Cumincad : CUMINCAD Papers : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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_id	caadria2021_196
id	caadria2021_196
authors	Lu, Yueheng, Tian, Runjia, Li, Ao, Wang, Xiaoshi and Jose Luis, Garcia del Castillo Lopez
year	2021
title	CubiGraph5K - Organizational Graph Generation for Structured Architectural Floor Plan Dataset
doi	https://doi.org/10.52842/conf.caadria.2021.1.081
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. 81-90
summary	In this paper, a novel synthetic workflow is presented for procedural generation of room relation graphs of floor plans from structured architectural datasets. Different from classical floor plan generation models, which are based on strong heuristics or low-level pixel operations, our method relies on parsing vectorized floor plans to generate their intended organizational graph for further graph-based deep learning. This research work presents the schema for the organizational graphs, describes the generation algorithms, and analyzes its time/space complexity. As a demonstration, a new dataset called CubiGraph5K is presented. This dataset is a collection of graph representations generated by the proposed algorithms, using the floor plans in the popular CubiCasa5K dataset as inputs. The aim of this contribution is to provide a matching dataset that could be used to train neural networks on enhanced floor plan parsing, analysis and generation in future research.
keywords	Graph Theory; Algorithm; Architecture Design Dataset; Organizational Graph
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	ecaade2021_203
id	ecaade2021_203
authors	Arora, Hardik, Bielski, Jessica, Eisenstadt, Viktor, Langenhan, Christoph, Ziegler, Christoph, Althoff, Klaus-Dieter and Dengel, Andreas
year	2021
title	Consistency Checker - An automatic constraint-based evaluator for housing spatial configurations
doi	https://doi.org/10.52842/conf.ecaade.2021.2.351
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 2, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 351-358
summary	The gradual rise of artificial intelligence (AI) and its increasing visibility among many research disciplines affected Computer-Aided Architectural Design (CAAD). Architectural deep learning (DL) approaches are being developed and published on a regular basis, such as retrieval (Sharma et al. 2017) or design style manipulation (Newton 2019; Silvestre et al. 2016). However, there seems to be no method to evaluate highly constrained spatial configurations for specific architectural domains (such as housing or office buildings) based on basic architectural principles and everyday practices. This paper introduces an automatic constraint-based consistency checker to evaluate the coherency of semantic spatial configurations of housing construction using a small set of design principles to evaluate our DL approaches. The consistency checker informs about the overall performance of a spatial configuration followed by whether it is open/closed and the constraints it didn't satisfy. This paper deals with the relation of spaces processed as mathematically formalized graphs contrary to existing model checking software like Solibri.
keywords	model checking, building information modeling, deep learning, data quality
series	eCAADe
email
last changed	2022/06/07 07:54

_id	ijac202119106
id	ijac202119106
authors	Del Campo, Matias; Alexandra Carlson, and Sandra Manninger
year	2021
title	Towards Hallucinating Machines - Designing with Computational Vision
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 1, 88–103
summary	There are particular similarities in how machines learn about the nature of their environment, and how humans learn to process visual stimuli. Machine Learning (ML), more specifically Deep Neural network algorithms rely on expansive image databases and various training methods (supervised, unsupervised) to “make sense” out of the content of an image. Take for example how students of architecture learn to differentiate various architectural styles. Whether this be to differentiate between Gothic, Baroque or Modern Architecture, students are exposed to hundreds, or even thousands of images of the respective styles, while being trained by faculty to be able to differentiate between those styles. A reversal of the process, striving to produce imagery, instead of reading it and understanding its content, allows machine vision techniques to be utilized as a design methodology that profoundly interrogates aspects of agency and authorship in the presence of Artificial Intelligence in architecture design. This notion forms part of a larger conversation on the nature of human ingenuity operating within a posthuman design ecology. The inherent ability of Neural Networks to process large databases opens up the opportunity to sift through the enormous repositories of imagery generated by the architecture discipline through the ages in order to find novel and bespoke solutions to architectural problems. This article strives to demystify the romantic idea of individual artistic design choices in architecture by providing a glimpse under the hood of the inner workings of Neural Network processes, and thus the extent of their ability to inform architectural design.The approach takes cues from the language and methods employed by experts in Deep Learning such as Hallucinations, Dreaming, Style Transfer and Vision. The presented approach is the base for an in-depth exploration of its meaning as a cultural technique within the discipline. Culture in the extent of this article pertains to ideas such as the differentiation between symbolic and material cultures, in which symbols are defined as the common denominator of a specific group of people.1 The understanding and exchange of symbolic values is inherently connected to language and code, which ultimately form the ingrained texture of any form of coded environment, including the coded structure of Neural Networks.A first proof of concept project was devised by the authors in the form of the Robot Garden. What makes the Robot Garden a distinctively novel project is the motion from a purely two dimensional approach to designing with the aid of Neural Networks, to the exploration of 2D to 3D Neural Style Transfer methods in the design process.
keywords	Artificial intelligence, design agency, neural networks, machine learning, machine vision
series	journal
email
last changed	2021/06/03 23:29

