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_161
id	caadria2021_161
authors	Zhao, Xin, Han, Yunsong and Shen, Linhai
year	2021
title	Multi-objective Optimisation of a Free-form Building Shape to improve the Solar Energy Utilisation Potential using Artificial Neural Networks
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. 221-230
doi	https://doi.org/10.52842/conf.caadria.2021.1.221
summary	Optimisation of free-form building design is more challenging in terms of building information modelling and performance evaluation compared to conventional buildings. The paper provides a Photogrammetry-based BIM Modelling - Machine Learning Modelling - Multi-objective Optimisation framework to improve the solar energy utilisation potential of free-form buildings. Low altitude photogrammetry is used to collect the building and site environmental information. An ANN prediction model is developed using the control point coordinates and simulation data. Through parametric programming, the multi-objective algorithm is coupled with the ANN model to obtain the trade-off optimal building form. The results show that the maximum solar radiation value in winter can increase by 30.60% and the minimum solar radiation in summer can decrease by 13.99%. It is also shown that the integration of ANN modelling and photogrammetry-based BIM modelling into the multi-objective optimisation method can accelerate the optimisation process.
keywords	Multi-objective optimisation; Artificial neural network; Free-form shape building ; Solar energy utilisation
series	CAADRIA
email
last changed	2022/06/07 07:57

_id	caadria2021_191
id	caadria2021_191
authors	Shou, Xinyue, Chen, Pinyang and Zheng, Hao
year	2021
title	Predicting the Heat Map of Street Vendors from Pedestrian Flow through Machine Learning
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. 569-578
doi	https://doi.org/10.52842/conf.caadria.2021.2.569
summary	Street vending is a recent policy advocated by city governments to support small and intermediate businesses in the post-pandemic period in China. Street vendors select their locations primarily based on their intuitions about the surrounding environment; they temporarily occupy popular locations that benefit their business. Taking the city of Chengdu as an example, this study aims to formulate the rules governing vendors location selection using machine learning and big data analysis techniques, thus identifying streets likely to become vital street markets. We propose a semantic segmentation method to construct heat maps that visualize and quantify the distribution of street vendors and pedestrians on public urban streets. The image-based generative adversarial network (GAN) is then trained to predict the vendors heat maps from the pedestrians heat map, finding the relationship between the locations of the vendors and the pedestrians. Our successful prediction of the vendors locations highlights machine learning techniques ability to quantify experience-based decision strategies. Moreover, suggesting potential marketing locations to vendors could help increase cities vitality.
keywords	Machine Learning; Big Data Analysis; Semantic Segmentation; Generative Adversarial Networks
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	cdrf2021_242
id	cdrf2021_242
authors	Waishan Qiu , Wenjing Li, Xun Liu, and Xiaokai Huang
year	2021
title	Subjectively Measured Streetscape Qualities for Shanghai with Large-Scale Application of Computer Vision and Machine Learning
source	Proceedings of the 2021 DigitalFUTURES The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021)
doi	https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_23
summary	Recently, many new studies emerged to apply computer vision (CV) to street view imagery (SVI) dataset to objectively extract the view indices of various streetscape features such as trees to proxy urban scene qualities. However, human perceptions (e.g., imageability) have a subtle relationship to visual elements which cannot be fully captured using view indices. Conversely, subjective measures using survey and interview data explain more human behaviors. However, the effectiveness of integrating subjective measures with SVI dataset has been less discussed. To address this, we integrated crowdsourcing, CV, and machine learning (ML) to subjectively measure four important perceptions suggested by classical urban design theory. We first collected experts’ rating on sample SVIs regarding the four qualities which became the training labels. CV segmentation was applied to SVI samples extracting streetscape view indices as the explanatory variables. We then trained ML models and achieved high accuracy in predicting the scores. We found a strong correlation between predicted complexity score and the density of urban amenities and services Point of Interests (POI), which validates the effectiveness of subjective measures. In addition, to test the generalizability of the proposed framework as well as to inform urban renewal strategies, we compared the measured qualities in Pudong to other five renowned urban cores worldwide. Rather than predicting perceptual scores directly from generic image features using convolution neural network, our approach follows what urban design theory suggested and confirms various streetscape features affecting multi-dimensional human perceptions. Therefore, its result provides more interpretable and actionable implications for policymakers and city planners.
series	cdrf
last changed	2022/09/29 07:53

