title	Using Grammars to Trace Architectural Hybridity in American Modernism - The case of William Hajjar single-family house
doi	https://doi.org/10.52842/conf.ecaade.2019.1.529
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 1, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 529-540
summary	In this paper, mid-century modern single-family houses designed by William Hajjar are analyzed through a shape grammar methodology within the context of the traditional architecture of an American college town. A member of the architecture faculty at the Pennsylvania State University, Hajjar was a practitioner in State College, PA, where the University Park campus is located, and an influential figure in the history of architecture in the area. The residential architecture he designed for and built in the area incorporates many of the formal and functional features typical of both modern European architecture and traditional American architecture. Based on a computational methodology, this study offers an investigation into this hybridity phenomenon by exploring Hajjar's architecture in relation to the traditional American architecture prevalent in the college town of State College.
keywords	shape grammar; American architecture; William Hajjar; hybridity; college town
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:49

_id	caadria2019_223
id	caadria2019_223
authors	Han, Yunsong, Pan, Yongjie, Zhao, Tianyu, Wang, Chunxing and Sun, Cheng
year	2019
title	Use of UAV Photogrammetry to Estimate the Solar Energy Potential of Residential Buildings in Severe Cold Region
doi	https://doi.org/10.52842/conf.caadria.2019.2.613
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 2, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 613-622
summary	In this paper, a method based on UAV photogrammetry is proposed to estimate the solar energy potential of the building surface. This methodology goes from the acquired aerial images captured by the camera mounted on UAV. 3D model of the urban context in study area was extracted from the aerial images using SFM and MVS algorithms, which could be directly applied to the Ladybug plugin as analysis objects. Estimates of solar radiation are expressed by means of data visualization. The results showed that the UAV photogrammetry could demonstrate the geometry and texture of residential buildings precisely and the solar radiation simulation results showed significant spatial and temporal variations in solar radiation on residential buildings.
keywords	Residential buildings; UAV photogrammetry; 3D reconstruction; Solar energy potential; Severe cold region
series	CAADRIA
email
last changed	2022/06/07 07:50

_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
doi	https://doi.org/10.52842/conf.caadria.2020.2.669
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
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	caadria2019_600
id	caadria2019_600
authors	Subramanian, Ramanathan, Tuncer, Bige and Binder, Alexander
year	2019
title	Thermal Comfort Based Performance Appraisal of Covered Walkways in Singapore
doi	https://doi.org/10.52842/conf.caadria.2019.1.805
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 1, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 805-814
summary	This paper describes an ongoing research project to establish a thermal comfort based walkway performance analysis that embodies the effect of context and climate. This study combines the survey data (perceived comfort) from walkway users and thermal sensor data (actual thermal comfort) collected at various covered walkways across Singapore. One contribution is the combination of subjective and objective comfort measurements in a tropical context . We work with descriptive statistical measures to help better understand the ranges of thermal comfort offered by covered walkways. This research highlighted that the comfort offered by current walkways were identified to have no significance, and the walkways are unable to reduce the heat stress into the moderate range at all times of the day. A key contribution of this research project identified missing datasets and help improve our data collection methodology for the future expansion dataset that employ machine learning.
keywords	Biometeorology; Data analytics; Informed design
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	caadria2019_396
id	caadria2019_396
authors	Cao, Rui, Fukuda, Tomohiro and Yabuki, Nobuyoshi
year	2019
title	Quantifying Visual Environment by Semantic Segmentation Using Deep Learning - A Prototype for Sky View Factor
doi	https://doi.org/10.52842/conf.caadria.2019.2.623
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 2, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 623-632
summary	Sky view factor (SVF) is the ratio of radiation received by a planar surface from the sky to that received from the entire hemispheric radiating environment, in the past 20 years, it was more applied to urban-climatic areas such as urban air temperature analysis. With the urbanization and the development of cities, SVF has been paid more and more attention on as the important parameter in urban construction and city planning area because of increasing building coverage ratio to promote urban forms and help creating a more comfortable and sustainable urban residential building environment to citizens. Therefore, efficient, low cost, high precision, easy to operate, rapid building-wide SVF estimation method is necessary. In the field of image processing, semantic segmentation based on deep learning have attracted considerable research attention. This study presents a new method to estimate the SVF of residential environment by constructing a deep learning network for segmenting the sky areas from 360-degree camera images. As the result of this research, an easy-to-operate estimation system for SVF based on high efficiency sky label mask images database was developed.
keywords	Visual environment; Sky view factor; Semantic segmentation; Deep learning; Landscape simulation
series	CAADRIA
email
last changed	2022/06/07 07:54