_id	caadria2021_130
id	caadria2021_130
authors	Han, Yoojin and Lee, Hyunsoo
year	2021
title	Exploring the Key Attributes of Lifestyle Hotels: A Content Analysis of User-Created Content on Instagram
doi	https://doi.org/10.52842/conf.caadria.2021.1.071
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. 71-80
summary	This study aims to investigate the key attributes of lifestyle hotels by analyzing user-created content on Instagram, an image-based social network service. In an era of uncertainty in the tourism and hospitality industry, it is inevitable that hotels must create a competitive identity. However, even with the significant growth of the lifestyle hotel segment, the concept of a lifestyle hotel is still vague. Therefore, to explore how to define, perceive, and interpret lifestyle hotels and to suggest their crucial attributes, this paper examines user-created content on Instagram. The data from 20,886 Instagram posts related to lifestyle hotels, including 2,209 locations, 43,586 hashtags, and 20,866 images, were analyzed using Vision AI, a social network analysis method and computer vision technology. The results of this study demonstrated that lifestyle hotels are perceived as design-focused branded hotels that represent the urban lifestyle and share both vacation and urban activities. Furthermore, the results reflected one of the latest hospitality trends-a holiday in an urban setting in addition to the primary purpose of traveling. Finally, this research suggests broader uses of big data and deep learning for analyzing how a place is consumed in a geospatial context.
keywords	Lifestyle Hotel; Hospitality Experiences; User-Created Content; Social Network Analysis; Vision AI
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	caadria2021_166
id	caadria2021_166
authors	Hu, Wei
year	2021
title	The experiment of neural network on the cognition of style
doi	https://doi.org/10.52842/conf.caadria.2021.2.061
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 61-70
summary	This paper introduces a method to obtain quantified style description vector which is for computer analysis input by using image style classification task. In the experiment, 3331 architectural photos of three styles obtained by crawling and filtering were used as training data. A deep convolutional neural network was trained to map architectural images to high-dimensional feature space, and then the high-dimensional style description vector was used to output the measurement results of style cognition with fully connected neural network. Tested by test data-set of 371 architectural pictures, the accuracy rate of style cognition reached more than 80%. The neural network using architectural data training was applied to the style cognition of non-architectural objects, high accuracy rate was also achieved, it proved that this quantified style description vector did include the information about style cognition to some extent instead of simply classification. Finally, the similarities and differences between the cognitive characteristics of style of neural network and human beings are investigated.
keywords	deep neural network; style cognition experiment; eye tracker
series	CAADRIA
email
last changed	2022/06/07 07:50