_id	ecaade2023_259
id	ecaade2023_259
authors	Sonne-Frederiksen, Povl Filip, Larsen, Niels Martin and Buthke, Jan
year	2023
title	Point Cloud Segmentation for Building Reuse - Construction of digital twins in early phase building reuse projects
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 327–336
doi	https://doi.org/10.52842/conf.ecaade.2023.2.327
summary	Point cloud processing has come a long way in the past years. Advances in computer vision (CV) and machine learning (ML) have enabled its automated recognition and processing. However, few of those developments have made it through to the Architecture, Engineering and Construction (AEC) industry. Here, optimizing those workflows can reduce time spent on early-phase projects, which otherwise could be spent on developing innovative design solutions. Simplifying the processing of building point cloud scans makes it more accessible and therefore, usable for design, planning and decision-making. Furthermore, automated processing can also ensure that point clouds are processed consistently and accurately, reducing the potential for human error. This work is part of a larger effort to optimize early-phase design processes to promote the reuse of vacant buildings. It focuses on technical solutions to automate the reconstruction of point clouds into a digital twin as a simplified solid 3D element model. In this paper, various ML approaches, among others KPConv Thomas et al. (2019), ShapeConv Cao et al. (2021) and Mask-RCNN He et al. (2017), are compared in their ability to apply semantic as well as instance segmentation to point clouds. Further it relies on the S3DIS Armeni et al. (2017), NYU v2 Silberman et al. (2012) and Matterport Ramakrishnan et al. (2021) data sets for training. Here, the authors aim to establish a workflow that reduces the effort for users to process their point clouds and obtain object-based models. The findings of this research show that although pure point cloud-based ML models enable a greater degree of flexibility, they incur a high computational cost. We found, that using RGB-D images for classifications and segmentation simplifies the complexity of the ML model but leads to additional requirements for the data set. These can be mitigated in the initial process of capturing the building or by extracting the depth data from the point cloud.
keywords	Point Clouds, Machine Learning, Segmentation, Reuse, Digital Twins
series	eCAADe
email
last changed	2023/12/10 10:49

_id	acadia21_160
id	acadia21_160
authors	Cao, Shicong; Zheng, Hao
year	2021
title	A POI-Based Machine Learning Method in Predicting Health
source	ACADIA 2021: Realignments: Toward Critical Computation [Proceedings of the 41st Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-986-08056-7]. Online and Global. 3-6 November 2021. edited by B. Bogosian, K. Dörfler, B. Farahi, J. Garcia del Castillo y López, J. Grant, V. Noel, S. Parascho, and J. Scott. 160-169.
doi	https://doi.org/10.52842/conf.acadia.2021.160
summary	This research aims to explore the quantitative relationship between urban planning decisions and the health status of residents. By modeling the Point of Interest (POI) data and the geographic distribution of health-related outcomes, the research explores the critical factors in urban planning that could influence the health status of residents. It also informs decision-making regarding a healthier built environment and opens up possibilities for other data-driven methods. The data source constitutes two data sets, the POI data from OpenStreetMap, and the PLACES: Local Data for Better Health dataset from CDC. After the data is collected and joined spatially, a machine learning method is used to select the most critical urban features in predicting the health outcomes of residents. Several machine learning models are trained and compared. With the chosen model, the prediction is evaluated on the test dataset and mapped geographically. The relations between factors are explored and interpreted. Finally, to understand the implications for urban design, the impact of modified POI data on the prediction of residents' health status is calculated and compared. This research proves the possibility of predicting resident's health from urban conditions with machine learning methods. The result verifies existing healthy urban design theories from a different perspective. This approach shows vast potential that data could in future assist decision-making to achieve a healthier built environment.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_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
source	Proceedings of the 2021 DigitalFUTURES The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021)
doi	https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_13
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	cdrf2021_168
id	cdrf2021_168
authors	Hainan Yan1, Yiting Zhang, Sheng Liu, Ka Ming Cheung, and Guohua Ji
year	2021
title	Optimization of Daylight and Thermal Performance of Building Façade: A Case Study of Office Buildings in Nanjing
source	Proceedings of the 2021 DigitalFUTURES The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021)
doi	https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_16
summary	In China’s hot summer and cold winter areas, the façade design of buildings needs to respond to a variety of performance objectives. This study focuses on the optimization of daylight and solar radiation of building façade of office buildings in Nanjing and proposes a simple and efficient method. The method mainly includes a random sampling of design models, simplified operation of daylight performance criteria and selection of optimal solution. The results show that the building façade can improve the indoor lighting uniformity and reduce the indoor illumination level compared with the unshaded reference building. Besides, the amount of solar radiation received by office buildings in summer and winter becomes more balanced with the building façade. The optimization design method of building façade proposed in this study can be of guiding significance for office buildings in Nanjing.
series	cdrf
email
last changed	2022/09/29 07:53

