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	acadia20_94
id	acadia20_94
authors	Yoo, Wonjae; Kim, Hyoungsub; Shin, Minjae; J.Clayton, Mark
year	2020
title	BIM-Based Automatic Contact Tracing System Using Wi-Fi
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 94-101.
doi	https://doi.org/10.52842/conf.acadia.2020.1.094
summary	This study presents a BIM-based automatic contact tracing method using a stations-oriented indoor localization (SOIL) system. The SOIL system integrates BIM models and existing network infrastructure (i.e., Wi-Fi), using a clustering method to generate roomlevel occupancy schedules. In this study, we improve the accuracy of the SOIL system by including more detailed Wi-Fi signal travel sources, such as reflection, refraction, and diffraction. The results of field measurements in an educational building show that the SOIL system was able to produce room-level occupant location information with a 95.6% level of accuracy. This outcome is 2.6% more accurate than what was found in a previous study. We also describe an implementation of the SOIL system for conducting contact tracing in large buildings. When an individual is confirmed to have COVID-19, public health professionals can use this system to quickly generate information regarding possible contacts. The greatest strength of this SOIL implementation is that it has wide applicability in largescale buildings, without the need for additional sensing devices. Additional tests using buildings with multiple floors are required to further explore the robustness of the system.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2020_222
id	caadria2020_222
authors	Sun, Chengyu and Hu, Wei
year	2020
title	A Rapid Building Density Survey Method Based on Improved Unet
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 649-658
doi	https://doi.org/10.52842/conf.caadria.2020.2.649
summary	How to rapidly obtain building density information in a large range is a key problem for architecture and planning. This is because architectural design or urban planning is not isolated, and the environment of the building is influenced by the distribution of other buildings in a larger area. For areas where building density data are not readily available, the current methods to estimate building density are more or less inadequate. For example, the manual survey method is relatively slow and expensive, the traditional satellite image processing method is not very accurate or needs to purchase high-precision multispectral remote sensing image from satellite companies. Based on the deep neural network, this paper proposes a method to quickly extract large-scale building density information by using open satellite images platforms such as Baidu map, Google Earth, etc., and optimizes the application in the field of building and planning. Compared with the traditional method, it has the advantages of less time and money, higher precision, and can provide data support for architectural design and regional planning rapidly and conveniently.
keywords	building density; rapidly and conveniently; neural network
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	acadia20_228
id	acadia20_228
authors	Alawadhi, Mohammad; Yan, Wei
year	2020
title	BIM Hyperreality
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 228-236.
doi	https://doi.org/10.52842/conf.acadia.2020.1.228
summary	Deep learning is expected to offer new opportunities and a new paradigm for the field of architecture. One such opportunity is teaching neural networks to visually understand architectural elements from the built environment. However, the availability of large training datasets is one of the biggest limitations of neural networks. Also, the vast majority of training data for visual recognition tasks is annotated by humans. In order to resolve this bottleneck, we present a concept of a hybrid system—using both building information modeling (BIM) and hyperrealistic (photorealistic) rendering—to synthesize datasets for training a neural network for building object recognition in photos. For generating our training dataset, BIMrAI, we used an existing BIM model and a corresponding photorealistically rendered model of the same building. We created methods for using renderings to train a deep learning model, trained a generative adversarial network (GAN) model using these methods, and tested the output model on real-world photos. For the specific case study presented in this paper, our results show that a neural network trained with synthetic data (i.e., photorealistic renderings and BIM-based semantic labels) can be used to identify building objects from photos without using photos in the training data. Future work can enhance the presented methods using available BIM models and renderings for more generalized mapping and description of photographed built environments.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2020_089
id	ecaade2020_089
authors	Ardic, Sabiha Irem, Kirdar, Gulce and Lima, Angela Barros
year	2020
title	An Exploratory Urban Analysis via Big Data Approach: Eindhoven Case - Measuring popularity based on POIs, accessibility and perceptual quality parameters
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 309-318
doi	https://doi.org/10.52842/conf.ecaade.2020.2.309
summary	The cities are equipped with the data as a result of the individuals' sharings and application usage. This significant amount of data has the potential to reveal relations and support user-centric decision making. The focus of the research is to examine the relational factors of the neighborhoods' popularity by implementing a big data approach to contribute to the problem of urban areas' degradation. This paper presents an exploratory urban analysis for Eindhoven at the neighborhood level by considering variables of popularity: density and diversity of points of interest (POI), accessibility, and perceptual qualities. The multi-sourced data are composed of geotagged photos, the location and types of POIs, travel time data, and survey data. These different datasets are evaluated using BBN (Bayesian Belief Network) to understand the relationships between the parameters. The results showed a positive and relatively high connection between popularity - population change, accessibility by walk - density of POIs, and the feeling of safety - social cohesion. For further studies, this approach can contribute to the decision-making process in urban development, specifically in real estate and tourism development decisions to evaluate the land prices or the hot-spot touristic places.
keywords	big data approach; neighborhood analysis; popularity; point of interest (POI); accessibility; perceptual quality
series	eCAADe
email
last changed	2022/06/07 07:54