_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	caadria2021_052
id	caadria2021_052
authors	Yousif, Shermeen and Bolojan, Daniel
year	2021
title	Deep-Performance - Incorporating Deep Learning for Automating Building Performance Simulation in Generative Systems
doi	https://doi.org/10.52842/conf.caadria.2021.1.151
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. 151-160
summary	In this study, we introduce a newly developed method called Deep-Performance, to enable automatic environmental performance simulation prediction without the need to perform simulations, by integrating deep learning strategies. The aim is to train neural networks on datasets with thousands of building design samples and their corresponding performance simulation. The trained model would offer performance prediction for design options emerging in generative protocols. The research is a work-in-progress within a broader project aimed at automating buildings environmental performance evaluations of daylight analysis and energy simulation, using deep learning (DL) models. This paper focuses on the implementation of a supervised DL method for automating the retrieval of daylight analysis metrics, targeting successful daylight design and higher building enclosure efficiency. We have further improved a Pix2Pix model trained on 5 different datasets, each containing 6000 paired images of architectural floor plans and their daylight simulation metrics. In the inference phase, the model was able to accurately predict the daylight simulation for unseen sets of floor plans. For validation, two quantitative assessment metrics were followed to assess the predicted daylight performance against the daylight performance simulation. Both assessment metrics showed high accuracy levels.
keywords	Deep Learning; Artificial Intelligence; Deep-Performance; Automating Building Performance Simulation; Generative Systems
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	caadria2021_160
id	caadria2021_160
authors	Ding, Jie and Xiang, Ke
year	2021
title	The influence of spatial geometric parameters of Glazed-atrium on office building energy consumption in the hot summer-warm winter region of China
doi	https://doi.org/10.52842/conf.caadria.2021.1.391
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. 391-400
summary	To investigate the influence of the spatial geometric parameters of glazed-atrium on building energy consumption, this study established a prototypical office building model in the hot summer-warm winter region in China, and simulated the effect of energy consumption of six selected factors based on orthogonal experimental design (OED). Through the statistical analysis, the results showed that the floor height and the skylight-roof ratio were the most important parameters affecting the total energy consumption, with the contribution rates of 55.5% and 18.2%, followed by the section shape parameter and the plane orientation. In addition, the floor height and the section shape parameter were closely related to the cooling load and the lighting load, respectively, and both energy consumption could be reduced to a lower degree when the atrium inner interface window-wall ratio was 60%. Finally, the optimized parameter combination and energy-saving design strategies were proposed. This study provides architects with a simplified energy evaluation of atrium spatial geometric parameters in the early design stage, and it has an important guiding significance for the sustainable development of office buildings in the future.
keywords	Energy consumption; Spatial geometric factors; Glazed atrium; Office building; Hot summerâ€“warm winter region
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	caadria2021_137
id	caadria2021_137
authors	Fattahi Tabasi, Saba, Alaghmandan, Matin and Rafizadeh, Hamid Reza
year	2021
title	Simultaneous effect of form modifications and topology of the bracing system on the structural performance of timber high rise building - Introducing an innovative approach using parametric design
doi	https://doi.org/10.52842/conf.caadria.2021.1.421
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. 421-430
summary	Topology optimization is a tool that minimizes the material consumption in a structure, while at the same time provides us design alternatives integrating architectural and structural engineering concepts. However, topology optimization is a structural engineering subject and its known methods are required professional knowledge of engineering to be used. In this article, the mutual effect of form modifications and topology of the bracing system in a 9-story timber exoskeleton high-rise building regarding the governing wind load and seismic load is examined. What differentiates this study from former ones and in fact its main purpose is introducing an innovative approach towards structural topology optimization using parametric design. In this innovative approach, the possibility of moving for each central node of bracing systems in defined ranges independently and the possibility of the existence or absence of each bracing member is provided. This parametric model will enable architects to optimize the topology of the structural elements which are part of their architectural design by themselves. The CMA-ES-algorithm-based optimization is done to minimize both total mass of structure per unit area and the horizontal displacement of the top floor. For modeling, optimizing cross-sections and structural analysis, Grasshopper and its plug-in called Karamba are utilized.
keywords	Topology optimization; Form finding; Parametric design; Timber tall buildings; Exoskeleton structures
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	ecaade2021_121
id	ecaade2021_121
authors	Mondal, Joy
year	2021
title	Eelish 2.0: Grasshopper Plugin for Automated Grid-Driven Column-Beam Placement on Orthogonal Floor Plans - Formalising manual workflow into an algorithm through empirical analysis
doi	https://doi.org/10.52842/conf.ecaade.2021.1.427
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 427-436
summary	The implicitly parametric if-else logic of determining column-beam locations is applied manually on virtually every orthogonal design, thereby inflating workhours and cost through unnecessary repetition of labour. This paper presents the development of a generative algorithm (developed as Grasshopper plugin Eelish 2.0) that automates the placement of column centre points and beam centre lines on orthogonal floor plans. The manual process of column-beam placement is formalised as the algorithm through empirical analysis of layouts drawn by architects. The placement is executed iteratively from the largest room to the smallest room. It is guided by local and/or global orthogonal grids that are generated using walls of other rooms and beams of rooms calculated till the previous iteration. The placements are controlled by the maximum and minimum allowed spans of slabs, and four modes of grid generations. The generated layouts have a qualitative 'Satisfactory' or better approval rating of 82.4% by architects and 88.4% by structural engineers.
keywords	Column; Beam; Orthogonal; Floor Plan; Automated; Grid
series	eCAADe
email
last changed	2022/06/07 07:58