_id	sigradi2021_300
id	sigradi2021_300
authors	Leiro, Manoela, Darzé, Júlia, Rios, Matheus and Lemos, Paulo
year	2021
title	An Experience with the Use of a BIM Tool in the Thermal Environmental Comfort Discipline
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. 889–900
summary	This article presents a didactic experience carried out with the use of a BIM tool in the Thermal Environmental Comfort discipline of the graduate course in Architecture and Urbanism of a private Higher Education Institution in the city of Salvador-Bahia. Starting in 2020, students began designing solar protection devices using a geometric model in Revit. The method described in Annex I of the Technical Regulation on the Quality of Energy Efficiency Level in Residential Buildings (RTQ-R) was applied. The results obtained showed a better understanding by the students about the importance of correctly sizing solar protection devices for different orientations.
keywords	BIM, Ensino, Conforto Ambiental Térmico
series	SIGraDi
email
last changed	2022/05/23 12:11

_id	caadria2022_157
id	caadria2022_157
authors	Liu, Sijie, Wei, Ziru and Wang, Sining
year	2022
title	On-site Holographic Building Construction: A Case Study of Aurora
source	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 405-414
doi	https://doi.org/10.52842/conf.caadria.2022.2.405
summary	Geometrically complex building components‚ reliance on high-touch implementation often results in tedious information reprocessing. Recent use of Mixed Reality (MR) in architectural practices, however, can reduce data translation and potentially increase design-to-build efficiency. This paper uses Aurora, a single-story residential building for 2021 China‚s Solar Decathlon Competition, as a demonstrator to evaluate the performance of on-site holographic building construction. This paper firstly reviews recent studies of MR in architectural design and practice. It then describes an MR-aided construction process of Aurora's non-standard building envelope and rooftop mounting structure, where in-situ holographic registration, human-machine cooperation, and as-built analysis are discussed. This paper concludes by stating that MR technologies provide unskilled implementers with a handy approach to materialise complex designs. The research was guided by the UN Sustainable Development Goals, especially aligning with the GOAL 9 which seeks innovations in industry and infrastructure.
keywords	Mixed Reality, Non-standard Architecture, Low-tech Construction, Solar Decathlon Competition, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	sigradi2021_90
id	sigradi2021_90
authors	Mateus, Daniel, Pinto Duarte, José and Romao, Luís
year	2021
title	Energy-Based Design: A Digital Design System for the Design of Energy-Harvesting Building Envelopes
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. 831–842
summary	The goal of the research described in this paper is to address climate change by promoting the production of renewable energy in building envelopes, which are exposed to solar radiation. It proposes an energy-based design paradigm, where energy processes shape the building form, and a digital design system for building envelopes that considers the trajectory of sunrays. The goal is to create envelopes that are efficient in harvesting solar energy, enabling them to produce the electricity that buildings consume. To operationalize the proposed digital design system, a building envelope grammar is developed and implemented in a software called LIDIA to be used by architects in design process to generate solutions with improved energy production performance. The efficiency of the resulting solutions, the effectiveness of LIDIA and, therefore, the validity of the proposed paradigm, is demonstrated with the design of envelope solutions for single family houses.
keywords	Energy-based design, Architecture-Building envelopes system, Buildings envelope grammar, Shape grammar, LIDIA software.
series	SIGraDi
email
last changed	2022/05/23 12:11

_id	ecaade2021_133
id	ecaade2021_133
authors	Sharp, Alexa, Blay, Georgina, Kholodova, Janna and Correa, David
year	2021
title	An Autonomous Bio-Inspired Shading Façade System based on Plant Movement Principles
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. 463-472
doi	https://doi.org/10.52842/conf.ecaade.2021.2.463
summary	Utilizing existing principles of plant movement, we can design climatic-responsive facades made of hygroscopic materials. This paper investigates the use of a double actuating system to create an architectural façade. Several adaptive façade strategies have been previously developed using wood bilayers, but there has not been significant investigation into the application of multiple actuation points in a single unit. The paper presents a façade that is responsive to the surrounding environment via the kinematic amplification of hygroscopic wood expansion. The kinematic amplification uses the biomechanical principles from both the Water Lily (Nymphaea) and the Purple Shamrock (Oxalis triangularis). Acting as an adaptive shading mechanism, the façade system - arranged using Lindenmayer system principles - can improve occupant comfort by controlling solar radiation . The developed prototypes use climate-responsive wood bilayer actuators. The aesthetic and functional features of the bio-inspired mechanism promote a visual awareness between our built environment and environmental conditions.
keywords	Adaptive Façade; Biomimetics; Plant Movement; Responsive Architecture; Hygroscopic; Stimulus-Responsive Materials
series	eCAADe
email
last changed	2022/06/07 07:56

_id	ascaad2021_044
id	ascaad2021_044
authors	Özerol, Gizem; Semra Selçuk
year	2021
title	Designing Facades Based on Daylight Parameter: A Proposal for the Production of Complex Surface Panelization
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. 58-68
summary	Nowadays, due to the increasing demand for sustainable design and energy efficient buildings, “performance” is becoming a key parameter behind design decisions. Traditional design methods may be insufficient in both evaluating the energy performance and producing optimized design alternatives, as well as in understanding the relationship between design variables and performance metrics. Recently, via parametric design tools and optimization algorithms, a wide range of design methods have been formed and various performance data have been measured and optimized. In this context, this study offers a design approach to integrate sustainability principles and physical environmental conditions into the design process as a quantifiable parameter used to improve building performance. Further, this study aims to design a facade and its modules based on environmental conditions in Istanbul, Turkey. The design process focuses on daylight radiation and the analysis of environmental data using a digital model. Rhino and Grasshopper software was used as the digital medium for design and Ladybug-Honeybee plugins were utilized in the analysis. Based on Istanbul’s weather data obtained from Ladybug, optimization of the model consisting of the first diagrams was achieved during the environmental analysis process. The model underwent the analysis process created for facade panelization and the panelization process was carried out according to daylight radiation. After the design process is completed, the model will be ready for production for the 3d printed model. As a result of the study, a discussion developed on how to integrate precast concrete panels into the design of complex geometrical surfaces using computational design techniques.
series	ASCAAD
email
last changed	2021/08/09 13:11

_id	ascaad2021_021
id	ascaad2021_021
authors	Albassel, Mohamed; Mustafa Waly
year	2021
title	Applying Machine Learning to Enhance the Implementation of Egyptian Fire and Life Safety Code in Mega Projects
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. 7-22
summary	Machine Learning has become a significant research area in architecture; it can be used to retrieve valuable information for available data used to predict future instances. the purpose of this research was to develop an automated workflow to enhance the implementation of The Egyptian fire & life safety (FLS) code in mega projects and reduce the time wasted on the traditional process of rooms’ uses, occupant load, and egress capacity calculations to increase productivity by applying Supervised Machine Learning based on classification techniques through data mining and building datasets from previous projects, and explore the methods of preparation and analyzing data (text cleanup- tokenization- filtering- stemming-labeling). Then, provide an algorithm for classification rules using C# and python in integration with BIM tools such as Revit-Dynamo to calculate cumulative occupant load based on factors which are mentioned in the Egyptian FLS code, determine classification and uses of rooms to validate all data related to FLS. Moreover, calculating the egress capacity of means of egress for not only exit doors but also exit stairs. In addition, the research is to identify a clear understanding about ML and BIM through project case studies and how to build a model with the needed accuracy.
series	ASCAAD
email
last changed	2021/08/09 13:11