_id	acadia20_74
id	acadia20_74
authors	Bucklin, Oliver; Born, Larissa; Körner, Axel; Suzuki, Seiichi; Vasey, Lauren; T. Gresser, Götz; Knippers, Jan; Menges,
year	2020
title	Embedded Sensing and Control
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 74-83.
doi	https://doi.org/10.52842/conf.acadia.2020.1.074
summary	This paper investigates an interactive and adaptive control system for kinetic architectural applications with a distributed sensing and actuation network to control modular fiber-reinforced composite components. The aim of the project was to control the actuation of a foldable lightweight structure to generate programmatic changes. A server parses input commands and geometric feedback from embedded sensors and online data to drive physical actuation and generate a digital twin for real-time monitoring. Physical components are origami-like folding plates of glass and carbon-fiber-reinforced plastic, developed in parallel research. Accelerometer data is analyzed to determine component geometry. A component controller drives actuators to maintain or move towards desired positions. Touch sensors embedded within the material allow direct control, and an online user interface provides high-level kinematic goals to the system. A hierarchical control system parses various inputs and determines actuation based on safety protocols and prioritization algorithms. Development includes hardware and software to enable modular expansion. This research demonstrates strategies for embedded networks in interactive kinematic structures and opens the door for deeper investigations such as artificial intelligence in control algorithms, material computation, as well as real-time modeling and simulation of structural systems.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2020_118
id	caadria2020_118
authors	Chow, Ka Lok and van Ameijde, Jeroen
year	2020
title	Generative Housing Communities - Design of Participatory Spaces in Public Housing Using Network Configurational Theories
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 283-292
doi	https://doi.org/10.52842/conf.caadria.2020.2.283
summary	This research-by-design project explores how public housing estates can accommodate social diversity and the appropriation of shared spaces, using qualitative and quantitative analysis of circulation networks. A case study housing estate in Hong Kong was analysed through field observations of movements and activities and as a site for the speculative re-design of shared spaces. Generative design processes were developed based on several parameters, including shortest paths, visibility integration and connectivity integration (Hillier & Hanson, 1984). Additional tools were developed to combine these techniques with optimisation of sunlight access, maximisation of views for residential towers and the provision of permeability of ground level building volumes. The project demonstrates how flexibility of use and social engagement can constitute a platform for self-organisation, similar to Jane Jacobs' notion of vibrant streets leading to active and progressive communities. It shows how computational design and configurational theories can promote a bottom-up approach for generating new types of residential environments that support participatory and diverse communities, rather than a conventional top-down approach that is perceived to embody mechanisms of social regimentation.
keywords	Urban Planning and Design; Network Configuration; Community Space and Social Interaction; Hong Kong Public Housing
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	caadria2020_402
id	caadria2020_402
authors	Ezzat, Mohammed
year	2020
title	A Framework for a Comprehensive Conceptualization of Urban Constructs - SpatialNet and SpatialFeaturesNet for computer-aided creative urban design
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 111-120
doi	https://doi.org/10.52842/conf.caadria.2020.2.111
summary	Analogy is thought to be foundational for designing and for design creativity. Nonetheless, practicing analogical reasoning needs a knowledge-base. The paper proposes a framework for constructing a knowledge-base of urban constructs that builds on an ontology of urbanism. The framework is composed of two modules that are responsible for representing either the concepts or the features of any urban constructs' materialization. The concepts are represented as a knowledge graph (KG) named SpatialNet, while the physical features are represented by a deep neural network (DNN) called SpatialFeaturesNet. For structuring SpatialNet, as a KG that comprehensively conceptualizes spatial qualities, deep learning applied to natural language processing (NLP) is employed. The comprehensive concepts of SpatialNet are firstly discovered using semantic analyses of nine English lingual corpora and then structured using the urban ontology. The goal of the framework is to map the spatial features to the plethora of their matching concepts. The granularity Ã nd the coherence of the proposed framework is expected to sustain or substitute other known analogical, knowledge-based, inspirational design approaches such as case-based reasoning (CBR) and its analogical application on architectural design (CBD).
keywords	Domain-specific knowledge graph of urban qualities; Deep neural network for structuring KG; Natural language processing and comprehensive understanding of urban constructs; Urban cognition and design creativity; Case-based reasoning (CBR) and case-based design (CBD)
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	artificial_intellicence2019_207
id	artificial_intellicence2019_207
authors	Hao Zheng
year	2020
title	Form Finding and Evaluating Through Machine Learning: The Prediction of Personal Design Preference in Polyhedral Structures
source	Architectural Intelligence Selected Papers from the 1st International Conference on Computational Design and Robotic Fabrication (CDRF 2025)
doi	https://doi.org/https://doi.org/10.1007/978-981-15-6568-7_13
summary	3D Graphic Statics (3DGS) is a geometry-based structural design and analysis method, helping designers to generate 3D polyhedral forms by manipulating force diagrams with given boundary conditions. By subdividing 3D force diagrams with different rules, a variety of forms can be generated, resulting in more members with shorter lengths and richer overall complexity in forms. However, it is hard to evaluate the preference toward different forms from the aspect of aesthetics, especially for a specific architect with his own scene of beauty and taste of forms. Therefore, this article proposes a method to quantify the design preference of forms using machine learning and find the form with the highest score based on the result of the preference test from the architect. A dataset of forms was firstly generated, then the architect was asked to keep picking a favorite form from a set of forms several times in order to record the preference. After being trained with the test result, the neural network can evaluate a new inputted form with a score from 0 to 1, indicating the predicted preference of the architect, showing the possibility of using machine learning to quantitatively evaluate personal design taste.
series	Architectural Intelligence
email
last changed	2022/09/29 07:28

