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	caadria2022_152
id	caadria2022_152
authors	Deshpande, Rutvik, Nisztuk, Maciej, Cheng, Cesar, Subramanian, Ramanathan, Chavan, Tejas, Weijenberg, Camiel and Patel, Sayjel Vijay
year	2022
title	Synthetic Machine Learning for Real-time Architectural Daylighting Prediction
doi	https://doi.org/10.52842/conf.caadria.2022.1.313
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. 313-322
summary	"Synthetic Machine Learning‚ offers a revolutionary leap in real-time environmental analysis for conceptual architectural design. By integrating automatic synthetic data generation, artificial neural network (ANN) training and online deployment, Synthetic Machine Learning offers two main advantages over conventional simulation; First, it reduces the analysis time for a reference simulation from minutes to seconds; Second, it is possible to deploy ANN as a web service in an online design environment, which therein increases accessibility, significantly reducing simulation costs and setup time. The application of Synthetic Machine Learning to perform Daylight Autonomy (DA) and Spatial Daylight Autonomy (sDA) studies to maximise building daylighting for a given use, window to wall ratio, and floorplan arrangement is showcased through a preliminary demonstration work. Comparatively the use of algorithmically generated synthetic data versus real-world data is becoming ubiquitous in other disciplines, the advantages of this approach to the building design process are further discussed.
keywords	Daylight Autonomy, machine learning, building energy performance, synthetic data-sets, SDG 7, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	sigradi2022_157
id	sigradi2022_157
authors	Santos, Luis; Berger, Christiane
year	2022
title	Mediating heat and light: a visualization-based tool to support the development of shading control protocols
source	Herrera, PC, Dreifuss-Serrano, C, Gómez, P, Arris-Calderon, LF, Critical Appropriations - Proceedings of the XXVI Conference of the Iberoamerican Society of Digital Graphics (SIGraDi 2022), Universidad Peruana de Ciencias Aplicadas, Lima, 7-11 November 2022 , pp. 1151–1162
summary	Operating Dynamic Shading Systems (DSS) is often an ill-defined task since it requires pondering design criteria that are often at odds with each other, e.g., visual comfort and building energy-efficiency. This work proposes a co-simulation approach to efficiently analyze the impact of controlling DSS in the daylight and the energy performance of indoor spaces to address this difficulty. The proposed workflow uses advanced daylight and building energy simulation tools to enable comprehensive comparative analyses by post-processing simulation output through queryable visualizations. Additionally, the approach allows designers to create or refine DSS control strategies by manipulating visualizations of hourly DSS operation schedules. The authors tested the workflow in designing and controlling a DSS in three different climates: a temperate, a hot, and a very cold climate. The proposed approach delivered valuable insights about the complex tradeoffs that emerge from controlling DSS.
keywords	Building Performance, Solar Heat Gain, Visual Comfort, Building Operation
series	SIGraDi
email
last changed	2023/05/16 16:57

_id	ecaade2022_248
id	ecaade2022_248
authors	Szentesi-Nejur, Szende, de Luca, Francesco and Flamand, Krystel
year	2022
title	Simulation Based Daylight Uniformity Optimizations for Elementary School Projects in Quebec Province
doi	https://doi.org/10.52842/conf.ecaade.2022.1.639
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 1, Ghent, 13-16 September 2022, pp. 639–648
summary	Adequate quantity of daylight provision and its uniform distribution is a key factor in the design of educational buildings. Methods exists to assess through simulations the adequacy of daylight quantity and its uniformity in buildings during the design phase. Building regulations and public procurement procedures set the daylight requirements for school buildings. Daylight regulations were recently introduced in Quebec, Canada, which have an impact also on the economic feasibility of projects. This study presents an investigation about optimal daylight design solutions for a classroom in the Quebec climate. A parametric model and a generative process was developed to optimize a skylight and a light shelf to improve uniformity while providing adequate daylight in classrooms with different orientations. The methods and outcomes which includes economic considerations represent a useful insight for designers and researchers.
keywords	Daylight Optimization, Iterative Design, Cost-Effective Design, School Buildings, Climate-based Daylight Modelling
series	eCAADe
email
last changed	2024/04/22 07:10

_id	acadia22_506
id	acadia22_506
authors	Ozarisoy, Bertug; Altan, Hasim
year	2022
title	Passive Cooling Strategies for Thriving in a Changing Climate
source	ACADIA 2022: Hybrids and Haecceities [Proceedings of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 506-523.
summary	This paper investigates the thermal performance of 288 flats in three different nationally representative collective housing archetypes in the southeastern Mediterranean island of Cyprus, where the climate is subtropical (Csa) and partly semi-arid (Bsh), as designated in the Köppen climate classification system. The participants’ experiences and thermal sensation votes were assessed to predict individual aspects of adaptive thermal comfort, and the relevance thereof on overheating, and in situ measurements—including indoor air temperatures, thermal imaging survey, recorded building-fabric-element heat fluxes, on-site environmental conditions monitoring, and review of household energy bills to accurately determine actual energy use—were collected
series	ACADIA
type	paper
email
last changed	2024/02/06 14:04

_id	cdrf2022_337
id	cdrf2022_337
authors	Ping Chen, Chang Liu, and Hsin-Hsien Chiu
year	2022
title	Study on Optimization of Building Climate Adaptive Morphology in Cold Regions of China: Case of U-Shaped College Building
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_30
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	Proper design of building form will facilitate the use of climate environment in order to reduce the reliance of buildings on active equipment. This study takes the cold region of China as the research area, and Jinan city of Shandong province as a typical city in the cold region for specific research. The multi-objective optimization tool based on NSGA-II algorithm is used to optimize the opening angle, length of both sides and floor height of the building, and finally the optimal size range of the university teaching building under the influence of solar radiation heat gain in winter and summer is obtained, and the results show that for the U-shaped university teaching building, the parameters that affect the building performance more in the case of the east side opening are the length of the north side building and the rotation angle of the south side building, and the parameters that affect the performance more in the case of the west side opening are the length of the building on the south side.
series	cdrf
email
last changed	2024/05/29 14:03

_id	ascaad2022_011
id	ascaad2022_011
authors	Najafi, Qodsiye; Mahlabani, Yousef; Goharian, Ali; Mahdavinejad, Mohammadjavad
year	2022
title	A Novel Design-Based Optimization Solution for Building by Sensitivity Analysis
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 632-653
summary	The important objective of a building must be to provide a comfortable environment for people. Heating, ventilation and air conditioning (HVAC) systems provide a comfortable environment, but they are using high energy consumption, therefore, designing an energy-efficient building that balances energy performance and thermal comfort is necessary. To achieve this subject is important to choose the effective parameters for energy performance. This research aim is to produce a methodology for multi-objective optimization of daylight and thermal comfort in order to study the effect of wall material and shading of an office building (Tehran a basic-location). The building simulation was developed and validated by comparing predicted daylight hours and thermal comfort hour based on test and training on Jupiter Notebook (Anaconda3). The sensitivity analysis uses a multiple linear regression (MLR) method. Secondly, optimization is based on a genetic algorithm (GA) with the effective parameters to optimize the daylight and thermal comfort performance. For this, we developed a parametric model using the Grasshopper plugin for Rhino and then use Honeybee and Ladybug plugins to simulate thermal comfort and daylight, at the end use the Octopus engine to find an optimization solution. The result of this paper is essential as a preliminary analysis for shading devices, window-to-wall ratios, and wall construction optimization in the open-plan office.
series	ASCAAD
email
last changed	2024/02/16 13:24

_id	sigradi2022_169
id	sigradi2022_169
authors	Riquelme, Marianne; Martínez Arias, Andrea; Rivera Barraza, María Isabel
year	2022
title	The effects of vertical growth: Study of the right to solar access in residential areas. The case of Concepción, Chile.