_id	caadria2021_354
id	caadria2021_354
authors	Huang, Chenyu, Gong, Pixin, Ding, Rui, Qu, Shuyu and Yang, Xin
year	2021
title	Comprehensive analysis of the vitality of urban central activities zone based on multi-source data - Case studies of Lujiazui and other sub-districts in Shanghai CAZ
doi	https://doi.org/10.52842/conf.caadria.2021.2.549
source	A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 2, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 549-558
summary	With the use of the concept Central Activities Zone in the Shanghai City Master Plan (2017-2035) to replace the traditional concept of Central Business District, core areas such as Shanghai Lujiazui will be given more connotations in the future construction and development. In the context of todays continuous urbanization and high-speed capital flow, how to identify the development status and vitality characteristics is a prerequisite for creating a high-quality Central Activities Zone. Taking Shanghai Lujiazui sub-district etc. as an example, the vitality value of weekday and weekend as well as 19 indexes including density of functional facilities and building morphology is quantified by obtaining multi-source big data. Meanwhile, the correlation between various indexes and the vitality characteristics of the Central Activities Zone are tried to summarize in this paper. Finally, a neural network regression model is built to bridge the design scheme and vitality values to realize the prediction of the vitality of the Central Activities Zone. The data analysis method proposed in this paper is versatile and efficient, and can be well integrated into the urban big data platform and the City Information Modeling, and provides reliable reference suggestions for the real-time evaluation of future urban construction.
keywords	multi-source big data; Central Activities Zone; Vitality; Lujiazui
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	cdrf2021_139
id	cdrf2021_139
authors	Shicong Cao1 and Hao Zheng
year	2021
title	A POI-Based Machine Learning Method for Predicting Residents’ Health Status
doi	https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_13
source	Proceedings of the 2021 DigitalFUTURES The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021)
summary	Health environment is a key factor in public health. Since people’s health depends largely on their lifestyle, the built environment which supports a healthy living style is becoming more important. With the right urban planning decisions, it’s possible to encourage healthier living and save healthcare expenditures for the society. However, there is not yet a quantitative relationship established between urban planning decisions and the health status of the residents. With the abundance of data and computing resources, this research aims to explore this relationship with a machine learning method. The data source is from both the OpenStreetMap and American Center for Decease Control and Prevention (CDC). By modeling the Point of Interest data and the geographic distribution of health-related outcome, the research explores the key factors in urban planning that could influence the health status of the residents quantitatively. It informs how to create a built environment that supports health and opens up possibilities for other data-driven methods in this field.
series	cdrf
email
last changed	2022/09/29 07:53

_id	ascaad2021_050
id	ascaad2021_050
authors	Zamani, Erfan; Theodoros Dounas
year	2021
title	Parametric Iranian-Islamic Muqarnas as Drivers for Design for Fabrication and Assembly via UAVs: Parametric Analysis and Synthesis of Iranian-Islamic Muqarnas
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 436-449
summary	This study proposes a DfMA (from design to assembly) based on Unmanned Aerial Vehicle (UAV) and uses Iranian-Islamic Muqarnas as the case study due to their geometric modularity. In Islamic architecture, different geographic regions are known to have used various design and construction methods of Muqarnas. There are four main specifications of the Muqarnas that define to which category they belong; first, its three-dimensional shape, that provides volume. Secondly, the size of its modules is variable. Third, its specific generative algorithm. And finally, the 2-dimensional pattern plan that is used in the design. First, this study presents thus a global analytical study that drives a generative system to construct Muqarnas, through a careful balance of four specifications. In this second step, the paper reports the result of using a parametric tool, Grasshopper and parametric plugins, for creating a generative system of several types of Muqarnas. This synthetic translation aims at expanding our understanding of parametric analysis and synthesis of traditional architecture, advancing our understanding towards using parametric synthesis towards UAV-based fabrication of Muqarnas, by taking advantage of their inherent repetition and recursion.
series	ASCAAD
email
last changed	2021/08/09 13:11