_id	ecaade2020_167
id	ecaade2020_167
authors	Newton, David, Piatkowski, Dan, Marshall, Wesley and Tendle, Atharva
year	2020
title	Deep Learning Methods for Urban Analysis and Health Estimation of Obesity
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 297-304
doi	https://doi.org/10.52842/conf.ecaade.2020.1.297
summary	In the 20th and 21st centuries, urban populations have increased dramatically with a whole host of impacts to human health that remain unknown. Research has shown significant correlations between design features in the built environment and human health, but this research has remained limited. A better understanding of this relationship could allow urban planners and architects to design healthier cities and buildings for an increasingly urbanized population. This research addresses this problem by using discriminative deep learning in combination with satellite imagery of census tracts to estimate rates of obesity. Data from the California Health Interview Survey is used to train a Convolutional Neural Network that uses satellite imagery of selected census tracts to estimate rates of obesity. This research contributes knowledge on methods for applying deep learning to urban health estimation, as well as, methods for identifying correlations between urban morphology and human health.
keywords	Deep Learning; Artificial Intelligence; Urban Planning; Health; Remote Sensing
series	eCAADe
email
last changed	2022/06/07 07:58

_id	caadria2020_384
id	caadria2020_384
authors	Patt, Trevor Ryan
year	2020
title	Spectral Clustering for Urban Networks
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 91-100
doi	https://doi.org/10.52842/conf.caadria.2020.2.091
summary	As planetary urbanization accelerates, the significance of developing better methods for analyzing and making sense of complex urban networks also increases. The complexity and heterogeneity of contemporary urban space poses a challenge to conventional descriptive tools. In recent years, the emergence of urban network analysis and the widespread availability of GIS data has brought network analysis methods into the discussion of urban form. This paper describes a method for computationally identifying clusters within urban and other spatial networks using spectral analysis techniques. While spectral clustering has been employed in some limited urban studies, on large spatialized datasets (particularly in identifying land use from orthoimages), it has not yet been thoroughly studied in relation to the space of the urban network itself. We present the construction of a weighted graph Laplacian matrix representation of the network and the processing of the network by eigen decomposition and subsequent clustering of eigenvalues in 4d-space.In this implementation, the algorithm computes a cross-comparison for different numbers of clusters and recommends the best option based on either the 'elbow method,' or by "eigen gap" criteria. The results of the clustering operation are immediately visualized on the original map and can also be validated numerically according to a selection of cluster metrics. Cohesion and separation values are calculated simultaneously for all nodes. After presenting these, the paper also expands on the 'silhouette' value, which is a composite measure that seems especially suited to urban network clustering.This research is undertaken with the aim of informing the design process and so the visualization of results within the active 3d model is essential. Within the paper, we illustrate the process as applied to formal grids and also historic, vernacular urban fabric; first on small, extract urban fragments and then over an entire city networks to indicate the scalability.
keywords	Urban morphology; network analysis; spectral clustering; computation
series	CAADRIA
email
last changed	2022/06/07 07:59

_id	caadria2020_259
id	caadria2020_259
authors	Rhee, Jinmo, Veloso, Pedro and Krishnamurti, Ramesh
year	2020
title	Integrating building footprint prediction and building massing - an experiment in Pittsburgh
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 669-678
doi	https://doi.org/10.52842/conf.caadria.2020.2.669
summary	We present a novel method for generating building geometry using deep learning techniques based on contextual geometry in urban context and explore its potential to support building massing. For contextual geometry, we opted to investigate the building footprint, a main interface between urban and architectural forms. For training, we collected GIS data of building footprints and geometries of parcels from Pittsburgh and created a large dataset of Diagrammatic Image Dataset (DID). We employed a modified version of a VGG neural network to model the relationship between (c) a diagrammatic image of a building parcel and context without the footprint, and (q) a quadrilateral representing the original footprint. The option for simple geometrical output enables direct integration with custom design workflows because it obviates image processing and increases training speed. After training the neural network with a curated dataset, we explore a generative workflow for building massing that integrates contextual and programmatic data. As trained model can suggest a contextual boundary for a new site, we used Massigner (Rhee and Chung 2019) to recommend massing alternatives based on the subtraction of voids inside the contextual boundary that satisfy design constraints and programmatic requirements. This new method suggests the potential that learning-based method can be an alternative of rule-based design methods to grasp the complex relationships between design elements.
keywords	Deep Learning; Prediction; Building Footprint; Massing; Generative Design
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	sigradi2020_267
id	sigradi2020_267
authors	Soares, Juliana Maria Moreira; Campos, Paulo Eduardo Fonseca de
year	2020
title	Women and intersectionality: perspectives based on digital fabrication as a viable platform for assistive design
source	SIGraDi 2020 [Proceedings of the 24th Conference of the Iberoamerican Society of Digital Graphics - ISSN: 2318-6968] Online Conference 18 - 20 November 2020, pp. 267-274
summary	This article aims to present analyzes and perceptions regarding the experience of a project developed under the following axes: women, disabilities and the development of Assistive Technology in spaces called Fablabs. The study is developed according to an exploratory approach, with a qualitative nature. This paper provides an introduction, an exploration of the experiments and the reflections over the performed activities. In the practical stages, among others, methods of an ethnographic nature and Design Research were used. The group of women, mothers of children with disabilities, variable in size during the practices (from two to five people), carried out activities to develop assistive technology products using digital manufacturing tools in a public laboratory of the Fablab Livre SP Network , in the city of Sao Paulo, Brazil. The reflections of this study go towards questions related to women's self- esteem in the face of processes of inclusion in the technological area. The multi-signification of the Fablab space and the need to expand the intersectional debate within these environments are also encounters provided by this research.
keywords	Women, isability, fablabs, assistive technology, digital fabrication
series	SIGraDi
email
last changed	2021/07/16 11:49

_id	sigradi2020_128
id	sigradi2020_128
authors	Sousa, Megg; Mônaco, Denise; Martínez, Andressa; Souza, Douglas
year	2020
title	The operationalization of "A Pattern Language" by using network analysis tools
source	SIGraDi 2020 [Proceedings of the 24th Conference of the Iberoamerican Society of Digital Graphics - ISSN: 2318-6968] Online Conference 18 - 20 November 2020, pp. 128-136
summary	One of the most significant public space studies, "A Pattern Language", published in 1977, is until today's background for some contemporary investigations. The aim of this paper is to propose an operationalization of the patterns' network of the book into a network analysis tool. The methodology is based on a new classification of patterns, in addition to what is initially presented in the book: "context patterns" (evidencing pre-existing conditions and potentialities) and "design patterns" (considering possibilities limited by the stakeholder at that location). The digital operationalization can enhance the analytical and predictive character of the work.
keywords	Pattern language, Network analysis tool, Christopher Alexander, Public spaces
series	SIGraDi
email
last changed	2021/07/16 11:48

_id	acadia20_160
id	acadia20_160
authors	Sun, Yunjuan; Jiang, Lei; Zheng, Hao
year	2020
title	A Machine Learning Method of Predicting Behavior Vitality Using Open Source Data
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 160-168.
doi	https://doi.org/10.52842/conf.acadia.2020.2.160
summary	The growing popularity of machine learning has provided new opportunities to predict certain behaviors precisely by utilizing big data. In this research, we use an image-based neural network to explore the relationship between the built environment and the activity of bicyclists in that environment. The generative model can produce heat maps that can be used to predict quantitatively the cycling and running activity in a given area, and then use urban design to enhance urban vitality in that area. In the machine learning model, the input image is a plan view of the built environment, and the output image is a heat map showing certain activities in the corresponding area. After it is trained, the model yields output (the predicted heat map) at an acceptable level of accuracy. The heat map shows the levels and conditions of the subject activity in different sections of the built environment. Thus, the predicted results can help identify where regional vitality can be improved. Using this method, designers can not only predict the behavioral heat distribution but also examine the different interactions between behaviors and aspects of the environment. The extent to which factors might influence behaviors is also studied by generating a heat map of the modified plan. In addition to the potential applications of this approach, its limitations and areas for improvement are also proposed.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2020_093
id	ecaade2020_093
authors	Veloso, Pedro and Krishnamurti, Ramesh
year	2020
title	An Academy of Spatial Agents - Generating spatial configurations with deep reinforcement learning
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 191-200
doi	https://doi.org/10.52842/conf.ecaade.2020.2.191
summary	Agent-based models rely on decentralized decision making instantiated in the interactions between agents and the environment. In the context of generative design, agent-based models can enable decentralized geometric modelling, provide partial information about the generative process, and enable fine-grained interaction. However, the existing agent-based models originate from non-architectural problems and it is not straight-forward to adapt them for spatial design. To address this, we introduce a method to create custom spatial agents that can satisfy architectural requirements and support fine-grained interaction using multi-agent deep reinforcement learning (MADRL). We focus on a proof of concept where agents control spatial partitions and interact in an environment (represented as a grid) to satisfy custom goals (shape, area, adjacency, etc.). This approach uses double deep Q-network (DDQN) combined with a dynamic convolutional neural-network (DCNN). We report an experiment where trained agents generalize their knowledge to different settings, consistently explore good spatial configurations, and quickly recover from perturbations in the action selection.
keywords	space planning; agent-based model; interactive generative systems; artificial intelligence; multi-agent deep reinforcement learning
series	eCAADe
email
last changed	2022/06/07 07:58

_id	cdrf2019_169
id	cdrf2019_169
authors	Yubo Liu, Yihua Luo, Qiaoming Deng, and Xuanxing Zhou
year	2020
title	Exploration of Campus Layout Based on Generative Adversarial Network Discussing the Significance of Small Amount Sample Learning for Architecture
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_16
summary	This paper aims to explore the idea and method of using deep learning with a small amount sample to realize campus layout generation. From the perspective of the architect, we construct two small amount sample campus layout data sets through artificial screening with the preference of the specific architects. These data sets are used to train the ability of Pix2Pix model to automatically generate the campus layout under the condition of the given campus boundary and surrounding roads. Through the analysis of the experimental results, this paper finds that under the premise of effective screening of the collected samples, even using a small amount sample data set for deep learning can achieve a good result.
series	cdrf
email
last changed	2022/09/29 07:51

_id	ecaade2020_190
id	ecaade2020_190
authors	Dounas, Theodoros, Jabi, Wassim and Lombardi, Davide
year	2020
title	Smart Contracts for Decentralised Building Information Modelling
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 565-574
doi	https://doi.org/10.52842/conf.ecaade.2020.2.565
summary	The paper presents a model for decentralizing building information modelling, through implementing its infrastructure using the decentralized web. We discuss the shortcomings of BIM in terms of its infrastructure, with a focus on tracing identities of design authorship in this collective design tool. In parallel we examine the issues with BIM in the cloud and propose a decentralized infrastructure based on the Ethereum blockchain and the Interplanetary filesystem (IPFS). A series of computing nodes, that act as nodes on the Ethereum Blockchain, host disk storage with which they participate in a larger storage pool on the Interplanetary Filesystem. This storage is made available through an API is used by architects and designers creating and editing a building information model that resides on the IPFS decentralised storage. Through this infrastructure central servers are eliminated, and BIM libraries and models can be shared with others in an immutable and transparent manner. As such Architecture practices are able to exploit their intellectual property in novel ways, by making it public on the internet. The infrastructure also allows the decentralised creation of a resilient global pool of data that allows the participation of computation agents in the creation and simulation of BIM models.
keywords	Blockchain; decentralisation; immutability; resilience; Building Information Modelling
series	eCAADe
email
last changed	2022/06/07 07:55

_id	acadia20_84
id	acadia20_84
authors	Kirova, Nikol; Markopoulou, Areti
year	2020
title	Pedestrian Flow: Monitoring and Prediction
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 84-93.
doi	https://doi.org/10.52842/conf.acadia.2020.1.084
summary	The worldwide lockdowns during the first wave of the COVID-19 pandemic had an immense effect on the public space. The events brought up an opportunity to redesign mobility plans, streets, and sidewalks, making cities more resilient and adaptable. This paper builds on previous research of the authors that focused on the development of a graphene-based sensing material system applied to a smart pavement and utilized to obtain pedestrian spatiotemporal data. The necessary steps for gradual integration of the material system within the urban fabric are introduced as milestones toward predictive modeling and dynamic mobility reconfiguration. Based on the capacity of the smart pavement, the current research presents how data acquired through an agent-based pedestrian simulation is used to gain insight into mobility patterns. A range of maps representing pedestrian density, flow, and distancing are generated to visualize the simulated behavioral patterns. The methodology is used to identify areas with high density and, thus, high risk of transmitting airborne diseases. The insights gained are used to identify streets where additional space for pedestrians is needed to allow safe use of the public space. It is proposed that this is done by creating a dynamic mobility plan where temporal pedestrianization takes place at certain times of the day with minimal disruption of road traffic. Although this paper focuses mainly on the agent-based pedestrian simulation, the method can be used with real-time data acquired by the sensing material system for informed decision-making following otherwise-unpredictable pedestrian behavior. Finally, the simulated data is used within a predictive modeling framework to identify further steps for each agent; this is used as a proof-of-concept through which more insights can be gained with additional exploration.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2020_009
id	caadria2020_009
authors	Wang, Likai, Chen, Kian Wee, Janssen, Patrick and Ji, Guohua
year	2020
title	Algorithmic generation of architectural Massing Models for building design optimisation - Parametric Modelling Using Subtractive and Additive Form Generation Principles
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 385-394
doi	https://doi.org/10.52842/conf.caadria.2020.1.385
summary	Using performance-based optimisation to explore unknown design solutions space has become widely acknowledged and considered an efficient approach to designing high-performing buildings. However, the lack of design diversity in the design space defined by the parametric model often confines the search of the optimisation process to a family of similar design variants. In order to overcome this weakness, this paper presents two parametric massing generation algorithms based on the additive and subtractive form generation principles. By abstracting the rule of these two principles, the algorithms can generate diverse building massing design alternatives. This allows the algorithms to be used in performance-based optimisation for exploring a wide range of design alternatives guided by various performance objectives. Two case studies of passive solar energy optimisation are presented to demonstrate the efficacy of the algorithm in helping architects achieve an explorative performance-based optimisation process.
keywords	parametric massing algorithms; performance-based optimisation; design exploration; solar irradiation
series	CAADRIA
email
last changed	2022/06/07 07:58

_id	caadria2020_028
id	caadria2020_028
authors	Xia, Yixi, Yabuki, Nobuyoshi and Fukuda, Tomohiro
year	2020
title	Development of an Urban Greenery Evaluation System Based on Deep Learning and Google Street View
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 1, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 783-792
doi	https://doi.org/10.52842/conf.caadria.2020.1.783
summary	Street greenery has long played a vital role in the quality of urban landscapes and is closely related to people's physical and mental health. In the current research on the urban environment, researchers use various methods to simulate and measure urban greenery. With the development of computer technology, the way to obtain data is more diverse. For the assessment of urban greenery quality, there are many methods, such as using remote sensing satellite images captured from above (antenna, space) sensors, to assess urban green coverage. However, this method is not suitable for the evaluation of street greenery. Unlike most remote sensing images, from a pedestrian perspective, urban street images are the most common view of green plants. The street view image presented by Google Street View image is similar to the captured by the pedestrian perspective. Thus it is more suitable for studying urban street greening. With the development of artificial intelligence, based on deep learning, we can abandon the heavy manual statistical work and obtain more accurate semantic information from street images. Furthermore, we can also measure green landscapes in larger areas of the city, as well as extract more details from street view images for urban research.
keywords	Green View Index; Deep Learning; Google Street View; Segmentation
series	CAADRIA
email
last changed	2022/06/07 07:57

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