_id	ascaad2021_004
id	ascaad2021_004
authors	Ali, Nouran; Samir Hosny, Ahmed Abdin
year	2021
title	Thermal Performance of Nanomaterials of a Medium Size Office Building Envelope: With a Special Reference to Hot Arid Climatic Zone of Egypt
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. 385-396
summary	Global warming is becoming a huge threat in the 21st century. The building is the main contributor to energy consumption and greenhouse gas emissions which play an important role in global warming. Using new technologies provides a step towards a better-built environment. Nanotechnology is an emerging technology that provides innovative materials that integrate with the building envelope to enhance energy efficiency and decrease energy consumption in buildings. Many Nano products are a promising candidate for building thermal insulation and increasing the building’s efficiency. This paper aims to reach minimum energy consumption by investigating Nanomaterials thermal performance on a building’s envelope in a hot arid climate. An office building in Cairo, Egypt is chosen as a case study. The paper presents an empirical/applied inquiry that is based on a computer simulation using Design Builder software. Energy consumption is calculated for different cases; the base model of the office building without using nanomaterials, and several nano models using nanomaterials. The results indicate that the use of Nanomaterials can enhance the thermal performance of the office building and save about 13.44 % of the annual energy consumption of the building.
series	ASCAAD
email
last changed	2021/08/09 13:11

_id	ascaad2021_151
id	ascaad2021_151
authors	Allam, Samar; Soha El Gohary, Maha El Gohary
year	2021
title	Surface Shape Grammar Morphology to Optimize Daylighting in Mixed-Use Building Skin
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. 479-492
summary	Building Performance simulation is escalating towards design optimization worldwide utilizing computational and advanced tools. Egypt has its plan and agenda to adopt new technologies to mitigate energy consumption through various sectors. Energy consumption includes electricity, crude oil, it encompasses renewable and non-renewable energy consumption. Egypt Electricity (EE) consumption by sector percentages is residential (47%), industrial (25%) and commercial (12%), with the remainder used by government, agriculture, public lighting and public utilities (4%). Electricity building consumption has many divisions includes HVAC systems, lighting, Computers and Electronics and others. Lighting share of electricity consumption can vary from 11 to 15 percent in mixed buildings as in our case study which definitely less that the amount used for HVAC loads. This research aims at utilizing shape morphogenesis on facades using geometric shape grammar to enhance daylighting while blocking longwave radiations causing heat stress. Mixed-use building operates in daytime more than night which emphasizes the objective of this study. Results evaluation is referenced to LEED v4.1 and ASHRAE 90.1-2016 window-to-wall ratio calibration and massive wall description. Geometric morphogenesis relies on three main parameters; Pattern (Geometry Shape Grammar: R1, R2, and R3), a reference surface to map from, and a target surface to map to which is the south-western façade of the case study. Enhancing Geo-morph rule is to guarantee flexibility due to the rotation of sun path annually with different azimuth and altitude angles and follow LEED V4.1 enhancements of opaque wall percent for building envelope.
series	ASCAAD
email
last changed	2021/08/09 13:13

_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
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
doi	https://doi.org/10.52842/conf.ecaade.2021.2.351
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	ijac202119404
id	ijac202119404
authors	Ghandi, Mona; Blaisdell, Marcus; Ismail, Mohamed
year	2021
title	Embodied empathy: Using affective computing to incarnate human emotion and cognition in architecture
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 4, 532–552
summary	This research aims to develop a cyber-physical adaptive architectural space capable of real-time responses topeople’s emotions, based on biological and neurological data. To achieve this goal, we integrated artificialintelligence (AI), wearable technology, sensory environments, and adaptive architecture to create anemotional bond between a space and its occupants and encourage affective emotional interactions betweenthe two. The project’s objectives were to (1) measure and analyze biological and neurological data to detectemotions, (2) map and illustrate that emotional data, and (3) link occupants’emotions and cognition to a builtenvironment through a real-time emotive feedback loop. Using an interactive installation as a case study, thiswork examines the cognition-emotion-space interaction through changes in volume, color, and light as ameans of emotional expression. It contributes to the current theory and practice of cyber-physical design andthe role AI plays, as well as the interaction of technology and empathy.
keywords	Places and awareness, artificial intelligence and machine learning in design, intelligent responsive spaces,affective computing in architecture, cognition-emotion-space interaction, embodied empathy, neuromorphicdesign, cyber-physical neurospaces
series	journal
email
last changed	2024/04/17 14:29

