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_153
id	caadria2022_153
authors	Cheng, Cesar, Li, Yuke, Deshpande, Rutvik, Antonio, Rishan, Chavan, Tejas, Nisztuk, Maciej, Subramanian, Ramanathan, Weijenberg, Camiel and Patel, Sayjel Vijay
year	2022
title	Realtime Urban Insights for Bottom-up 15-minute City Design
doi	https://doi.org/10.52842/conf.caadria.2022.1.435
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. 435-444
summary	This paper introduces a real-time neighbour scoring system, using data collected from various web-based APIs, to facilitate "15-minute city‚ designs. The system extends on the current state of the art in three ways; first, it incorporates a multi-source urban API, to automate the extraction of location-based information from online sources; second, it provides a quantitative method to calculate and index "15-minute city‚ performance; and third, it provides a web-based application, to allow real-time feedback of neighbourhood design performance complementing the design refinements at a building and tenancy level. In addition to discussing its theoretical basis, and technical implementation, this paper provides a case study to demonstrate how the neighbourhood scoring system is incorporated into the design of a hypothetical mixed-use urban development.
keywords	Industry Innovation and Infrastructure, Sustainable Cities and Communities, Urban Walkability, Urban Accessibility, 15-minute City, Spatial Analysis, SDG 9, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_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	caadria2022_194
id	caadria2022_194
authors	Cheung, Ling Kit, Xu, Zhitao, Chen, Pei and Makki, Mohammed
year	2022
title	An Alternative Model for Urban Renewal: A Generative Approach to the (Re)-Development of Xian Village
doi	https://doi.org/10.52842/conf.caadria.2022.1.181
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. 181-190
summary	The impact of urban renewal, specifically in countries experiencing rapid urbanisation due to population growth, has resulted in the erasure of urban culture and heritage in favour of repetitive homogeneity that has been synonymous with 20th century modernist planning models. One such region experiencing this rapid urban renewal is the Guangzhou region in southern China. The presented experiments examine Xian Village in Guangzhou, a culturally rich urban tissue currently experiencing redevelopment, and proposes an alternative model for urban renewal, employing a bottom-up approach to urban growth through the use of a multi-objective evolutionary model; presenting a model that integrates historic and existing urban characteristics adapted to future development plans.
keywords	China, Guangzhou, Xian Village, Village in the City, Urban Renewal, Cultural and Heritage Preservation, Multi-Objective Evolutionary Algorithm (MOEA), SDG 10, SDG 11, SDG 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_336
id	caadria2022_336
authors	Araujo, Goncalo, Santos, Luis, Leitao, Antonioand Gomes, Ricardo
year	2022
title	AD-Based Surrogate Models for Simulation and Optimization of Large Urban Areas
doi	https://doi.org/10.52842/conf.caadria.2022.2.689
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. 689-698
summary	Urban Building Energy Model (UBEM) approaches help analyze the energy performance of urban areas and predict the impact of different retrofit strategies. However, UBEM approaches require a high level of expertise and entail time-consuming simulations. These limitations hinder their successful application in designing and planning urban areas and supporting the city policy-making sector. Hence, it is necessary to investigate alternatives that are easy-to-use, automated, and fast. Surrogate models have been recently used to address UBEM limitations; however, they are case-specific and only work properly within specific parameter boundaries. We propose a new surrogate modeling approach to predict the energy performance of urban areas by integrating Algorithmic Design, UBEM, and Machine Learning. Our approach can automatically model and simulate thousands of building archetypes and create a broad surrogate model capable of quickly predicting annual energy profiles of large urban areas. We evaluated our approach by applying it to a case study located in Lisbon, Portugal, where we compare its use in model-based optimization routines against conventional UBEM approaches. Results show that our approach delivers predictions with acceptable accuracy at a much faster rate.
