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 653

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

_id	cdrf2019_189
id	cdrf2019_189
authors	Dasong Wang and Roland Snooks
year	2020
title	Artificial Intuitions of Generative Design: An Approach Based on Reinforcement Learning
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_18
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	This paper proposes a Reinforcement Learning (RL) based design approach that augments existing algorithmic generative processes through the emergence of a form of artificial design intuition. The research presented in the paper is embedded within a highly speculative research project, Artificial Agency, exploring the operation of Machine Learning (ML) in generative design and digital fabrication. After describing the inherent limitations of contemporary generative design processes, the paper compares the three fundamental types of machine learning frameworks in terms of their characteristics and potential impact on generative design. A theoretical framework is defined to demonstrate the methodology of integrating RL with existing generative design procedures, which is further explained with a Random Walk based experimental design example. The paper includes detailed RL definitions as well as critical reflections on its impact and the effects of its implementation. The proposed artificial intuition within this generative approach is currently being further developed through a series of ongoing and proposed research trajectories noted in the conclusion. The ambition of this research is to deepen the integration of intention with machine learning in generative design.
series	cdrf
email
last changed	2022/09/29 07:51

_id	acadia20_248
id	acadia20_248
authors	Saha, Nirvik; Haymaker, John; Shelden, Dennis
year	2020
title	Space Allocation Techniques (SAT)
doi	https://doi.org/10.52842/conf.acadia.2020.1.248
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. 248-257.
summary	Architects and urban designers use space allocation to develop layouts constrained by project-specific attributes of spaces and relations between them. The space allocation problem (SAP) is a general class of computable problems that eluded automation due to combinatorial complexity and diversity of architectural forms. In this paper, we propose a solution to the space allocation problem using reinforcement learning (RL). In RL, an artificial agent interacts with a simulation of the design problem to learn the optimal spatial organization of a layout using a feedback mechanism based on project-specific constraints. Compared to supervised learning, where the scope of the design problem is restricted by the availability of prior samples, we developed a general approach using RL to address novel design problems, represented as SAP. We integrated the proposed solution to SAP with numerous geometry modules, collectively defined as the space allocation techniques (SAT). In this implementation, the optimization and generative modules are decoupled such that designers can connect the modules in various ways to generate layouts with desired geometric and topological attributes. The outcome of this research is a user-friendly, freely accessible Rhino Grasshopper (C#) plugin, namely, the Design Optimization Toolset or DOTs, a compilation of the proposed SAT. DOTs allows designers to interactively develop design alternatives that reconcile project-specific constraints with the geometric complexity of architectural forms. We describe how professional designers have applied DOTs in space planning, site parcellation, massing, and urban design problems that integrate with performance analysis to enable a holistic, semi-automated design exploration.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2020_093
id	ecaade2020_093
authors	Veloso, Pedro and Krishnamurti, Ramesh
year	2020
title	An Academy of Spatial Agents - Generating spatial configurations with deep reinforcement learning
doi	https://doi.org/10.52842/conf.ecaade.2020.2.191
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. 191-200
summary	Agent-based models rely on decentralized decision making instantiated in the interactions between agents and the environment. In the context of generative design, agent-based models can enable decentralized geometric modelling, provide partial information about the generative process, and enable fine-grained interaction. However, the existing agent-based models originate from non-architectural problems and it is not straight-forward to adapt them for spatial design. To address this, we introduce a method to create custom spatial agents that can satisfy architectural requirements and support fine-grained interaction using multi-agent deep reinforcement learning (MADRL). We focus on a proof of concept where agents control spatial partitions and interact in an environment (represented as a grid) to satisfy custom goals (shape, area, adjacency, etc.). This approach uses double deep Q-network (DDQN) combined with a dynamic convolutional neural-network (DCNN). We report an experiment where trained agents generalize their knowledge to different settings, consistently explore good spatial configurations, and quickly recover from perturbations in the action selection.
keywords	space planning; agent-based model; interactive generative systems; artificial intelligence; multi-agent deep reinforcement learning
series	eCAADe
email
last changed	2022/06/07 07:58

_id	ecaade2020_253
id	ecaade2020_253
authors	Buš, Peter
year	2020
title	User-driven Configurable Architectural Assemblies - Towards artificial intelligence-embedded responsive environments
doi	https://doi.org/10.52842/conf.ecaade.2020.2.483
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. 483-490
summary	The paper theoretically elaborates the idea of individual users' customisation activities to create and configure responsive spatial scenarios by means of reconfigurable interactive adaptive assemblies. It reflects Gordon Pask's concept of human and device interaction based on its unpredictable notion speculating a potential to be enhanced by artificial intelligence learning approach of an assembly linked with human activator's participative inputs. Such a link of artificial intelligence, human agency and interactive assembly capable to generate its own spatial configurations by itself and users' stimuli may lead to a new understanding of humans' role in the creation of spatial scenarios. The occupants take the prime role in the evolution of spatial conditions in this respect. The paper aims to position an interaction between the human agents and artificial devices as a participatory and responsive design act to facilitate creative potential of participants as unique individuals without pre-specified or pre-programmed goal set by the designer. Such an approach will pave a way towards true autonomy of responsive built environments, determined by an individual human agent and behaviour of the spatial assemblies to create authentic responsive built forms in a digital and physical space.
keywords	deployable systems; responsive assemblies; embedded intelligence; Learning-to-Design-and-Assembly method; Conversation Theory
series	eCAADe
email
last changed	2022/06/07 07:54

_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
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_18
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
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	cdrf2019_134
id	cdrf2019_134
authors	Zhen Han, Wei Yan, and Gang Liu
year	2020
title	A Performance-Based Urban Block Generative Design Using Deep Reinforcement Learning and Computer Vision
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_13
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	In recent years, generative design methods are widely used to guide urban or architectural design. Some performance-based generative design methods also combine simulation and optimization algorithms to obtain optimal solutions. In this paper, a performance-based automatic generative design method was proposed to incorporate deep reinforcement learning (DRL) and computer vision for urban planning through a case study to generate an urban block based on its direct sunlight hours, solar heat gains as well as the aesthetics of the layout. The method was tested on the redesign of an old industrial district located in Shenyang, Liaoning Province, China. A DRL agent - deep deterministic policy gradient (DDPG) agent - was trained to guide the generation of the schemes. The agent arranges one building in the site at one time in a training episode according to the observation. Rhino/Grasshopper and a computer vision algorithm, Hough Transform, were used to evaluate the performance and aesthetics, respectively. After about 150 h of training, the proposed method generated 2179 satisfactory design solutions. Episode 1936 which had the highest reward has been chosen as the final solution after manual adjustment. The test results have proven that the method is a potentially effective way for assisting urban design.
series	cdrf
email
last changed	2022/09/29 07:51

_id	ecaade2020_227
id	ecaade2020_227
authors	Bielski, Jessica, Langenhan, Christoph, Weyand, Babara, Neuber, Markus, Eisenstadt, Viktor and Althoff, Klaus-Dieter
year	2020
title	Topological Queries and Analysis of School Buildings Based on Building Information Modeling (BIM) Using Parametric Design Tools and Visual Programming to Develop New Building Typologies
doi	https://doi.org/10.52842/conf.ecaade.2020.2.279
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. 279-288
summary	School buildings are currently one of the largest portions of planning and building projects in Germany. In order to reflect the continuous developments in school building construction with constantly changing spatial requirements, an approach to analyse, derive and combine patterns of schools is proposed to adapt school typologies accordingly. Therefore, the topology is analysed, concerning interconnection methods, such as adjacency, accessibility, depth, and flow. The geometric analysis of e.g. room sizes or spatial proportions is enhanced by including grouping of rooms, estimated room clusters, or room shapes. Furthermore, text-matching is used to determine e.g. room program fulfilment, or assigning functional room descriptions to predefined room types, revealing huge differences of terms throughout time and architects. First results of the analyses show a relevant correlation between spatial proportion and room types.
keywords	school building typologies; building information modeling (BIM); artificial intelligence (AI); topology; spatial analysis; digital semantic model
series	eCAADe
email
last changed	2022/06/07 07:52

_id	cdrf2019_17
id	cdrf2019_17
authors	Chuan Liu, Jiaqi Shen, Yue Ren, and Hao Zheng
year	2020
title	Pipes of AI – Machine Learning Assisted 3D Modeling Design
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_2
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	Style transfer is a design technique that is based on Artificial Intelligence and Machine Learning, which is an innovative way to generate new images with the intervention of style images. The output image will carry the characteristic of style image and maintain the content of the input image. However, the design technique is employed in generating 2D images, which has a limited range in practical use. Thus, the goal of the project is to utilize style transfer as a toolset for architectural design and find out the possibility for a 3D modeling design. To implement style transfer into the research, floor plans of different heights are selected from a given design boundary and set as the content images, while a framework of a truss structure is set as the style image. Transferred images are obtained after processing the style transfer neural network, then the geometric images are translated into floor plans for new structure design. After the selection of the tilt angle and the degree of density, vertical components that connecting two adjacent layers are generated to be the pillars of the structure. At this stage, 2D style transferred images are successfully transformed into 3D geometries, which can be applied to the architectural design processes. Generally speaking, style transfer is an intelligent design tool that provides architects with a variety of choices of idea-generating. It has the potential to inspire architects at an early stage of design with not only 2D but also 3D format.
series	cdrf
email
last changed	2022/09/29 07:51

_id	ecaade2020_501
id	ecaade2020_501
authors	La Russa, Federico Mario and Santagati, Cettina
year	2020
title	From the Cognitive to the Sentient Building - Machine Learning for the preservation of museum collections in historical architecture
doi	https://doi.org/10.52842/conf.ecaade.2020.2.507
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. 507-516
summary	The aim of this paper is to evaluate the efficacy of the Digital Twin approach to achieving Sentient buildings able to develop preservation and conservation action plans from environmental data. The case study is based on the integration of an H-BIM model with an AI-based Decision Support System implementing Machine Learning techniques for conserving museum collections in historical buildings.
keywords	Digital Twin; Historical Architecture; Artificial Intelligence; Decision Support System; Museum Collections; Preventive Conservation
series	eCAADe
email
last changed	2022/06/07 07:52

_id	cdrf2019_103
id	cdrf2019_103
authors	Runjia Tian
year	2020
title	Suggestive Site Planning with Conditional GAN and Urban GIS Data
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_10
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	In architecture, landscape architecture, and urban design, site planning refers to the organizational process of site layout. A fundamental step for site planning is the design of building layout across the site. This process is hard to automate due to its multi-modal nature: it takes multiple constraints such as street block shape, orientation, program, density, and plantation. The paper proposes a prototypical and extensive framework to generate building footprints as masterplan references for architects, landscape architects, and urban designers by learning from the existing built environment with Artificial Neural Networks. Pix2PixHD Conditional Generative Adversarial Neural Network is used to learn the mapping from a site boundary geometry represented with a pixelized image to that of an image containing building footprint color-coded to various programs. A dataset containing necessary information is collected from open source GIS (Geographic Information System) portals from the city of Boston, wrangled with geospatial analysis libraries in python, trained with the TensorFlow framework. The result is visualized in Rhinoceros and Grasshopper, for generating site plans interactively.
series	cdrf
email
last changed	2022/09/29 07:51

_id	ijac202119406
id	ijac202119406
authors	Silva Dória, David Rodrigues; Ramaswami, Keshav; Claypool, Mollie; Retsin, Gilles
year	2021
title	Public parts, resocialized autonomous communal life
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 4, 568–593
summary	Commoning embodies the product of social contracts and behaviors between groups of individuals. In thecase of social housing and the establishment of physical domains for life, commoning is an intersection of thesecontracts and the restrictions and policies that prohibit and allow them to occur within municipalities. Via aplatform-based project entitled Public Parts (2020), this article will also present positions on the reification ofthe common through a set of design methodologies and implementations of automation. This platform seeksto subvert typical platform models to decrease ownership, increase access, and produce a new form ofcommunal autonomous life amongst individuals that constitute the rapidly expanding freelance, work fromhome, and gig economies. Furthermore, this text investigates the consequences of merging domestic spacewith artificial intelligence by implementing machine learning to reconfigure spaces and program. Theproblems that arise from the deployment of machine learning algorithms involve issues of collection, usage,and ownership of data. Through the physical design of space, and a central AI which manages the platform andthe automated management of space, the core objective of Public Parts is to reify the common througharchitecture and collectively owned data.
keywords	Common, housing, platforms, reification, artificial intelligence, automation
series	journal
email
last changed	2024/04/17 14:29

_id	ecaade2020_445
id	ecaade2020_445
authors	Spiegelhalter, Thomas, Andia, Alfredo, Levente, Juhasz and Namuduri, Srikanth
year	2020
title	Part 1: The Integrated Decision Support System - Generative and synthetic biological design imaginations for the Miami bay area
doi	https://doi.org/10.52842/conf.ecaade.2020.2.011
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. 11-20
summary	In less than 150 years our carbon society transformed the planet. Today more than 50% of ecologies in the world are determined by unsustainable industrialization processes. The latest IPCC reports show that we are quickly arriving at points of no return in the warming of our planet. We cannot afford to continue in the same direction, we need a new imagination. As part of an E.U.-US funded $1.9 million research project we have been working on multiple projects for the future of the Miami islands since 2018:1. We developed a generative GIS-BIM based Python API for mapping and optimization of carbon-neutral design workflows. It includes genetic design combinatorics with intuitive graphical Dynamo-Python-Grasshopper programming with experimental design results. 2. We worked on a series of design research for the Miami Bay that envisions islands, living shorelines, programmable soils, and infrastructures that grow by themselves using synthetic biology.
keywords	Automated Workflows, Synthetic Biology, Artificial Intelligence, Architecture, Sea-level Rise
series	eCAADe
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
doi	https://doi.org/10.52842/conf.caadria.2020.1.783
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
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_015
id	ecaade2020_015
authors	Yazici, Sevil
year	2020
title	A machine-learning model driven by geometry, material and structural performance data in architectural design process
doi	https://doi.org/10.52842/conf.ecaade.2020.1.411
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. 411-418
summary	Artificial Intelligence (AI), based on interpretation of data, influences various professions including architectural design today. Although research on integrating conceptual design with Machine Learning (ML) algorithms as a subset of the AI has been investigated previously, there is not a framework towards integration of architectural geometry with material properties and structural performance data towards decision making in the early-design phase. Undertaking performance simulations require significant amount of computation power and time. The aim of this research is to integrate ML algorithms into design process to achieve time efficiency and improve design results. The proposed workflow consists of three stages, including generation of the parametric model; running structural performance simulations to collect the data, and operating the ML algorithms, including Artificial Neural Network (ANN), Non-Linear Regression (NLR) and Gaussian Mixture (GM) for undertaking different tasks. The results underlined that the system generates relatively fast solutions with accuracy. Additionally, ML algorithms can assist generative design processes.
keywords	Machine-learning; performance simulation; data-driven design; early-design phase
series	eCAADe
email
last changed	2022/06/07 07:57

_id	cdrf2019_169
id	cdrf2019_169
authors	Yubo Liu, Yihua Luo, Qiaoming Deng, and Xuanxing Zhou
year	2020
title	Exploration of Campus Layout Based on Generative Adversarial Network Discussing the Significance of Small Amount Sample Learning for Architecture
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_16
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	This paper aims to explore the idea and method of using deep learning with a small amount sample to realize campus layout generation. From the perspective of the architect, we construct two small amount sample campus layout data sets through artificial screening with the preference of the specific architects. These data sets are used to train the ability of Pix2Pix model to automatically generate the campus layout under the condition of the given campus boundary and surrounding roads. Through the analysis of the experimental results, this paper finds that under the premise of effective screening of the collected samples, even using a small amount sample data set for deep learning can achieve a good result.
series	cdrf
email
last changed	2022/09/29 07:51

_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
doi	https://doi.org/10.52842/conf.caadria.2020.2.707
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
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	ecaade2020_139
id	ecaade2020_139
authors	Zwierzycki, Mateusz
year	2020
title	On AI Adoption Issues in Architectural Design - Identifying the issues based on an extensive literature review.
doi	https://doi.org/10.52842/conf.ecaade.2020.1.515
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. 515-524
summary	An analysis of AI in design literature, compiled from almost 200 publications from the 1980s onwards. The majority of the sources are proceedings from various conferences. This work is inspired by the Ten Problems for AI in Design (Gero 1991) workshop report, which listed the problems to be tackled in design with AI. Almost 30 years since the publication, it seems most of the Ten Problems cannot be considered solved or even addressed. One of this paper's goals is to identify, categorize and examine the bottlenecks in the adoption of AI in design. The collected papers were analysed to obtain the following data: Problem, Tool, Solution, Stage and Future work. The conclusions drawn from the analysis are used to define a range of existing problems with AI adoption, further illustrated with an update to the Ten Problems. Ideally this paper will spark a discussion on the quality of research, methodology and continuity in research.
keywords	artificial intelligence; review; design automation; knowledge representation; machine learning; expert system
series	eCAADe
email
last changed	2022/06/07 07:57

_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
doi	https://doi.org/10.52842/conf.caadria.2020.1.485
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
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

_id	acadia20_228
id	acadia20_228
authors	Alawadhi, Mohammad; Yan, Wei
year	2020
title	BIM Hyperreality
doi	https://doi.org/10.52842/conf.acadia.2020.1.228
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 228-236.
summary	Deep learning is expected to offer new opportunities and a new paradigm for the field of architecture. One such opportunity is teaching neural networks to visually understand architectural elements from the built environment. However, the availability of large training datasets is one of the biggest limitations of neural networks. Also, the vast majority of training data for visual recognition tasks is annotated by humans. In order to resolve this bottleneck, we present a concept of a hybrid system—using both building information modeling (BIM) and hyperrealistic (photorealistic) rendering—to synthesize datasets for training a neural network for building object recognition in photos. For generating our training dataset, BIMrAI, we used an existing BIM model and a corresponding photorealistically rendered model of the same building. We created methods for using renderings to train a deep learning model, trained a generative adversarial network (GAN) model using these methods, and tested the output model on real-world photos. For the specific case study presented in this paper, our results show that a neural network trained with synthetic data (i.e., photorealistic renderings and BIM-based semantic labels) can be used to identify building objects from photos without using photos in the training data. Future work can enhance the presented methods using available BIM models and renderings for more generalized mapping and description of photographed built environments.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2020_093
id	caadria2020_093
authors	Cerovsek, Tomo and Martens, Bob
year	2020
title	The Evolution of CAADRIA Conferences - A Bibliometric Approach
doi	https://doi.org/10.52842/conf.caadria.2020.1.325
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. 325-334
summary	This paper presents an analysis of the output, impact, use and content of 1,860 papers that were published in the CAADRIA conference proceedings over the last 20+ years (from 1996 to 2019). The applied methodology is a blend of bibliometrics, webometrics and clustering with text mining. The bibliometric analysis leads to quantitative and qualitative results on three levels: (1) author, (2) article and (3) association. The most productive authors authored over 50 papers, and the top 20% authors have over 80 % of all citations generated by CAADRIA proceedings. The overall impact of CAADRIA may be characterised by nearly 2,000 known citations and by the h-index that is 17. The webometrics based on CumInCAD.org reveals that the CAADRIA papers served over 200 k users, which is a considerable visibility for scientific CAAD output. The keywords most frequently used by authors were digital fabrication, BIM and parametric, generative, computational design. Notably, 90% of the papers' descriptors are 2-grams. This study may be useful to researchers, educators and publishers interested in CAAD.
keywords	bibliometrics; open source; text clustering; n-gram
series	CAADRIA
email
last changed	2022/06/07 07:55

For more results click below: