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_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
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
doi	https://doi.org/10.52842/conf.caadria.2022.1.545
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_70
id	ecaade2022_70
authors	Kavuncuoglu, Canberk, Akgün, Yenal and Özener, Ozan Önder
year	2022
title	Kinetic Architecture and BIM Integration - A case study on a Bricard linkage canopy system
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. 243–252
doi	https://doi.org/10.52842/conf.ecaade.2022.1.243
summary	This paper presents a case study focusing on BIM integration to kinetic structure design based on an extended kinetic BIM (KBIM) ontology through a Bricard linkage canopy system. With the parametric abstraction of structural system components and kinematic behaviors, the design process was carried out in a BIM environment using KBIM models and objects. The study results show the effectiveness of the proposed KBIM framework for the synthesis and assessment of design alternatives according to total system performance, architectonic composition, and environmental responsiveness.
keywords	BIM, Kinetic Architecture, Bricard Linkage, Parametric Design
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ijac202220308
id	ijac202220308
authors	Rodrigues, Ricardo C; Rovenir B Duarte
year	2022
title	Generating floor plans with deep learning: A cross-validation assessment over different dataset sizes
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 3, pp. 630–644
summary	The advent of deep learning has enabled a series of opportunities; one of them is the ability to tackle subjective factors on the floor plan design and make predictions though spatial semantic maps. Nonetheless, the amount available of data grows exponentially on a daily basis, in this sense, this research seeks to investigate deep generative methods of floor plan design and its relationship between data volume, with training time, quality and diversity in the outputs; in other words, what is the amount of data required to rapidly train models that return optimal results. In our research, we used a variation of the Conditional Generative Adversarial Network algorithm, that is, Pix2pix, and a dataset of approximately 80 thousand images to train 10 models and evaluate their performance through a series of computational metrics. The results show that the potential of this data-driven method depends not only on the diversity of the training set but also on the linearity of the distribution; therefore, high-dimensional datasets did not achieve good results. It is also concluded that models trained on small sets of data (800 images) may return excellent results if given the correct training instructions (Hyperparameters), but the best baseline to this generative task is in the mid-term, using around 20 to 30 thousand images with a linear distribution. Finally, it is presented standard guidelines for dataset design, and the impact of data curation along the entire process
keywords	Dataset Reduction, Pix2pix, Artificial Intelligence, Deep Generative Models, GANs
series	journal
last changed	2024/04/17 14:30

_id	cdrf2022_478
id	cdrf2022_478
authors	Andrea Macruz, Mirko Daneluzzo, and Hind Tawaku
year	2022
title	Performative Ornament: Enhancing Humidity and Light Levels for Plants in Multispecies Design
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_41
summary	The paper shifts the design conversation from a human-centered design methodology to a posthuman design, considering human and nonhuman actors. It asks how designers can incorporate a multispecies approach to creating greater intelligence and performance projects. To illustrate this, we describe a project of “ornaments” for plants, culminating from a course in an academic setting. The project methodology starts with “Thing Ethnography” analyzing the movement of a water bottle inside a house and its interaction with different objects. The relationship between water and plant was chosen to be further developed, considering water as a material to increase environmental humidity for the plant and brightness through light reflectance and refraction. 3D printed biomimetic structures as supports for water droplets were designed according to their performance and placed in different arrangements around the plant itself. Humidity levels and illuminance of the structures were measured. Ultimately, this created a new approach for working with plants and mass customization. The paper discusses the resultant evidence-based design and environmental values.
series	cdrf
email
last changed	2024/05/29 14:03

_id	ecaade2022_16
id	ecaade2022_16
authors	Bailey, Grayson, Kammler, Olaf, Weiser, Rene, Fuchkina, Ekaterina and Schneider, Sven
year	2022
title	Performing Immersive Virtual Environment User Studies with VREVAL
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. 437–446
doi	https://doi.org/10.52842/conf.ecaade.2022.2.437
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 learns the relationship between building geometry, typology, and construction type with the Global Warming potential (GWP) in tons of C02 equivalent (tCO2e). The first one, a regression model, can 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	Pre-Occupancy Evaluation, Immersive Virtual Environment, Wayfinding, User Centered Design, Architectural Study Design
series	eCAADe
email
last changed	2024/04/22 07:10