_id	ascaad2021_150
id	ascaad2021_150
authors	Fathima, Linas; Chithra K
year	2021
title	Shapegrammar: A Tool for Research in Traditional Architecture
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 465-478
summary	Every Architectural style consists of an Architectural language with vocabulary, syntax, and semantics. The compositional principles of a particular style can be defined over as a set of rules. These rules can be reformed and converted using mathematical computational techniques using Shape Grammar (A systematic method used for interpreting spatial design and activities). Researchers across the world used shape grammar to analyse design patterns of traditional architectural styles, master architects' works, etc. These rule-based methods can be adopted into computer languages to produce new designs. Traditional Architecture of a region portrays culture integrated with all aspects of human life. The proposed paper is to study the potentials of shape grammar to use as a tool in the research of traditional architectural styles by analysing case studies. The research methodology reviews the previous shape grammar studies conducted in various conventional styles and comparative analysis of the approaches of authors in shape grammar generation. The research by Lambe and Dongre on the formulation of shape grammar of Pol houses of Ahmadabad and Cagdas's work on traditional Turkish houses is an example of this. T Knight had formulated shape grammar of Japanese tea houses, and Yousefniapasha and Teeling developed a grammar of vernacular houses facing rice fields of Mazandaran, Iran. Similarly, many researchers used shape grammars as a tool to analyse traditional architecture. So the study will compare the different traditional shape grammar generations and formulate a sample shape grammar of a traditional prototype to conclude the scope of further research in the domain.
series	ASCAAD
email
last changed	2021/08/09 13:13

_id	sigradi2021_274
id	sigradi2021_274
authors	Symeonidou, Ioanna and Papapanagiotou, Panagiotis
year	2021
title	From (Flat) Drawings to the (Ultra) Real: A Taxonomy of Architectural Visualizations
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. 843–854
summary	Architectural visualizations play an important role in transmitting architectural ideas. The research presented in this paper focuses on digital images and investigates the different types of architectural representations and visualizations in relation to the architectural/designerly workflow. The study of a series architectural images reveals that based on the stylistic characteristics they tend to coalesce into categories, ranging from (flat) drawings to (ultra) real architectural visualizations. The research aims to identify different types of architectural visualizations, question whether there are links between the style and the design tools and software used and decipher the digital workflow employed for their creation. Taking into consideration the findings, a pilot 3D scene is selected as a case study to test the hypothesis of replicating different visualization styles, using a corresponding workflow and methodology, in order to compare the results and reach conclusions about the features, characteristics and workflow.
keywords	architectural visualizations, renderings, digital workflows, visualization styles, photorealistic representations
series	SIGraDi
email
last changed	2022/05/23 12:11

_id	sigradi2021_114
id	sigradi2021_114
authors	Cesar Rodrigues, Ricardo, Kenzo Imagawa, Marcelo, Rubio Koga, Renan and Bertola Duarte, Rovenir
year	2021
title	Big Data vs Smart Data on the Generation of Floor Plans with Deep Learning
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. 217–228
summary	Due to the progressive growth of data dimensionality, addressing how much data and time is required to train deep learning models has become an important research topic. Thus, in this paper, we present a benchmark for generating floor plans with Conditional Generative Adversarial Networks in which we compare 10 trained models on a dataset of 80.000 samples, the models use different data dimensions and hyper-parameters on the training phase, beyond this objective, we also tested the capability of Convolutional Neural Networks (CNN) to reduce the dataset noise. The models' assessment was made on more than 6 million with the Frétche Inception Distance (FID). The results show that such models can rapidly achieve similar or even better FID results if trained with 800 images of 512x512 pixels, in comparison to high dimensional datasets of 256x256 pixels, however, using CNNs to enhance data consistency reproduced optimal results using around 27.000 images.
keywords	Floor plans, Generative design, Generative adversarial networks, Smart Data, Dataset reduction.
series	SIGraDi
email
last changed	2022/05/23 12:10

_id	caadria2021_039
id	caadria2021_039
authors	Chen, Jielin, Stouffs, Rudi and Biljecki, Filip
year	2021
title	Hierarchical (multi-label) architectural image recognition and classification
doi	https://doi.org/10.52842/conf.caadria.2021.1.161
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. 161-170
summary	The task of architectural image recognition for both architectural functionality and style remains an open challenge. In addition, the paucity of well-organized, large-scale architectural image datasets with specific consideration for the domain of architectural design research has hindered the exploration of these challenging tasks. Drawing upon images from the professional architectural website ArchdailyÂ®, and leveraging state-of-the-art deep-learning-based classification models, we explore a hierarchical multi-label classification model as a potential baseline for the task of architectural image classification. The resulting model showcases the potential for innovative architectural discipline-related analyses and demonstrates some heuristic insights for visual feature extraction pertaining to both architectural functionality and architectural style.
keywords	image recognition; hierarchical classification; multi-label classification; architectural functionality; style
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	caadria2021_086
id	caadria2021_086
authors	Eisenstadt, Viktor, Arora, Hardik, Ziegler, Christoph, Bielski, Jessica, Langenhan, Christoph, Althoff, Klaus-Dieter and Dengel, Andreas
year	2021
title	Exploring optimal ways to represent topological and spatial features of building designs in deep learning methods and applications for architecture
doi	https://doi.org/10.52842/conf.caadria.2021.1.191
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. 191-200
summary	The main aim of this research is to harness deep learning techniques to support architectural design problems in early design phases, for example, to enable auto-completion of unfinished designs. For this purpose, we investigate the possibilities offered by established deep learning libraries such as TensorFlow. In this paper, we address a core challenge that arises, namely the transformation of semantic building information into a tensor format that can be processed by the libraries. Specifically, we address the representation of information about room types of a building and type of connection between the respective rooms. We develop and discuss five formats. Results of an initial evaluation based on a classification task show that all formats are suitable for training deep learning networks. However, a clear winner could be determined as well, for which a maximum value of 98% for validation accuracy could be achieved.
keywords	deep learning; spatial configuration; data representation; semantic building fingerprint
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	ecaade2021_254
id	ecaade2021_254
authors	Eisenstadt, Viktor, Arora, Hardik, Ziegler, Christoph, Bielski, Jessica, Langenhan, Christoph, Althoff, Klaus-Dieter and Dengel, Andreas
year	2021
title	Comparative Evaluation of Tensor-based Data Representations for Deep Learning Methods in Architecture
doi	https://doi.org/10.52842/conf.ecaade.2021.1.045
source	Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 45-54
summary	This paper presents an extended evaluation of tensor-based representations of graph-based architectural room configurations. This experiment is a continuation of examination of recognition of semantic architectural features by contemporary standard deep learning methods. The main aim of this evaluation is to investigate how the deep learning models trained using the relation tensors as data representation means perform on data not available in the training dataset. Using a straightforward classification task, stepwise modifications of the original training dataset and manually created spatial configurations were fed into the models to measure their prediction quality. We hypothesized that the modifications that influence the class label will not decrease this quality, however, this was not confirmed and most likely the latent non-class defining features make up the class for the model. Under specific circumstances, the prediction quality still remained high for the winning relation tensor type.
keywords	Deep Learning; Spatial Configuration; Semantic Building Fingerprint
series	eCAADe
email
last changed	2022/06/07 07:55

_id	ascaad2021_062
id	ascaad2021_062
authors	Elgobashi, Aya; Yasmeen El Semary
year	2021
title	Redefinition of Heritage Public Spaces Using PPGIS: The Case of Religious Complex in Old Cairo
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 355-370
summary	Plenty of challenges all over the world are affecting the urban development of spaces in the cities, especially those of heritage sites; these urban spaces provide various ambiances that appeal to the senses. Although surrounded open spaces in heritage sites are full of rich, deep knowledge that plays an active role in the community perceptions, it has been recently neglected. A contribution is paid to the combination of digital technologies to help in preserving those spaces. Its integrated use could exponentially increase the effectiveness of conservation strategies of ancient buildings. GIS technology became a usual documentation tool for heritage managers, conservators, restorers, architects, archaeologists, painters, and all other categories of experts involved in cultural heritage activities. Consequently, the GIS has faced strong criticism as it is a tool for documentation without engaging in the public environment and the users’ needs; as a result, GIS cannot help in any enhancing process as it does not have any idea about the needs of the users. This paper analyses public uses efficiency in heritage public spaces in Cairene context using public participation geographic information system (PPGIS) methodology, as it gives attention to the term “user” to include the “public” incorporating the concept of “public participation” commonly used in planning. An online survey was set up, based on Google Maps, where respondents were asked to place and rate twenty-five items on an interactive map done by (ARCGIS 10.4). These items were based on the criteria of placemaking to make those spaces full of creative ambiance to be more attractive and useful to the communities. Finally, 200 valid surveys have been collected and mapped 1500 opinions have been mapped. The Results of this research show that PPGIS is an effective tool in measuring the efficiency of those heritage public spaces, which may be valuable for future planning.
series	ASCAAD
email
last changed	2021/08/09 13:13

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