keywords	urban building energy modelling, algorithmic design, machine learning in Architecture, optimization of urban areas, SDG 7, SDG 12, SDG 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_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_272
id	caadria2022_272
authors	Dong, Zhiyong
year	2022
title	Perceiving Fabric Immersed in Time, an Exploration of Urban Cognitive Capabilities of Neural Networks
doi	https://doi.org/10.52842/conf.caadria.2022.1.263
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. 263-272
summary	City develops gradually with the lapse of time. Cities, as a ‚container‚, are injected new urban elements along the trajectory of the times and the progress of human civilization, constructing the historical structures involved past, present and future. Thus, the cultural information of each era is preserved in the urban fabric together and urban fabric features are complex and rich, which are difficult to capture in traditional design methods. In this paper, we try to use Generative Adversarial Networks (GAN), one of the neural network algorithms, to explore the inner rules of complex urban morphological features and realize the perception of the urban fabric. Neural networks are innovatively applied to the larger and more complex city generation in this experiment. First, we collect European urban fabric as the dataset, then label data to facilitate machine training, use GAN to learn the feature of the dataset by adjusting parameters, and analyze the effect of the generated results. The automatic feature learning capability of the neural networks is used to summarize the inherent patterns and rules in urban development which is difficult for human to discover.
keywords	Deep Learning, Generative Adversarial Networks, Generative Design, Morphology Cognition, Urban Fabric, 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	caadria2022_312
id	caadria2022_312
authors	Forster, Nick, Schubert, Gerhard and Petzold, Frank
year	2022
title	Rebugging the Smart City. Design Explorations of Digital Urban Infrastructure
doi	https://doi.org/10.52842/conf.caadria.2022.1.635
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. 635-644
summary	Smart Cities are presented as a straightforward solution to diverse urban problems. On a closer look, however, the discourse on ‚Smart Cities‚ seems wicked in various ways: vaguely defined, speculative, and fragmented into incommensurable positions. Focussing on this ‚wickedness,‚ we explore the potential of design approaches to pervade the obscurities and discursive segregations around digital urban infrastructure. Insights from critical design theory lead us to an engagement with digital design not only as validation and enhancement of Smart City projects but as contingent and political exploration. Design becomes an investigation and remaking of what a ‚Smart City‚ means in a concrete context. Hence, this approach allows an intersection of social and technical, affirmative and critical perspectives. We explore this approach through an experimental workshop. Hence, we discuss the unfolding of two design engagements: the reframing of ‚Smart Lighting‚ as cosmopolitical controversy and the hacking of pedestrian navigation as urban exploration. This approach shows a double potential: On the one hand, it makes digital design practices aware of their ambiguous and political effects. On the other, we scrutinise the possibility of sociotechnical design perspectives as a research approach towards ‚Smart City‚ projects and digital urban infrastructure.
keywords	smart city, design theory, prototyping, digital infrastructure, urban studies, critical making, speculative design, SDG 9, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_196
id	caadria2022_196
authors	Grisiute, Ayda, Shi, Zhongming, Chadzynski, Arkadiusz, Silvennoinen, Heidi, von Richthofen, Aurel and Herthogs, Pieter
year	2022
title	Automated Semantic SWOT Analysis for City Planning Targets: Data-driven Solar Energy Potential Evaluations for Building Plots in Singapore
doi	https://doi.org/10.52842/conf.caadria.2022.1.555
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. 555-564
summary	Singapore‚s urban planning and management is cross-domain in nature and need to be assessed using multi-domain indicators ‚ such as SDGs. However, urban planning processes are often confronted with data interoperability issues. In this paper, we demonstrate how a Semantic Web Technology-based approach combined with a SWOT analysis framework can be used to develop an architecture for automated multi-domain evaluations of SDG-related planning targets. This paper describes an automated process of storing heterogeneous data in a semantic data store, deriving planning metrics and integrating a SWOT framework for the multi-domain evaluation of on-site solar energy potential across plots in Singapore. Our goal is to form the basis for a more comprehensive planning support tool that is based on a reciprocal relationship between innovations in SWT and a versatile SWOT framework. The presented approach has many potential applications beyond the presented energy potential evaluation.
keywords	Semantic Web, Knowledge Graphs, SWOT analysis, energy-driven urban design, SDG 11, SDG 7
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_140
id	caadria2022_140
authors	Huang, Shuyi and Zheng, Hao
year	2022
title	Morphological Regeneration of the Industrial Waterfront Based on Machine Learning
doi	https://doi.org/10.52842/conf.caadria.2022.1.475
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. 475-484
summary	The regeneration of the industrial waterfront is a global issue, and its significance lies in transforming the waterfront brownfield into an eco-friendly, hospitable, and vibrant urban space. However, the industrial waterfront naturally has comparatively unmanageable morphological features, including linear shape, irregular waterfront boundary, and separation with urban networks. Therefore, how to subdivide the vacant land and determine the land-use type for each subdivision becomes a challenging problem. Accordingly, this study proposes an application of machine learning models. It allows the generation of morphological elements of the vacant industrial waterfront by comparing the before-and-after scenarios of successful regeneration projects. The data collected from New York City is used as a showcase of this method.
keywords	machine learning, urban morphology, industrial waterfront regeneration, sustainable cities, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_388
id	caadria2022_388
authors	Leong, Siew Leng and Janssen, Patrick
year	2022
title	Participatory Planning: Heritage Conservation Through Co-design and Co-decision
doi	https://doi.org/10.52842/conf.caadria.2022.2.505
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. 505-514
summary	Citizen participation in urban planning and architectural design has been long discussed and experimented with since the 1960s. With existing participatory design approaches, two key challenges can be identified. First, the power of citizens to directly affect the decision-making processes is typically quite limited. Second, the use of traditional face-to-face design workshop results in low levels of participation. This paper proposes an innovative participatory design approach with a focus on co-design and co-decision. The co-design stage provides citizens with a tool that empowers them to think critically of their built environment and to initiate design development in their own city. The co-decision stage gives citizens real power in determining the future changes to their city by embedding the participatory design approach into the planning permission system. This participatory design approach is implemented through a web application that allows participants to view design proposals within the existing site context from a birds-eye views and from multiple immersive views, leading to a better understanding of the design proposal‚s scale and impact. The design proposal viewer has been demonstrated on a heritage site in Singapore, showing its potential to be used as evidence for supporting or rejecting design proposals.
keywords	Participatory Planning, Co-design and Co-decision, Citizen Power, Visualisation Method, Bird's-eye View, Immersive View, Web Application, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_421
id	caadria2022_421
authors	Ng, Provides, Doria, David, Odaibat, Baha, Fernandez, Alberto and Karastathi, Nikoletta
year	2022
title	Decentralised Solar Economy: Unattended and Smart Solar Energy Urban System (UnSSEUS)
doi	https://doi.org/10.52842/conf.caadria.2022.2.759
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. 759-768
summary	Planners often go out of the city when planting large-scale solar farms due to requirements for huge, flat surface areas. This reduces urban proximity to renewable energy sources, causing dissipation during energy transfer and a waste in solar energy unused within urban areas. This paper aims at understanding the prospect and challenges in transforming buildings from passively consuming energy to actively generating energy for cities. As every building has a different renewable energy capacity, how may we re-distribute power amongst a network of users, forming a socio-economy around distributed power generation? This paper first presents its theoretical approach learning from fields of biology and information theory as a source of inspiration for its design methodology. It then presents a context study of Hong Kong and its Feed-in Tariff scheme that incentivizes distributed power generation, and identifies the challenges. Afterwards, it defines ‚Unattended and Smart Solar Energy Urban System‚ and proposes the parameters which the system should comprehend on its dashboard for demand-side management of energy. Finally, preliminary results of using a sudoku algorithm in distributing time and pricing factors of energy exchange are presented. This on-going research project aims at SDG goals 7 and 11.
keywords	Distributed Power Generation, Sudoku Gameplay, Unattended and Smart, Solar Energy, Urban System, SDG 7, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_260
id	caadria2022_260
authors	Ricafort, Kim, Koch, Ethan and Makki, Mohammed
year	2022
title	Addressing Flood Resilience In Jakarta‚s Kampungs Through The Use Of Sequential Evolutionary Simulations
doi	https://doi.org/10.52842/conf.caadria.2022.1.655
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. 655-664
summary	The urban superblock of Kampung Melayu, located in Jakarta, Indonesia, is a typology amalgamated by the environmental and infrastructural challenges caused by Jakarta‚s urban sprawl. Rapid and unregulated urban growth, fluctuating tropical conditions, rising sea levels and unprecedented environmental stresses have led to a city that is sinking, leaving unregulated low-income settlements, such as Kampung Melayu, most vulnerable. To address these issues, the presented research employs the use of a multi-objective evolutionary algorithm for an in-depth analysis of the various relationships within the urban fabric. The simulations present an alternative urban approach to the design of a flood resilient Kampung; addressing environmental and demographic stresses while maintaining the irregularity that has become ingrained in the history of the urban form.
keywords	jakarta, kampung melayu, sequential simulations, evolutionary algorithm, computational design, urban growth, flood resilience, SDG 3, SDG 6, SDG 10, SDG 11, SDG 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ascaad2022_099
id	ascaad2022_099
authors	Sencan, Inanc
year	2022
title	Progeny: A Grasshopper Plug-in that Augments Cellular Automata Algorithms for 3D Form Explorations
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. 377-391
summary	Cellular automata (CA) is a well-known computation method introduced by John von Neumann and Stanislaw Ulam in the 1940s. Since then, it has been studied in various fields such as computer science, biology, physics, chemistry, and art. The Classic CA algorithm is a calculation of a grid of cells' binary states based on neighboring cells and a set of rules. With the variation of these parameters, the CA algorithm has evolved into alternative versions such as 3D CA, Multiple neighborhood CA, Multiple rules CA, and Stochastic CA (Url-1). As a rule-based generative algorithm, CA has been used as a bottom-up design approach in the architectural design process in the search for form (Frazer,1995; Dinçer et al., 2014), in simulating the displacement of individuals in space, and in revealing complex relations at the urban scale (Güzelci, 2013). There are implementations of CA tools in 3D design software for designers as additional scripts or plug-ins. However, these often have limited ability to create customized CA algorithms by the designer. This study aims to create a customizable framework for 3D CA algorithms to be used in 3D form explorations by designers. Grasshopper3D, which is a visual scripting environment in Rhinoceros 3D, is used to implement the framework. The main difference between this work and the current Grasshopper3D plug-ins for CA simulation is the customizability and the real-time control of the framework. The parameters that allow the CA algorithm to be customized are; the initial state of the 3D grid, neighborhood conditions, cell states and rules. CA algorithms are created for each customizable parameter using the framework. Those algorithms are evaluated based on the ability to generate form. A voxel-based approach is used to generate geometry from the points created by the 3D cellular automata. In future, forms generated using this framework can be used as a form generating tool for digital environments.
series	ASCAAD
email
last changed	2024/02/16 13:38

_id	caadria2022_267
id	caadria2022_267
authors	Toohey, Gabrielle, Nguyen, Tommy Bao Nghi, Vilppola, Ritva, Qiu, Waishan, Li, Wenjing and Luo, Dan
year	2022
title	Data-Driven Evaluation of Streets to Plan for Bicycle Friendly Environments: A Case Study of Brisbane Suburbs
doi	https://doi.org/10.52842/conf.caadria.2022.1.243
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. 243-252
summary	Empirical cycling data from across the world illustrates the many barriers that car-dependent cities face when implementing cycling programs and infrastructure. Most studies focus on physical criteria, while perception criteria are less addressed. The correlations between the two are still largely unknown. This paper introduces a methodology that utilises computer vision analysis techniques to evaluate 15,383 Google Street View Images (SVI) of Brisbane City against both physical and perception cycling criteria. The study seeks to better understand correlations between the quality of a street environment and an urban area's 'bicycle-friendliness'. PSPNet Image Segmentation is utilised against SVIs to determine the percentage of an image corresponding with objects and the environment related to specific cycling factors. For physical criteria, these images are then further analysed by Masked RCNN processes. For perception criteria, subjective ranking of the images is undertaken using Machine Learning (ML) techniques to score images based on survey data. The methodology effectively allows for current findings in cycling research to be further utilised in combination via computer visioning (CV) and ML applications to measure different physical elements and urban design qualities that correspond with bicycle-friendliness. Such findings can assist targeted design strategies for cities to encourage the use of safer and more sustainable modes of transport.
keywords	Bicycle-friendly, Quality Streetscapes, Active Living, Visual Assessment, Computer Visioning, Machine Learning, SDG 3, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_172
id	caadria2022_172
authors	Xiao, Yahan, Hotta, Akito, Fuji, Takaaki, Kikuzato, Naoto and Hotta, Kensuke
year	2022
title	Urban Scale 3 Dimensional CFD Approximation Based on Deep Learning A Quick Air Flow Prediction for Volume Study in Architecture Early Design Stage
doi	https://doi.org/10.52842/conf.caadria.2022.1.303
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. 303-312
summary	The CFD generated by an object and its surroundings is critical during architectural design. The most common method of CFD calculation is to discretize the spatial region into small cells to form a three-dimensional grid or grid point and then apply a suitable algorithm to solve the equation iteratively until the steady state, which usually takes a significant amount of time before it converges to the exact solution of the problem. Deep learning is a subset of a Machine Learning algorithm that uses multiple layers of neural networks to perform in processing data and computations on a large amount of data. This paper presents a deep learning model CNN architecture to provide a quick and approximated 3-dimensional solution for the CFD. Our network speeds up 45 times compared to the standard CFD solver. Moreover, our network is able to predict a CFD in which the wind inlet and outlet appear at the same surface of a wind tunnel.
keywords	Urban Microclimate, Machine Learning, 3D Unet, Residual Block, 3 Dimensional CFD Prediction, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_169
id	caadria2022_169
authors	Xu, Hang and Wang, Tsung-Hsien
year	2022
title	An Integrated Parametric Generation and Computational Workflow to Support Sustainable City Planning
doi	https://doi.org/10.52842/conf.caadria.2022.1.535
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. 535-544
summary	To examine how efforts in the built environment can contribute to global climate change mitigation at the urban scale, urban building energy modelling (UBEM) is one of the research areas gaining increasing interest in recent years. However, limited studies systematically illustrate a comprehensive UBEM workflow for most architects and urban planners considering available public datasets, particularly at the early conceptual design phase. In current UBEM studies, major challenges arise from the lack of fine-grained measured urban data and incompatibility between software. To address these challenges and support future sustainable cities and communities, this paper proposed a streamlined computational workflow of UBEM to facilitate sustainable urban design development. Through a case study of Sheffield in the UK, this paper demonstrated an automated and standardised computational workflow that can test the decarbonisation potential in built environments by evaluating energy demand and supply scenarios at an urban scale. This workflow is envisaged to be applicable at various scales of an urban region given an appropriate geographic information system (GIS) dataset.
keywords	Parametric Design Generation, Urban Sustainability, Urban Building Energy Modelling, Building Performance Simulation, Renewable Energy, Decarbonisation, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_199
id	caadria2022_199
authors	Yang, Qing, Cao, Chufan, Li, Haimiao, Qiu, Waishan, Li, Wenjing and Luo, Dan
year	2022
title	Quantifying the Coherence and Divergence of Planned, Visual and Perceived Streets Greening to Inform Ecological Urban Planning
doi	https://doi.org/10.52842/conf.caadria.2022.1.565
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. 565-574
summary	This research attempts to combine the fields of urban planning, urban design and cognitive psychology, and propose three corresponding evaluation indicators for urban ecology, and further explore the coherence and divergence between them. This research defines land vegetation coverage, visibility of street green vegetation, and people's green perception as planned green, visual green and perceived green. Specifically, the three measures (i.e., planned, visual and perceived) refer to objectively extracting park lands and canopy areas from land use data, objectively extracting green pixels from street views, and subjectively collected through visual surveys. This study hypothesizes that there could exist large variation between the three measures, which would provide distinct implications for city planners. To test our hypothesis, this study selects Brisbane as the research area, effectively using computer deep learning, data visualization and mathematical statistics methods to achieve an accurate description of the three sets of data, and proposes a comprehensive evaluation of the urban ecological theory system. The results show the credibility and scope of application of the three types of greening, and quantitatively proposed and tested the relevant theories of urban design.
keywords	Urban Green Space, Urban Ecology, Street View Image, Green Perception, Subjective Measure, SDG 3, SDG 11, SDG 13
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_130
id	caadria2022_130
authors	Yu, Junah and Min, Deedee
year	2022
title	PL-System: Visual Representation of Pattern Language using L-System
doi	https://doi.org/10.52842/conf.caadria.2022.1.201
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. 201-210
summary	Pattern Language provides simple and conveniently formatted solutions to complex design problems ranging from urban planning to interior design for community-led inclusive designs. Despite the intention, the concept has been more widely adopted by computer science professionals. One possible reason is the lack of visualization, making it difficult to be used interactively by the non-professionals. To overcome the issue, we aim to integrate the patterns from Pattern Language into the L-system to visualize the paper architecture into geometric forms. Specifically, we implement Pattern L-System (PL-System), which generates diagrammatic floor plans using design rules based on the pattern languages. We first made analogical comparisons of the concepts and grammar structure between Pattern Language and the L-system. Next, we defined a geometric interpreter for drawing diagrammatic floor plans using turtle graphics, which consist of geometrical rules for putting shapes together. Then, we selected three patterns and reinterpreted them for visualization using strings of turtle graphics letters that determine the turtle‚s movements for the geometric representation of walls, columns, and doors. From this research process, we learned that Modular L-system opens up the possibility for the visualization of the patterns in Pattern Language.
keywords	Pattern Language, L-System, Diagrammatic Floor Plan, Turtle Graphics, Geometric Forms, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

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