_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
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
doi	https://doi.org/10.52842/conf.caadria.2022.1.313
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	caadria2022_338
id	caadria2022_338
authors	Dias Guimaraes, Gabriela, Gu, Ning, Gomes da Silva, Vanessa, Ochoa Paniagua, Jorge, Rameezdeen, Rameez, Mayer, Wolfgang and Kim, Ki
year	2022
title	Data, Stakeholders, and Environmental Assessment: A BIM-Enabled Approach to Designing-out Construction and Demolition Waste
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. 587-596
doi	https://doi.org/10.52842/conf.caadria.2022.2.587
summary	Construction and Demolition waste has started to become a target in the path for a more sustainable industry mainly due to massive resource consumption, land depletion and emissions. As a substantial amount of waste originates due to inadequate decision-making during design, strategies to design-out waste are required. Accurate environmental impact of, not only the whole building, but construction materials and elements are crucial to the development of these strategies, but dependent on data availability, expert knowledge and proper sharing and storage of information. Hence, this study aims to investigate the relation between data, stakeholders and environmental assessment to properly build a design-out waste framework. An in-depth data collection from literature review and stakeholders' interviews guided the development of a conceptual framework to assist designers with information related to waste production and its reduction. After that, the necessary technical specifications for its adoption through a BIM environment were analysed. Its contribution is firstly on a shift of thinking during the design phase, as the goal is to provide environmental information so designers can take into consideration the long-term consequences of waste from different strategies and solutions; and secondly in the development of a computational tool that facilitates the design-out process.
keywords	Construction and Demolition Waste, Design, BIM, Environmental Data, Stakeholders, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_367
id	ecaade2022_367
authors	Doumpioti, Christina and Huang, Jeffrey
year	2022
title	Field Condition - Environmental sensibility of spatial configurations with the use of machine intelligence
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. 67–74
doi	https://doi.org/10.52842/conf.ecaade.2022.2.067
summary	Within computational environmental design (CED), different Machine Learning (ML) models are gaining ground. They aim for time efficiency by automating simulation and speeding up environmental performance feedback. This study suggests an approach that enhances not the optimization but the generative aspect of environmentally driven ML processes in architectural design. We follow Stan Allen's (2009) idea of 'field conditions' as a bottom-up phenomenon according to which form and space emerge from local invisible and dynamic connections. By employing parametric modeling, environmental analysis data, and conditional Generative Adversarial Networks [cGAN] we introduce a generative approach in design that reverses the typical design process of going from formal interpretation to analysis and encourages the emergence of spatial configurations with embedded environmental intelligence. We call it Intensive-driven Environmental Design Computation [IEDC], and we employ it in a case study on a residential building typology encountered in the Mediterranean. The paper describes the process, emphasizing dataset preparation as the stage where the logic of field conditions is established. The proposed research differentiates from cGAN models that offer automatic environmental performance predictions to one that spatial predictions stem from dynamic fields.
keywords	Field Architecture, Environmental Design, Generative Design, Machine Learning, Residential Typologies
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ijac202220306
id	ijac202220306
authors	Duclos-Prévet, Claire; François Guéna; Mariano Efron
year	2022
title	Constraint handling methods for a generative envelope design using genetic algorithms: The case of a highly constrained problem
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 3, pp. 587–609
summary	The use of genetic algorithms as generative and performance design techniques often involves, in practice, constraint handling, which can be a complex task. Moreover, environmental simulations are computationally expensive and managing constraints can avoid wasting time on infeasible solutions. Despite these two incentives, and the benefits of an immense literature, both applied and theorical, on constrained optimization, there are only few guidelines and tools directly applicable by architects to address this issue. This paper proposes to fill this gap by identifying, classifying, and implementing different constraint management techniques available to architects. Seven methods have been tested for a highly constrained envelope design problem, consisting in the optimization of a sun-shading system. Three of them are easily replicable to different types of projects while the four others need to find a problem-specific heuristic. It appears that the second category is more efficient but implies the use of generative techniques that are more difficult to implement than parametric models
keywords	Optimization under constraint, performative envelope design, generative and sustainable design, agent-based modeling, multiobjective genetic algorithm, visual comfort
series	journal
last changed	2024/04/17 14:30

_id	ijac202220302
id	ijac202220302
authors	Kabošová, Lenka; Angelos Chronis; Theodoros Galanos
year	2022
title	Fast wind prediction incorporated in urban city planning
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 3, pp. 511–527
summary	Digital design and analysis tools are continually progressing, enabling more seamless integration of climatic impacts into the conceptual design stage, which naturally means enhanced environmental performance of the final designs. Planning sustainable urban configurations and, consequently, environment-derived architectural forms becomes more rapid and requires less effort enabling smooth incorporation into day-to-day practice. This research paper presents a wind prediction-based architectural design method for improving outdoor wind comfort through urbanism and architecture. The added value of the environment-driven design loop consisting of parametric design, wind flow analysis, and necessary design modifications lies in leveraging the newly developed wind prediction tool InFraRed. As is demonstrated in the application study in Kosice, Slovakia, iterating through various design options and evaluating their impact on the wind flow is swift and reliable. That enables the designer to explore the best-performing design alternatives for outdoor wind comfort, yet the extra time required for the analysis is negligible
keywords	real-time wind predictions, wind comfort, parametric design, computational fluid dynamics analysis, machine learning, infrared
series	journal
last changed	2024/04/17 14:29

_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
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
doi	https://doi.org/10.52842/conf.ecaade.2022.2.601
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_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
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
doi	https://doi.org/10.52842/conf.caadria.2022.1.141
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	caadria2022_464
id	caadria2022_464
authors	Liu, Xinyu and van Ameijde, Jeroen
year	2022
title	Data-driven Research on Street Environmental Qualities and Vitality Using GIS Mapping and Machine Learning, a Case Study of Ma On Shan, Hong Kong
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. 485-494
doi	https://doi.org/10.52842/conf.caadria.2022.1.485
summary	In a post-carbon framework, data-driven methods can be used to assess the environmental quality and sustainability of urban streetscape. Streets are an important part of people's daily lives and provide places for social interaction. Therefore, in this study, the relationship between street quality and street vibrancy is measured using the new town of Ma On Shan, Hong Kong as a study area. Firstly, machine learning was used to identify the physical features of streets through geographic information collection and streetscape image acquisition. Secondly, previous measurement algorithms are combined to calculate the greenness, walkability, safety, imageability, enclosure, and complexity of streets. Thirdly, secondary calculations and visualisations were carried out on a Geographic Information System (GIS) platform to observe the current distribution of street qualities. Finally, the relationship between street quality and vibrancy was analysed using SPSS statistical analysis software. The results show that walkability has a positive effect on street vitality, whereas safety and complexity have a negative effect on street vitality. This study demonstrates how the quantitative assessment of urban street environments can be used as a reference for building a green, low-carbon, healthy, and walkable city.
keywords	Street Quality, Geographic Information Systems, Machine Learning, Image Segmentation, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2022_47
id	ecaade2022_47
authors	Marsillo, Laura, Suntorachai, Nawapan, Karthikeyan, Keshava Narayan, Voinova, Nataliya, Khairallah, Lea and Chronis, Angelos
year	2022
title	Context Decoder - Measuring urban quality through artificial intelligence
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. 237–246
doi	https://doi.org/10.52842/conf.ecaade.2022.2.237
summary	Understanding the quality of places during the early design process can improve design decision making and increase not only the chance of effective site development for the place and surroundings but also provide foresight to the mental, physical and environmental well-being of the future occupants. A context can be described differently depending on the designer's studies. However, in order to view the place holistically, various layers should be considered for a cross-disciplinary correlation. This paper proposes a prototypical tool to evaluate the quality of places using machine learning to help cluster and visualise design metrics according to the features provided. By selecting a location in a city, it offers other site contexts with similar characteristics and a similar level of complexity in relation to the surroundings. The tool was initially developed for Naples (Italy) as a case study city and incorporates key indicators related to connectivity of amenities, walkability, urban density, population density, outdoor thermal comfort, popular rate review and sentiment analysis from social media. With current open-source data, these indicators such as OpenStreetMap or social media sentiment can be collected with embedded geotags. These site-specific multilayers were evaluated under the metrics of 3 ranges i.e 400, 800 and 1,200-metre walking distance. This paper demonstrates the potential of using machine learning integrated with computational design tools to visualise the otherwise invisible data for users to interpret any context comprehensively in a holistic approach. Even though this tool is made for Naples, this tool can be extended to other cities across the world. As a result, the tool assists users in understanding not only site-specific location but also draws lines to other neighbourhoods within the city with a similar phenomenon of correlation between key performance indicators.
keywords	Computational Design, Urban Analysis, Machine Learning, Computer Vision, Sentiment Analysis
series	eCAADe
email
last changed	2024/04/22 07:10

_id	sigradi2022_62
id	sigradi2022_62
authors	Mateus, Daniel; Henriques, Gonçalo Castro; Eskinazi, Mara; Menna, Ronaldo Lee; Nepomuceno, Taiane Melo
year	2022
title	Carioca modern facades: expanding passive shading systems through computational methods
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. 127–138
summary	In the 1940s, modern Rio de Janeiro architects developed passive systems to improve buildings performance, without resorting to air conditioning systems. This research studies the performance of a set of eight buildings, from the Carioca School, investigating in a prospective sense how to improve their performance through computational methods. The authors modelled the eight buildings and analysed as a case study the Nova Cintra building performance, regarding insolation and illuminance, using the environmental software Ladybug and Honeybee. Based on the simulation data, they used combinatorial modeling to change the position of each of the three shading type’s modules of the north facade of Nova Cintra, to improve their overall performance. Results confirm that is possible to continue to improve the buildings performance, as already accomplished by the modern buildings, using computational methods to improve, reducing also energy consumption through natural systems and diminishing the need for artificial air conditioning systems.
keywords	Generative Design, Shading performance, Insolation and illuminance analysis, Combinatorial modeling, Carioca modern facades
series	SIGraDi
email
last changed	2023/05/16 16:55

_id	ecaade2023_227
id	ecaade2023_227
authors	Moorhouse, Jon and Freeman, Tim
year	2023
title	Towards a Genome for Zero Carbon Retrofit of UK Housing
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. 197–206
doi	https://doi.org/10.52842/conf.ecaade.2023.2.197
summary	The United Kingdom has some of the worst insulated housing stock in Northern Europe. This is in part due to the age of housing in the UK, with over 90% being built before 1990 [McCrone 2017, Piddington 2020]. Moreover, 85% of current UK housing will still be in use in 2050 by which stage their Government are targeting Net Carbon Zero [Eyre 2019]. Domestic energy use accounts for around 25% of UK carbon emissions. The UK will need to retrofit 20 million dwellings in order to meet this target. If this delivery were evenly spread, it would equate to over 2,000 retrofit completions each day. Government-funded initiatives are stimulating the market, with upwards of 60,000 social housing retrofits planned for 2023, but it is clear that a system must be developed to enable the design and implementation of housing-stock improvement at a large scale.This paper charts the 20-year development of a digital approach to the design for low-carbon domestic retrofit by architects Constructive Thinking Studio Limited and thence documents the emergence of a collaborative approach to retrofit patterns on a National scale. The author has led the Research and Development stream of this practice, developing a Building Information Modelling methodology and integrated Energy Modelling techniques to optimise design for housing retrofit [Georgiadou 2019, Ben 2020], and then inform a growing palette of details and a database of validated solutions [Moorhouse 2013] that can grow and be used to predict options for future projects [D’Angelo 2022]. The data is augmented by monitoring energy and environmental performance, enabling a growing body of knowledge that can be aligned with existing big data to simulate the benefits of nationwide stock improvement. The paper outlines incremental case studies and collaborative methods pivotal in developing this work The proposed outcome of the work is a Retrofit Genome that is available at a national level.
keywords	Retrofit, Housing, Zero-Carbon, BIM, Big Data, Design Genome
series	eCAADe
email
last changed	2023/12/10 10:49

_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	ecaade2022_217
id	ecaade2022_217
authors	Panagiotidou, Vasiliki and Koerner, Andreas
year	2022
title	From Intricate to Coarse and Back - A voxel-based workflow to approximate high-res geometries for digital environmental simulations
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. 491–500
doi	https://doi.org/10.52842/conf.ecaade.2022.1.491
summary	Digital environmental simulations can present a computational bottleneck concerning the complexity of geometry. Therefore, a series of workarounds, ranging from cloud-based solutions to machine learning simulations as surrogate simulations are conventionally applied in practice. Concurrently, contemporary advances in procedural modelling in architecture result in design concepts with high polygon counts. This leads to an ever- increasing resolution discrepancy between design and analysis models. Responding to this problem, this research presents a step-by-step approximation workflow for handling and transferring high-resolution geometries between procedural modelling and environmental simulation software. The workflow is intended to allow designers to quickly assess a design’s interaction with environmental parameters such as airflow and solar radiation and further articulate them. A controllable voxelization procedure is applied to approximate the original geometry and therefore reduce the resolution. Controllable in this context refers to the user’s ability to locally adjust the voxel resolution to fit design needs. After export and simulation, 3d results are imported back into the design environment. The colour properties are re-mapped onto the original high- resolution geometry following a weighted proximity technique. The developed data transfer pipeline allows designers to integrate environmental analysis during initial design steps, which is essential for accessibility in the design profession. This can help to environmentally inform generative designs as well as to make simulation workflows more accessible when working with a wider range of geometries. In this, it reduces the perceived discrepancy between the concept and simulation model. This eases the use and allows a wider audience of users to develop co-creation processes between computation, architecture, and environment.
keywords	Simulation, Accessibility, Computation, Environmental Data, Workflow
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_448
id	ecaade2022_448
authors	Papanikolaou, Kyratsoula-Tereza, Liapi, Katherine and Sibetheros, Ioannis
year	2022
title	Environmental Impact Assessment and Visualization of Rain-Water Best Management Practices for Urban Blocks - An "architect-friendly" simulation model
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. 75–82
doi	https://doi.org/10.52842/conf.ecaade.2022.2.075
summary	In order to implement stormwater best management practices (BMPs) in urban blocks in Greece and other cities with warm and dry climates, such as green roofs, porous pavements etc., it is crucial that architects are able to assess their environmental impact during the design process in an efficient and simple way, without the requirement of an in depth understanding of the complex hydrological processes. To achieve the above, an “architect-friendly” computer-based model, under development by the authors, is presented. The model can be used as a decision support tool by allowing an assessment of the efficacy of non-conventional, water-sensitive, stormwater management strategies in an urban environment, measured by the stormwater runoff mitigation and temperature decrease. Wind flow simulation data from an external CFD model can be integrated into the proposed model, in order to visualize wind flow patterns in selected urban blocks. The user is able to select different stormwater BMPs from a BMP library and apply them on the 3D urban block model, in order to achieve an improved “water sensitive” state. The ENVI-MET plugin for Rhino is used for simulating temperature decrease and the SCS Curve Number method for determining stormwater runoff reduction, caused by each BMP application. The visualization of the results in the graphical interface of the Grasshopper programming environment facilitates the study of the environmental impact of stormwater BMPs in urban blocks and the comparison of different stormwater management scenarios. Several urban blocks in Athens will be used as case studies to test the proposed model and assess the efficiency of the visualization process.
keywords	Stormwater Best Management Practices, Urban Blocks, Runoff Mitigation, Temperature Reduction, Decision Support Tool, Environmental Impact Visualization
series	eCAADe
email
last changed	2024/04/22 07:10

_id	caadria2022_118
id	caadria2022_118
authors	Reitberger, Roland, Banihashemi, Farzan and Lang, Werner
year	2022
title	Sensitivity and Uncertainty Analysis of Combined Building Energy Simulation and Life Cycle Assessment, Implications for the Early Urban Design Process
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. 629-638
doi	https://doi.org/10.52842/conf.caadria.2022.2.629
summary	Life Cycle Assessment (LCA) is a suitable approach for evaluating environmental impact (e.g. Global Warming Potential (GWP)) related to construction elements and building operation. Since both contribute significantly to the lifecycle based GWP of buildings, combined consideration is necessary. This applies especially for the early design stages when measures for climate change mitigation can be implemented in a cost-efficient manner. In this paper, we describe a sensitivity and uncertainty analysis (SA/UA) for energy simulation and LCA with a total of 8,000 parameter combinations. Thereby, we investigated valuable input for the setup of a collaborative design process with limited information. Standardised Regression Coefficients (SRCs) were used to obtain sensitivity and resulting uncertainties were investigated. The results indicate Primary Energy Source (PES), compactness and energy standard to be the most important information for the robustness of the combined LCA approach. Uncertainty can be reduced by e.g. defining the energy system in an early stage or by designing compact buildings. Related to the early design stages, the application of combined approaches for SA and UA is recommended, as the results differ for embodied and operational emissions.
keywords	early design stages, Sensitivity Analysis (SA), Uncertainty Analysis (UA), Life Cycle Assessment (LCA), urban scale, synergy potential, SDG 13
series	CAADRIA
email
last changed	2022/07/22 07:34

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