source	Herrera, PC, Dreifuss-Serrano, C, Gómez, P, Arris-Calderon, LF, Critical Appropriations - Proceedings of the XXVI Conference of the Iberoamerican Society of Digital Graphics (SIGraDi 2022), Universidad Peruana de Ciencias Aplicadas, Lima, 7-11 November 2022 , pp. 1163–1174
summary	In Chile, the neoliberal economic model has intensified the densification process with few restrictions, giving rise to a prolific construction of high-rise multi-family buildings in low-density areas. The study uses a case of a high-rise housing complex that contrasts with local typologies and breaks the human scale in a traditional neighborhood (i.e., one story continues façade). The impact of shadow projections on its neighboring houses is calculated and duration according to the solar sun path. In addition, the effect on daylight availability in the residential units of the towers is analyzed. The results show that the tower's complex projects shade the neighborhood for up to 200m in winter. Also, a tower's daylighting autonomy decreases by 13% in lower floorplans because of the shades of its neighboring towers. It reflects how this form of high-rise housing affects a fundamental right: the right to the sun in its forms of radiation for passive heating in a heating-demanding climate zone and the potential for daylight harvesting for its own residential units.
keywords	Design, Nature and Ecosystems, shadow cones, high-rise residential, daylight, simulation
series	SIGraDi
email
last changed	2023/05/16 16:57

_id	sigradi2022_99
id	sigradi2022_99
authors	Schmidlin, Flavio; Tavares da Silva, Felipe
year	2022
title	Investigation of indoor daylight performance of the two-sided roof monitor system solution space
source	Herrera, PC, Dreifuss-Serrano, C, Gómez, P, Arris-Calderon, LF, Critical Appropriations - Proceedings of the XXVI Conference of the Iberoamerican Society of Digital Graphics (SIGraDi 2022), Universidad Peruana de Ciencias Aplicadas, Lima, 7-11 November 2022 , pp. 237–248
summary	The present study evaluated levels of zenith daylight and the incidence of solar radiation in an indoor environment with two-sided roof monitor system opening using a parametric geometric model, numerical simulations and machine learning techniques. It was used a climatic database from a locality with a hot and humid tropical climate, at low latitude in Brazil. The daylight performance was analyzed using the Useful Daylight Illuminances and the incident solar radiation with annual and daily maximum results. The study included analyzes with the zenith openings oriented to North-South and East-West, considering the photosensitive sensor meshes on the walls and floor. The results presented that the modeling process used can help the architectural design process in its dimension of natural illuminance and incidence of solar radiation in internal environments, showing optimized configurations for the room size and for the zenithal opening geometry.
keywords	Predictive Modeling, Parametric Modeling, Radiation, Zenithal Daylight, Indoor Environment
series	SIGraDi
email
last changed	2023/05/16 16:55

_id	acadia22_524
id	acadia22_524
authors	Xiao, Jun; Liu, Yubo; Deng, Qiaoming
year	2022
title	High-Density Building Form Generation Considering Daylight Performance
source	ACADIA 2022: Hybrids and Haecceities [Proceedings of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 524-535.
summary	In this case, aiming for a high-density building designed with high-quality daylighting, this article develops a building form generation program for daylight performance optimization integrating Cellular Automata (CA) and Genetic Algorithm (GA), tools that can provide a global daylighting optimization through the balance of competition and concession of agents. The CA model provides randomness and structural restriction, while GA provide optimization and convergence by evaluating and selecting a series of CA models. The model applies designed CA rules on daylighting and a mathematic proxy model for daylight performance in GA fitness calculation.
series	ACADIA
type	paper
email
last changed	2024/02/06 14:04

_id	caadria2022_59
id	caadria2022_59
authors	Banihashemi, Farzan, Reitberger, Roland and Lang, Werner
year	2022
title	Investigating Urban Heat Island and Vegetation Effects Under the Influence of Climate Change in Early Design Stages
doi	https://doi.org/10.52842/conf.caadria.2022.2.679
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. 679-688
summary	Different criteria need to be considered for optimal strategies in the early design stages of urban developments. Under the influence of climate change, the urban heat island effect (UHI) is a phenomenon that gains importance in the early design stages. Here, different parameters, for instance, vegetation ratio in the city district and building density, play a significant role in the UHI effect. These parameters need to be quantified through different simulation tools for optimal climate adaptation and mitigation measures on the urban district scale. However, not all parameters and their influence are clear to the decision-makers and actors in the early design stages. Hence, we propose a Monte Carlo based sensitivity analysis (SA) and uncertainty analysis (UA) to show the significance of different parameters and quantify them. The SA aims to identify the major influencing parameters, whereas the UA quantifies the effect on the energy performance and indoor thermal comfort of occupants. The workflow is integrated into a collaborative design platform and applied in a case study to support decision-makers in the early design stages for new developments, densification, or refurbishment scenarios.
keywords	Monte Carlo Simulation, Sensitivity Analysis, Uncertainty Analysis, Building Energy Simulation, SDG 13, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_114
id	caadria2022_114
authors	Dong, Zhiyong, Lin, Jinru, Wang, Siqi, Xu, Yijia, Xu, Jiaqi and Liu, Xiao
year	2022
title	Where Will Romance Occur, A New Prediction Method of Urban Love Map through Deep Learning
doi	https://doi.org/10.52842/conf.caadria.2022.1.213
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. 213-222
summary	Romance awakens fond memories of the city. Finding out the relationship between romantic scene and urban morphology, and providing a prediction, can potentially facilitate the better urban design and urban life. Taking the Yangtze River Delta region of China as an example, this study aims to predict the distribution of romantic locations using deep learning based on multi-source data. Specifically, we use web crawlers to extract romance-related messages and geographic locations from social media platforms, and visualize them as romance heatmap. The urban environment and building features associated with romantic information are identified by Pearson correlation analysis and annotated in the city map. Then, both city labelled maps and romance heatmaps are fed into a Generative Adversarial Networks (GAN) as the training dataset to achieve final romance distribution predictions across regions for other cities. The ideal prediction results highlight the ability of deep learning techniques to quantify experience-based decision-making strategies that can be used in further research on urban design.
keywords	Romance Heatmap, Generative Adversarial Networks, Deep Learning, Big Data Analysis, Correlation Analysis, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_145
id	caadria2022_145
authors	Duering, Serjoscha, Fink, Theresa, Chronis, Angelos and Konig, Reinhard
year	2022
title	Environmental Performance Assessment - The Optimisation of High-Rises in Vienna
doi	https://doi.org/10.52842/conf.caadria.2022.1.545
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. 545-554
summary	Our cities are facing different kinds of challenges - in parallel to the urban transformation and densification, climate targets and objectives of decision-makers are on the daily agenda of planning. Therefore, the planning of new neighbourhoods and buildings in high-density areas is complex in many ways. It requires intelligent processes that automate specific aspects of planning and thus enable impact-oriented planning in the early phases. The impacts on environment, economy and society have to be considered for a sustainable planning result in order to make responsible decisions. The objective of this paper is to explore pathways towards a framework for the environmental performance assessment and the optimisation of high-rise buildings with a particular focus on processing large amounts of data in order to derive actionable insights. A development area in the urban centre of Vienna serves as case study to exemplify the potential of automated model generation and applying ML algorithm to accelerate simulation time and extend the design space of possible solutions. As a result, the generated designs are screened on the basis of their performance using a Design Space Exploration approach. The potential for optimisation is evaluated in terms of their environmental impact on the immediate environment.
keywords	simulation, prediction and evaluation, machine learning, computational modelling, digital design, high-rises, SGD 11, SDG 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_222
id	ecaade2022_222
authors	Eisenstadt, Viktor, Bielski, Jessica, Langenhan, Christoph, Althoff, Klaus-Dieter and Dengel, Andreas
year	2022
title	Autocompletion of Design Data in Semantic Building Models using Link Prediction and Graph Neural Networks
doi	https://doi.org/10.52842/conf.ecaade.2022.1.501
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 1, Ghent, 13-16 September 2022, pp. 501–510
summary	This paper presents an approach for AI-based autocompletion of graph-based spatial configurations using deep learning in the form of link prediction through graph neural networks. The main goal of the research presented is to estimate the probability of connections between the rooms of the spatial configuration graph at hand using the available semantic information. In the context of early design stages, deep learning-based prediction of spatial connections helps to make the design process more efficient and sustainable using the past experiences collected in a training dataset. Using the techniques of transfer learning, we adapted methods available in the modern graph-based deep learning frameworks in order to apply them for our autocompletion purposes to suggest possible further design steps. The results of training, testing, and evaluation showed very good results and justified application of these methods.
keywords	Spatial Configuration, Autocompletion, Link Prediction, Deep Learning
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_366
id	ecaade2022_366
authors	Geropanta, Vasiliki, Karagianni, Anna, Parthenios, Panagiotis, Ampatzoglou, Triantafyllos, Fatouros, Loukas, Simantiraki, Vasiliki, Brokos-Melissaratos, Orestis and Eleftheriadis, Dimitris
year	2022
title	Digitalization of Participatory Greening - The case of UnionYouth in Chania
doi	https://doi.org/10.52842/conf.ecaade.2022.1.469
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 1, Ghent, 13-16 September 2022, pp. 469–478
summary	The contemporary climate crisis pushed communities of actors, cities and citizens to use smart technology, digital platforms, and data-based intelligence to steer creative solutions for greening in their urban ecosystems. This phenomenon brought about an increasing imperative for citizen participation and inclusion, in the co-design of green infrastructures, suggesting alternative ways to deal with the lack or misuse of public space. In this framework, this paper analyzes the case of ''UnionYouth in Chania'', a project that aims a) to build an environmental awareness strategy for Generation Z, b) to promote capacity-building processes related to climate change and environmental protection, c) actually transform the city public space through participatory processes. Specifically, the project describes the creation of a digital platform and a mobile app consisting of several engagement tools that allow interaction between the digital community of youth, the city's decision-makers, and city greening actors. Therefore, the first part of the paper talks about the necessity of promoting today's participatory processes in the city for climate change mitigation through a literature review that emerged in the last decade. The second part of the paper examines a case study, namely UnionYouth in Chania, a digital collaborative platform that promotes methods for greening the city through district-based, activity-based, and network-based redesign solutions. The third part of the paper brings about interesting reflections on the relationship between the analog and digital world, and how bottom-up processes may be an important tool in city planning. The overall scope of the analysis of the specific case study is to bring insights into the architectural world, as a means to create more bridges with citizens and communities and contribute to their greening understanding.
keywords	Climate Change, Generation Z, Green Infrastructure, Raise Awareness, Mobile Application, Participatory Design, Smart City
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ascaad2022_032
id	ascaad2022_032
authors	Ibrahim, Aly; Omar, Walid; Ebrahim, Sherif; Abdelmohsen, Sherif
year	2022
title	Moisture-Harvesting Lizard Skins as an Inspiration for Performative Building Envelopes in Arid Climates
source	Hybrid Spaces of the Metaverse - Architecture in the Age of the Metaverse: Opportunities and Potentials [10th ASCAAD Conference Proceedings] Debbieh (Lebanon) [Virtual Conference] 12-13 October 2022, pp. 515-528
summary	Research on shape-shifting adaptive architectural skins has recently focused on bio-inspired programmable materials. Only a few studies however examine the microstructure of living organisms, especially in terms of morphological adaptation in harsh climatic conditions. This paper explores the microstructure of moisture-harvesting lizard skins, specifically the Trapelus species of the Agamidae family in North-East Africa, as an inspiration for programmable materials in adaptive building skins in the arid climate of Egypt. The paper investigates the ability to improve the durability and morphological capabilities of programmable materials based on surface formation, utilizing digital fabrication techniques. A series of physical experiments were conducted on different samples of 3D printed wood filament under several humidity conditions, as a single layer, with textured patterns, and with the addition of potassium chloride as a moisture-harvesting chemical composite. The paper concluded that materials composed of textured patterns and moisture-harvesting chemical composites exhibited the highest moisture retention, therefore leading to advantages in its use in adaptive building skins in arid climates, through a wide variety of design possibilities for performative building envelopes.
series	ASCAAD
email
last changed	2024/02/16 13:24

_id	ecaade2022_161
id	ecaade2022_161
authors	Kharbanda, Kritika, Papadopoulou, Iliana, Pouliou, Panagiota, Daw, Karim, Belwadi, Anirudh and Loganathan, Hariprasath
year	2022
title	LearnCarbon - A tool for machine learning prediction of global warming potential from abstract designs
doi	https://doi.org/10.52842/conf.ecaade.2022.2.601
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 601–610
summary	The new construction that is projected to take place between 2020 and 2040 plays a critical role in embodied carbon emissions. The change in material selection is inversely proportional to the budget, as the project progresses. Given the fact that early-stage design processes often do not include environmental performance metrics, there is an opportunity to investigate a toolset that enables early-stage design processes to integrate this type of analysis into the preferred workflow of concept designers. The value here is that early-stage environmental feedback can inform the crucial decisions that are made in the beginning, giving a greater chance for a building with better environmental performance in terms of its life cycle. This paper presents the development of a tool called LearnCarbon, as a plugin of Rhino3d, used to educate architects and engineers in the early stages about the environmental impact of their design. It facilitates two neural networks trained with the Embodied Carbon Benchmark Study by Carbon Leadership Forum, which learn the relationship between building geometry, typology, and structure with the Global Warming potential in tCO2e. The first one, a regression model, is able to predict the GWP based on the massing model of a building, along with information about typology and location. The second one, a classification model, predicts the construction type given a massing model and target GWP. LearnCarbon can help improve the building life cycle impact significantly, through early predictions of the structure’s material, and can be used as a tool for facilitating sustainable discussions between the architect and the client.
keywords	Machine Learning, Carbon Emissions, LCA, Rhino Plug-in
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_471
id	caadria2022_471
authors	Kim, Taehoon, Hong, Soonmin, Panya, David Stephen, Gu, Hyeongmo, Park, Hyejin, Won, Junghye and Choo, Seungyeon
year	2022
title	Development of Technology for Automatic Extraction of Architectural Plan Wall Lines for Concrete Waste Prediction Using Point Cloud
doi	https://doi.org/10.52842/conf.caadria.2022.2.597
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. 597-606
summary	Recently, as more and more projects on residential environment improvement in cities are actively carried out, the cases of demolishing or remodelling buildings has been increasing. Most of the target buildings for such projects are made of concrete. In order to reduce energy use as well as carbon emissions, the amount of concrete used as a building material should be reduced. This is because the concrete is the largest amount of construction waste, which the exact amount of concrete needs to be predicted. The architectural drawings are essential for the estimation and demolition of building waste, but the problem is that most of the old buildings' drawings do not exist. The 3D scanning process was performed to create the plans for such old buildings instead of the conventional method that is long time-consuming and labour-intensive actual measurement. In this study, we scanned 40 old houses that were scheduled to be demolished. The result showed that the 3D scanned drawings' accuracy - 99.2% - was higher than the ones measured by the conventional way. Through the algorithm developed in this study, the various processes of demolition, drawing measurement, and discarding quantity prediction can be solved in one process, thereby reducing work efficiently. And, considering the reliability of the research results, it is possible to reduce the economic loss by predicting the exact amount of waste in advance. After that, if the algorithm, developed in this study, can be further subdivided and supplemented to identify the materials for each part of the old buildings, it will be able to propose an efficient series of processes that distinguish between recyclable materials and wastes and thereby efficiently dispose of them. 0864108000
keywords	Point Cloud, Construction Waste, Parametric Design, Algorithm, Automatic Extraction, SDG 8
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_344
id	caadria2022_344
authors	Krezlik, Adrian
year	2022
title	Considering Energy, Materials and Health Factors in Architectural Design, Two Renovation Strategies for the Portuguese Building Stock
doi	https://doi.org/10.52842/conf.caadria.2022.2.619
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. 619-628
summary	According to the Intergovernmental Panel on Climate Change, the built environment has a significant share in global final energy use, greenhouse gases emission, land-system change, and biodiversity loss to list some indicators. In Europe, the biggest challenge is to regenerate existing building stock to create a positive impact on Nature. The Portuguese housing stock is old: 56% is more than 30 years old, and it has a low level of thermal comfort and energy efficiency. The first thermal regulations appeared in 1990 and therefore most of the houses need urgent renovation to meet EU decarbonization goals, and to improve energy efficiency, as well as well-being and comfort of residents. This paper presents a method that aims to verify existing solutions known from vernacular architecture as complementary to existing strategies. It employs digital simulation to verify whether they could be used for renovation, measuring their impact on human and planetary health. The paper shows that there is a wide spectrum of parameters that influence the renovation process and that it is possible to enhance building performance using vernacular knowledge.
keywords	Building Energy Modelling, Life Cycle Assessment, Occupant Health, Energy Renovation, Vernacular Mimicry, SDG 3, SDG 11, SDG 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_394
id	caadria2022_394
authors	Li, Yuanyuan, Huang, Chenyu, Zhang, Gengjia and Yao, Jiawei
year	2022
title	Machine Learning Modeling and Genetic Optimization of Adaptive Building Facade Towards the Light Environment
doi	https://doi.org/10.52842/conf.caadria.2022.1.141
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. 141-150
summary	For adaptive facades, the dynamic integration of architectural and environmental information is essential but complex, especially for the performance of indoor light environments. This research proposes a new approach that combines computer-aided design methods and machine learning to enhance the efficiency of this process. The first step is to clarify the design factors of adaptive facade, exploring how parameterized typology models perform in simulation. Then interpretable machine learning is used to explain the contribution of adaptive facade parameters to light criteria (DLA, UDI, DGP) and build prediction models for light simulation. Finally, Wallacei X is used for multi-objective optimization, determines the optimal skin options under the corresponding light environment, and establishes the optimal operation model of the adaptive facades against changes in the light environment. This paper provides a reference for designers to decouple the influence of various factors of adaptive facades on the indoor light environment in the early design stage and carry out more efficient adaptive facades design driven by environmental performance.
keywords	Adaptive Facades, Light Environment, Machine learning, Light Simulation, Genetic Algorithm, SDG 3, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	architectural_intelligence2022_8
id	architectural_intelligence2022_8
authors	Manning He, Huiwang Pen, Meixiang Li, Yu Huang, Da Yan, Siwei Lou & Liwei Wen
year	2022
title	Investigation on typical occupant behavior in air-conditioned office buildings for South China’s Pearl River Delta
doi	https://doi.org/https://doi.org/10.1007/s44223-022-00005-w
source	Architectural Intelligence Journal
summary	The excessive simplification of occupant behavior is considered as the most important factor that affects the uncertainty of building performance simulation, thus affects the reliability and generalizability of simulation-based design and forecast. In this paper, occupant behavior in air-conditioned office buildings of the Pearl River Delta (PRD) region was investigated and defined. Copies of 873 questionnaires about the occupant behavior in air-conditioned office buildings in the PRD region were collected to study the relationship between indoor environment quality and adaptive behaviors. Eight typical office occupant schedules were defined via K-means clustering method. A probability prediction model of cooling temperature set-point was established by using the Ordinal Logistic Regression method. According to the different control modes of air conditioning, window, blind and lighting equipment, four types of typical behavior patterns were proposed using the K-prototype clustering method, which could be developed into 20 typical occupant behavior styles of office buildings in the PRD region.
series	Architectural Intelligence
email
last changed	2025/01/09 15:00

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