_id	ecaadesigradi2019_250
id	ecaadesigradi2019_250
authors	Czyñska, Klara
year	2019
title	Visual Impact Analysis of Large Urban Investments on the Cityscape
doi	https://doi.org/10.52842/conf.ecaade.2019.3.297
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 3, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 297-304
summary	The article presents the assessment method for large (horizontally spread) urban investment and its visual impact on the cityscape using digital analyses. The visual impact assessment is often used in relation to facilities which dominate in the cityscape, mainly tall buildings. Various studies, however, examine the impact of wide but relatively low-rising buildings and their impact on the cityscape. The article presents a methodology for the assessment of the visual impact and a case study for a building facility comprising several tightly developed and medium height blocks of buildings in a city center of a significant historical value in Gdañsk, Poland. The research has been based on the Visual Impact Size method (VIS) and a city model consisting of a regular cloud of points (Digital Surface Model). The simulation has been developed using a dedicated C++ software (developed by author). The study aimed at assessing the following: a) to what degree such an urban investment can influence the cityscape; b) how the impact can be analyzed using digital techniques, and c) what input parameters of the analysis are crucial for satisfactory accuracy of its results.
keywords	digital cityscape analysis; urban skyline; large urban investments; visual impact; VIS method
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:56

_id	caadria2019_080
id	caadria2019_080
authors	Green, Stephen, King, Geoff, Fabbri, Alessandra, Gardner, Nicole, Haeusler, M. Hank and Zavoleas, Yannis
year	2019
title	Designing Out Urban Heat Islands - Optimisation of footpath materials with different albedo value through evolutionary algorithms to address urban heat island effect
doi	https://doi.org/10.52842/conf.caadria.2019.2.603
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 2, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 603-612
summary	The Urban Heat Island (UHI) effect is pronounced in dense urban developments, and particular an issue in the case study city of Parramatta, where temperature increases are impacting use of public space, health, and economic productivity. To mitigate against elevated temperatures in built up areas, this research explores the optimisation of paving material layouts through using an evolutionary algorithm. High albedo (reflective) materials are objectively cooler than low albedo (absorbent) materials yet tend to be more expensive. To reduce the amount of heat absorbent pavement materials whilst keeping in mind material costs, a range of materials of different albedo levels (reflectivity) can be assigned on the same path using an evolutionary algorithm to optimise the coolest materials for the cheapest price. Over the course of this paper, this research aim will be approached using visual scripting software such as Grasshopper to simulate daylight analysis and to generate an optimisation algorithm. Previous research on the topics of UHI have revealed different methods for solving specific problems, all focusing on using software analysis to determine an informed decision on construction. The paper contributes via a computational approach of material selection to battle urban heat island effects.
keywords	urban heat island; albedo value; material properties; evolutionary algorithm ; landscape architecture
series	CAADRIA
email
last changed	2022/06/07 07:51

_id	cf2019_022
id	cf2019_022
authors	Koh, Immanuel and Jeffrey Huang
year	2019
title	Citizen Visual Search Engine:Detection and Curation of Urban Objects
source	Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, p. 170
summary	Increasingly, the ubiquity of satellite imagery has made the data analysis and machine learning of large geographical datasets one of the building blocks of visuospatial intelligence. It is the key to discover current (and predict future) cultural, social, financial and political realities. How can we, as designers and researchers, empower citizens to understand and participate in the design of our cities amid this technological shift? As an initial step towards this broader ambition, a series of creative web applications, in the form of visual search engines, has been developed and implemented to data mine large datasets. Using open sourced deep learning and computer vision libraries, these applications facilitate the searching, detecting and curating of urban objects. In turn, the paper proposes and formulates a framework to design truly citizen-centric creative visual search engines -- a contribution to citizen science and citizen journalism in spatial terms.
keywords	Deep Learning, Computer Vision, Satellite Imagery, Citizen Science, Artificial Intelligence
series	CAAD Futures
email
last changed	2019/07/29 14:08