_id	caadria2021_117
id	caadria2021_117
authors	Ikeno, Kazunosuke, Fukuda, Tomohiro and Yabuki, Nobuyoshi
year	2021
title	Can a Generative Adversarial Network Remove Thin Clouds in Aerial Photographs? - Toward Improving the Accuracy of Generating Horizontal Building Mask Images for Deep Learning in Urban Planning and Design
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. 377-386
doi	https://doi.org/10.52842/conf.caadria.2021.2.377
summary	Information extracted from aerial photographs is widely used in the fields of urban planning and architecture. An effective method for detecting buildings in aerial photographs is to use deep learning to understand the current state of a target region. However, the building mask images used to train the deep learning model must be manually generated in many cases. To overcome this challenge, a method has been proposed for automatically generating mask images by using textured 3D virtual models with aerial photographs. Some aerial photographs include thin clouds, which degrade image quality. In this research, the thin clouds in these aerial photographs are removed by using a generative adversarial network, which leads to improvements in training accuracy. Therefore, the objective of this research is to propose a method for automatically generating building mask images by using 3D virtual models with textured aerial photographs to enable the removable of thin clouds so that the image can be used for deep learning. A model trained on datasets generated by the proposed method was able to detect buildings in aerial photographs with an accuracy of IoU = 0.651.
keywords	Urban planning and design; Deep learning; Generative Adversarial Network (GAN); Semantic segmentation; Mask image
series	CAADRIA
email
last changed	2022/06/07 07:50

_id	acadia23_v1_242
id	acadia23_v1_242
authors	Noel, Vernelle A.
year	2023
title	Carnival + AI: Heritage, Immersive virtual spaces, and Machine Learning
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 1: Projects Catalog of the 43rd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 242-245.
summary	Built on a Situated Computations framework, this project explores preservation, reconfiguration, and presentation of heritage through immersive virtual experiences, and machine learning for new understandings and possibilities (Noel 2020; 2017; Leach and Campo 2022; Leach 2021). Using the Trinidad and Tobago Carnival - hereinafter referred to as Carnival - as a case study, Carnival + AI is a series of immersive experiences in design, culture, and artificial intelligence (AI). These virtual spaces create new digital modes of engaging with cultural heritage and reimagined designs of traditional sculptures in the Carnival (Noel 2021). The project includes three virtual events that draw on real events in the Carnival: (1) the Virtual Gallery, which builds on dancing sculptures in the Carnival and showcases AI-generated designs; (2) Virtual J’ouvert built on J’ouvert in Carnival with AI-generated J’ouvert characters specific; and (3) Virtual Mas which builds on the masquerade.
series	ACADIA
type	project
email
last changed	2024/04/17 13:58

_id	ascaad2021_049
id	ascaad2021_049
authors	Ramadan, Ayah
year	2021
title	Double Green Façades using Parametric Sustainable Design: A Simulation Tools with Parametric Approach to Improve Energy Performance of Office Buildings in Egypt
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. 727-741
summary	Parametric Sustainable design of the indoor environment of double green façades buildings focus on the development of office building structure in Egypt and achieved indoor thermal comfort at a low level of energy use. The goal of this paper is to study parametric design from a wide perspective in order to classify its advantages and evaluate its skill to support Sustainable design. As building construction sector is the largest energy consumer, Operation hours of air conditioners is speedily increasing in the office buildings area through summer season, which already accounts for 50% of energy consumption in Egypt. This study was carried out based on the simulation in Design Builder (6) software. The case, studied in the article is for office building, newly erected building with surface area of 25, 500 m2 is considered as the basis for the parametric Sustainable study. The new energy model was simulated resulting in about 70% in HVAC consumption and approximately 75% for whole building energy consumption. Analysis results showed that parametric optimization of building envelope at the design stage is a practicable approach to reducing energy consumption in office building design.
series	ASCAAD
email
last changed	2021/08/09 13:11

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