_id	caadria2019_452
id	caadria2019_452
authors	Choi, Minkyu, Yi, Taeha, Kim, Meereh and Lee, Ji-Hyun
year	2019
title	Land Price Prediction System Using Case-based Reasoning
doi	https://doi.org/10.52842/conf.caadria.2019.1.767
source	M. Haeusler, M. A. Schnabel, T. Fukuda (eds.), Intelligent & Informed - Proceedings of the 24th CAADRIA Conference - Volume 1, Victoria University of Wellington, Wellington, New Zealand, 15-18 April 2019, pp. 767-774
summary	Real estate price prediction is very complex process. Big data and machine learning technology have been introduced in many research areas, and they are also making such an attempt in the real estate market. Although real estate price forecasting studies is actively conducted, using support vector machine, machine learning algorithm, AHP method, and so on, validity and accuracy are still not reliable.In this research, we propose a Case-Based Reasoning system using regression analysis to allocate weight of attributes. This proposed system can support to predict the real estate price based on collecting public data and easily update the knowledge about real estate. Since the result shows error rate less than 30% through the experiment, this algorithm gives better performance than previous one. By this research, it is possible for help decision-makers to expect the real estate price of interested area.
keywords	Artificial intelligence; Case-based reasoning; Land price prediction; Regression
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	ecaadesigradi2019_150
id	ecaadesigradi2019_150
authors	Thomsen, Mette, Nicholas, Paul, Tamke, Martin, Gatz, Sebastian and Sinke, Yuliya
year	2019
title	Predicting and steering performance in architectural materials
doi	https://doi.org/10.52842/conf.ecaade.2019.2.485
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 485-494
summary	This paper presents the prototyping of new methods by which functionally graded materials can be specified and produced. The paper presents a case study exploring how machine learning can be used to train a model in order to predict fabrication files from formalised design requirements. By using knit as a model for material fabrication, the paper outlines the making of new cyclical design methods employing machine learning in which simpler prototypical materials acts as input for more complex graded materials. A case study - Ombre - showcases the implementation of this workflow and results and perspectives are discussed.
keywords	computational design; material specification; machine learning; convolution algorithm; knit
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:56

_id	ecaadesigradi2019_218
id	ecaadesigradi2019_218
authors	Grasser, Alexander
year	2019
title	Towards an Architecture of Collaborative Objects
doi	https://doi.org/10.52842/conf.ecaade.2019.1.325
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 1, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 325-332
summary	Towards an Architecture of Collaborative Objects, explores the potential of playing with Collaborative Objects in real, augmented and mixed realities. A multi-player game platform App: VoxelCO, developed by the author, provides a speculative playground to research, the interaction with objects, things and people, as well as provokes new opportunities to engage deeply with its content and context. Furthermore, VoxelCO, reveals new modes of participation, to design and collaborate in real-time with augmented reality, using millennial tools: mobile devices. A case study project, the VoxelStage, offered an opportunity to apply VoxelCO to design a stage together with a group of students. To merge the collaboratively aggregated virtual objects of VoxelCO with reality, real WireCubes were augmented and assembled, proposing an architecture of socially augmented fuzzy formations.
keywords	Collaborative Objects; Augmented Reality; Realtime; Fuzzy; Play
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:51

_id	ecaadesigradi2019_671
id	ecaadesigradi2019_671
authors	Jabi, Wassim, Chatzivasileiadi, Aikaterini, Wardhana, Nicholas Mario, Lannon, Simon and Aish, Robert
year	2019
title	The synergy of non-manifold topology and reinforcement learning for fire egress
doi	https://doi.org/10.52842/conf.ecaade.2019.2.085
source	Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 85-94
summary	This paper illustrates the synergy of non-manifold topology (NMT) and a branch of artificial intelligence and machine learning (ML) called reinforcement learning (RL) in the context of evaluating fire egress in the early design stages. One of the important tasks in building design is to provide a reliable system for the evacuation of the users in emergency situations. Therefore, one of the motivations of this research is to provide a framework for architects and engineers to better design buildings at the conceptual design stage, regarding the necessary provisions in emergency situations. This paper presents two experiments using different state models within a simplified game-like environment for fire egress with each experiment investigating using one vs. three fire exits. The experiments provide a proof-of-concept of the effectiveness of integrating RL, graphs, and non-manifold topology within a visual data flow programming environment. The results indicate that artificial intelligence, machine learning, and RL show promise in simulating dynamic situations as in fire evacuations without the need for advanced and time-consuming simulations.
keywords	Non-manifold topology; Topologic; Reinforcement Learning; Fire egress
series	eCAADeSIGraDi
email
last changed	2022/06/07 07:52

For more results click below: