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

PDF papers
References

Hits 1 to 20 of 545

Reformat results as: short short into frame detailed detailed into frame

_id	ijac202018402
id	ijac202018402
authors	Mette Ramsgaard Thomsen, Paul Nicholas, Martin Tamke, Sebastian Gatz, Yuliya Sinke and Gabriella Rossi
year	2020
title	Towards machine learning for architectural fabrication in the age of industry 4.0
source	International Journal of Architectural Computing vol. 18 - no. 4, 335–352
summary	Machine Learning (ML) is opening new perspectives for architectural fabrication, as it holds the potential for the profession to shortcut the currently tedious and costly setup of digital integrated design to fabrication workflows and make these more adaptable. To establish and alter these workflows rapidly becomes a main concern with the advent of Industry 4.0 in building industry. In this article we present two projects, which presents how ML can lead to radical changes in generation of fabrication data and linking these directly to design intent. We investigate two different moments of implementation: linking performance to the generation of fabrication data (KnitCone) and integrating the ability to adapt fabrication data in realtime as response to fabrication processes (Neural-Network Steered Robotic Fabrication). Together they examine how models can employ design information as training data and be trained to by step processes within the digital chain. We detail the advantages and limitations of each experiment, we reflect on core questions and perspectives of ML for architectural fabrication: the nature of data to be used, the capacity of these algorithms to encode complexity and generalize results, their task-specificness versus their adaptability and the tradeoffs of using them with respect to conventional explicit analytical modelling.
keywords	Machine learning, architectural design, industry 4.0, digital fabrication, robotic fabrication, CNC knit, neural networks
series	journal
email
last changed	2021/06/03 23:29

_id	ijac202119406
id	ijac202119406
authors	Silva Dória, David Rodrigues; Ramaswami, Keshav; Claypool, Mollie; Retsin, Gilles
year	2021
title	Public parts, resocialized autonomous communal life
source	International Journal of Architectural Computing 2021, Vol. 19 - no. 4, 568–593
summary	Commoning embodies the product of social contracts and behaviors between groups of individuals. In thecase of social housing and the establishment of physical domains for life, commoning is an intersection of thesecontracts and the restrictions and policies that prohibit and allow them to occur within municipalities. Via aplatform-based project entitled Public Parts (2020), this article will also present positions on the reification ofthe common through a set of design methodologies and implementations of automation. This platform seeksto subvert typical platform models to decrease ownership, increase access, and produce a new form ofcommunal autonomous life amongst individuals that constitute the rapidly expanding freelance, work fromhome, and gig economies. Furthermore, this text investigates the consequences of merging domestic spacewith artificial intelligence by implementing machine learning to reconfigure spaces and program. Theproblems that arise from the deployment of machine learning algorithms involve issues of collection, usage,and ownership of data. Through the physical design of space, and a central AI which manages the platform andthe automated management of space, the core objective of Public Parts is to reify the common througharchitecture and collectively owned data.
keywords	Common, housing, platforms, reification, artificial intelligence, automation
series	journal
email
last changed	2024/04/17 14:29

_id	ascaad2021_041
id	ascaad2021_041
authors	Taºdelen, Sümeyye; Leman Gül
year	2021
title	Social Network Analysis of Digital Design Actors: Exploratory Study Covering the Journal Architectural Design
source	Abdelmohsen, S, El-Khouly, T, Mallasi, Z and Bennadji, A (eds.), Architecture in the Age of Disruptive Technologies: Transformations and Challenges [9th ASCAAD Conference Proceedings ISBN 978-1-907349-20-1] Cairo (Egypt) [Virtual Conference] 2-4 March 2021, pp. 280-292
summary	This research asks the question of how the design knowledge production mechanism is processed differentiates digital design actors from each other in the social media/professional and academic fields of architecture. Due to the broad nature of the research question, the study focuses on academia and academia-related media through prominent architect-authors and subject titles in the literature. Bourdieu’s concept of capital is introduced, in which cultural and symbolic capital are considered part of the production values of digital design actors. Digital design actors use image-based social media tools such as Instagram effectively. The paper uses two methods: the first is a bibliographical analysis of author-texts, and the second is a social network analysis. By employing the keyword-based search from the Web of Science database, this study has managed to extract papers with full records (citations, keywords, and abstracts), with the journal Architectural Design having most publications. Considering that both academicians and professionals contribute to publications in Architectural Design, we selected all its publications between 2010-2020 for bibliometric analysis. These analysis techniques include the bibliometric network analyses and social network analysis with the focus on visualizing the algorithms and statistical calculations of well-established metrics. The research reveals the most critical nodes of the bibliometric network by calculating the appropriate central metrics. The network formed by the selected Instagram accounts of digital design actors are shown to be a small-scale network group, while the hashtags of digital design concepts are more numerous than the digital design actors.
series	ASCAAD
email
last changed	2021/08/09 13:11

_id	caadria2020_063
id	caadria2020_063
authors	Wang, Chunxiao and Lu, Shuai
year	2020
title	Influence of Uncertainties in Envelope and Occupant Parameters on the Reliability of Energy-Based Form Optimization of Office Buildings
doi	https://doi.org/10.52842/conf.caadria.2020.1.497
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. 497-506
summary	Building performance optimization is effective in finding optimal designs and improving building energy efficiency, but its reliability can be affected by uncertainties in input parameters. This paper conducts a reliability analysis on energy-based form optimization of office buildings under uncertainties in envelope and occupancy parameters. An optimization process involving Rhinoceros, EnergyPlus and genetic algorithms is first implemented. Then parametric studies of 644 scenarios involving 4 cities in different climates and 3 form variables are conducted. The results indicate that uncertainties in input parameters could lead to major unreliability of optimization results, including reductions up to 13% in energy saving achieved by optimization and descents up to 10% in energy efficiency compared with results before optimization. Moreover, the uncertainty in visual transmittance of windows is the most significant cause for the unreliability, followed by U-value of walls, while the uncertainty in occupant density and occupant schedule has limited influence. The results can help designers understand the uncertainty of which parameters should be controlled and to what extend optimization results can be trusted in various scenarios.
keywords	Building Performance Optimization; Form Design; Building Energy Efficiency; Uncertainty Analysis; Office Building
series	CAADRIA
email
last changed	2022/06/07 07:58

_id	sigradi2020_608
id	sigradi2020_608
authors	Costa, Eduardo; Duarte, José; Bilén, Sven G.
year	2020
title	Robotic Apprentices: Leveraging Augmented Reality for Robot Training in Manufacturing Automation
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. 608-614
summary	In the scope of Industry 4.0, a framework is proposed to leverage the potential of articulating Augmented Reality and Robotic Manufacturing in the construction industry. The objective of such framework is to enable robots to learn how to perform tasks using direct interaction with human operators. As a first step, we established a connection between a robot and its trainer— or controller—in which the robot mirrors the operator’s actions. Augmented Reality hardware is used for capturing the trainer’s gestures and the surrounding environment. A digital tool was implemented using Grasshopper and additional plugins to control the process.
keywords	Augmented reality, Robotic arm, Programming by demonstration, Human–Robot Collaboration, Industry 4.0
series	SIGraDi
email
last changed	2021/07/16 11:52

_id	ijac202018403
id	ijac202018403
authors	Dagmar Reinhardt, Matthias Hank Haeusler, Kerry London, Lian Loke, Yingbin Feng, Eduardo De Oliveira Barata, Charlotte Firth, Kate Dunn, Nariddh Khean, Alessandra Fabbri, Dylan Wozniak-O’Connor and Rin Masuda
year	2020
title	CoBuilt 4.0: Investigating the potential of collaborative robotics for subject matter experts
source	International Journal of Architectural Computing vol. 18 - no. 4, 353–370
summary	Human-robot interactions can offer alternatives and new pathways for construction industries, industrial growth and skilled labour, particularly in a context of industry 4.0. This research investigates the potential of collaborative robots (CoBots) for the construction industry and subject matter experts; by surveying industry requirements and assessments of CoBot acceptance; by investing processes and sequences of work protocols for standard architecture robots; and by exploring motion capture and tracking systems for a collaborative framework between human and robot co-workers. The research investigates CoBots as a labour and collaborative resource for construction processes that require precision, adaptability and variability.Thus, this paper reports on a joint industry, government and academic research investigation in an Australian construction context. In section 1, we introduce background data to architecture robotics in the context of construction industries and reports on three sections. Section 2 reports on current industry applications and survey results from industry and trade feedback for the adoption of robots specifically to task complexity, perceived safety, and risk awareness. Section 3, as a result of research conducted in Section 2, introduces a pilot study for carpentry task sequences with capture of computable actions. Section 4 provides a discussion of results and preliminary findings. Section 5 concludes with an outlook on how the capture of computable actions provide the foundation to future research for capturing motion and machine learning.
keywords	Industry 4.0, collaborative robotics, on-site robotic fabrication, industry research, machine learning
series	journal
email
last changed	2021/06/03 23:29

_id	ecaade2020_264
id	ecaade2020_264
authors	Nicholas, Paul, Rossi, Gabriella, Papadopoulou, Iliana, Tamke, Martin, Aalund Brandt, Nikolaj and Jessen Hansen, Leif
year	2020
title	Precision Partner - Enhancing GFRC craftsmanship with industry 4.0 factory-floor feedback
doi	https://doi.org/10.52842/conf.ecaade.2020.2.631
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. 631-640
summary	This paper presents a novel human-machine collaborative approach to automatic quality-control of Glass-Fiber Reinforced Concrete (GFRC) molds directly on the factory floor. The framework introduces Industry 4.0 technologies to enhance the ability of skilled craftsmen to make molds through the provision of horizontal feedback regarding dimensional tolerances. Where digital tools are seldom used in the fabrication of GFRC molds, and expert craftsmen are not digital experts, our implementation of automated registration and feedback processes enables craftsmen to be integrated into and gain value from the digital production chain. In this paper, we describe the in-progress framework, Precision Partner, which connects 3d scanning and point cloud registration of geometrically complex and varied one off elements to factory floor dimensional feedback. We firstly introduce the production context of GFRC molds, as well as industry standards for production feedback. We then detail our methods, and report the results of a case study that tests the framework on the case of a balcony element.
keywords	3d Scanning; GFRC; Feedback; Automation; Human in the loop; Digital Chain
series	eCAADe
email
last changed	2022/06/07 07:58

_id	ijac202018404
id	ijac202018404
authors	Paul Nicholas, Gabriella Rossi, Ella Williams, Michael Bennett and Tim Schork
year	2020
title	Integrating real-time multi-resolution scanning and machine learning for Conformal Robotic 3D Printing in Architecture
source	International Journal of Architectural Computing vol. 18 - no. 4, 371–384
summary	Robotic 3D printing applications are rapidly growing in architecture, where they enable the introduction of new materials and bespoke geometries. However, current approaches remain limited to printing on top of a flat build bed. This limits robotic 3D printing’s impact as a sustainable technology: opportunities to customize or enhance existing elements, or to utilize complex material behaviour are missed. This paper addresses the potentials of conformal 3D printing and presents a novel and robust workflow for printing onto unknown and arbitrarily shaped 3D substrates. The workflow combines dual-resolution Robotic Scanning, Neural Network prediction and printing of PETG plastic. This integrated approach offers the advantage of responding directly to unknown geometries through automated performance design customization. This paper firstly contextualizes the work within the current state of the art of conformal printing. We then describe our methodology and the design experiment we have used to test it. We lastly describe the key findings, potentials and limitations of the work, as well as the next steps in this research.
keywords	Conformal printing, robotic fabrication, 3D scanning, neural networks, industry 4.0
series	journal
email
last changed	2021/06/03 23:29

_id	ecaade2020_193
id	ecaade2020_193
authors	Alymani, Abdulrahman, Jabi, Wassim and Corcoran, Padraig
year	2020
title	Machine Learning Methods for Clustering Architectural Precedents - Classifying the relationship between building and ground
doi	https://doi.org/10.52842/conf.ecaade.2020.1.643
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. 643-652
summary	Every time an object is built, it creates a relationship with the ground. Architects have a full responsibility to design the building by taking the ground into consideration. In the field of architecture, using data mining to identify any unusual patterns or emergent architectural trends is a nascent area that has yet to be fully explored. Clustering techniques are an essential tool in this process for organising large datasets. In this paper, we propose a novel proof-of-concept workflow that enables a machine learning computer system to cluster aspects of an architect's building design style with respect to how the buildings in question relate to the ground. The experimental workflow in this paper consists of two stages. In the first stage, we use a database system to collect, organise and store several significant architectural precedents. The second stage examines the most well-known unsupervised learning algorithm clustering techniques which are: K-Means, K-Modes and Gaussian Mixture Models. Our experiments demonstrated that the K-means clustering algorithm method achieves a level of accuracy that is higher than other clustering methods. This research points to the potential of AI in helping designers identify the typological and topological characteristics of architectural solutions and place them within the most relevant architectural canons
keywords	Machine Learning; Building and Ground Relationship; Clustering Algorithms; K-means cluster Algorithms
series	eCAADe
email
last changed	2022/06/07 07:54

_id	cdrf2019_199
id	cdrf2019_199
authors	Ana Herruzo and Nikita Pashenkov
year	2020
title	Collection to Creation: Playfully Interpreting the Classics with Contemporary Tools
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_19
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	This paper details an experimental project developed in an academic and pedagogical environment, aiming to bring together visual arts and computer science coursework in the creation of an interactive installation for a live event at The J. Paul Getty Museum. The result incorporates interactive visuals based on the user’s movements and facial expressions, accompanied by synthetic texts generated using machine learning algorithms trained on the museum’s art collection. Special focus is paid to how advances in computing such as Deep Learning and Natural Language Processing can contribute to deeper engagement with users and add new layers of interactivity.
series	cdrf
email
last changed	2022/09/29 07:51

_id	ecaade2020_146
id	ecaade2020_146
authors	Andriasyan, Mesrop, Zanelli, Alessandra, Yeghikyan, Gevorg, Asher, Rob and Haeusler, Hank
year	2020
title	Algorithmic Planning and Assessment of Emergency Settlements and Refugee Camps
doi	https://doi.org/10.52842/conf.ecaade.2020.2.115
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. 115-124
summary	The planning quality of refugee camps profoundly affects the people living there. Because of the short time span allotted to planners due to the state of emergency, camps are often poorly planned or not planned at all. This paper proposes tools and methods developed through computational modelling algorithms that can enhance the design procedure and provide instant feedback about the plan performance to the planner. The developed planning framework allows defining the planning guidelines which will be tested for compliance. The paper also shows case studies of analysing an existing refugee camp.
keywords	Refugee camp; shelter; generative design; UNHCR; humanitarian architecture
series	eCAADe
email
last changed	2022/06/07 07:54

_id	acadia20_584
id	acadia20_584
authors	Brás, Catarina; Castelo-Branco, Renata; Menezes Leitao, António
year	2020
title	Parametric Model Manipulation in Virtual Reality
doi	https://doi.org/10.52842/conf.acadia.2020.1.584
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. 584-593.
summary	Algorithmic design (AD) uses algorithms to describe architectural designs, producing results that are visual by nature and greatly benefit from immersive visualization. Having this in mind, several approaches have been developed that allow architects to access and change their AD programs in virtual reality (VR). However, programming in VR introduces a new level of complexity that hinders creative exploration. Solutions based in visual programming offer limited parameter manipulation and do not scale well, particularly when used in a remote collaboration environment, while those based in textual programming struggle to find adequate interaction mechanisms to efficiently modify existing programs in VR. This research proposes to ease the programming task for architects who wish to develop and experiment with collaborative textual-based AD in VR, by bringing together the user-friendly features of visual programming and the flexibility and scalability of textual programming. We introduce an interface for the most common parametric changes that automatically generates the corresponding code in the AD program, and a hybrid programming solution that allows participants in an immersive collaborative design experience to combine textual programming with this new visual alternative for the parametric manipulation of the design. The proposed workflow aims to foster remote collaborative work in architecture studios, offering professionals of different backgrounds the opportunity to parametrically interact with textual-based AD projects while immersed in them.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_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
doi	https://doi.org/10.52842/conf.acadia.2020.1.074
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.
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	ecaade2020_307
id	ecaade2020_307
authors	Caetano, Ines and Leitao, António
year	2020
title	When the Geometry Informs the Algorithm - A hybrid visual/textual programming framework for facade design
doi	https://doi.org/10.52842/conf.ecaade.2020.2.371
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. 371-380
summary	Facade design is becoming increasingly complex, forcing architects to more frequently resort to analysis and optimization processes. However, these processes are time-consuming and require the coordination of multiple tools. Algorithmic Design (AD) has the potential to overcome these limitations through the use of algorithms implemented in Textual Programming Languages (TPLs) or Visual Programming Languages (VPLs). VPLs are more used in architecture due to their smoother learning curve and user-friendliness, but TPLs are better suited than VPLs for handling complex AD problems. To make TPLs more appealing to architects, we incorporated VPLs' features in the textual paradigm, namely, Visual Input Mechanisms (VIMs). In this paper, we propose an extension to an existing AD framework for the design exploration, analysis, and optimization of facades to support a TPL-based approach that handles VIMs.
keywords	Algorithmic Design; Facade Design; Textual Languages; Visual Input
series	eCAADe
email
last changed	2022/06/07 07:54

_id	sigradi2020_149
id	sigradi2020_149
authors	Canestrino, Giuseppe; Laura, Greco; Spada, Francesco; Lucente, Roberta
year	2020
title	Generating architectural plan with evolutionary multiobjective optimization algorithms: a benchmark case with an existent construction system
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. 149-156
summary	In architectural design, evolutionary multiobjective optimization algorithms (EMOA) have found use in numerous practical applications in which qualitative and quantitative aspects can be transformed into fitness functions to be optimized. This paper shows that they can be used in an architectural plan design process that starts from a more traditional approach. The benchmark case uses a novel construction system, called Ac.Ca. Building, with a vast architectural and technological database, arleady validated, to generate architectural plan for a residential towerbuilding with a parametric approach and EMOA. The proposed framework differs from past research because uses spatial units with high level of architectural and tecnological definition.
keywords	Architectural design, Parametric architecture, Performance-driven design, architectural layout, evolutionary multiobjective optimization
series	SIGraDi
email
last changed	2021/07/16 11:48

_id	caadria2020_224
id	caadria2020_224
authors	Castelo-Branco, Renata and LeitÃ£o, AntÃ³nio
year	2020
title	Visual Meets Textual - A Hybrid Programming Environment for Algorithmic Design
doi	https://doi.org/10.52842/conf.caadria.2020.1.375
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. 375-384
summary	Algorithmic approaches are currently being introduced in many areas of human activity and architecture is no exception. However, designing with algorithms is a foreign concept to many and the inadequacy of current programming environments creates a barrier to the generalized adoption of Algorithmic Design (AD). This research aims to provide architects with a programming tool they feel comfortable with, while allowing them to fully benefit from AD's advantages in the creation of complex architectural models. We present Khepri.gh, a hybrid solution that combines Grasshopper, a visual programming environment, with Khepri, a flexible and scalable textual programming tool. Khepri.gh establishes a bridge between the visual and the textual paradigm, offering its users the best of both worlds while providing an extra set of advantages, including portability among CAD, BIM, and analysis tools.
keywords	Algorithmic Design; Hybrid Programming Environment; Textual Programming; Visual Programming
series	CAADRIA
email
last changed	2022/06/07 07:55

_id	ecaade2020_515
id	ecaade2020_515
authors	Chadha, Kunaljit, Dubor, Alexandre, Puigpinos, Laura and Rafols, Irene
year	2020
title	Space Filling Curves for Optimising Single Point Incremental Sheet Forming using Supervised Learning Algorithms
doi	https://doi.org/10.52842/conf.ecaade.2020.1.555
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. 555-562
summary	Increasing use of computational design tools have led to an increase in the demand for mass customised fabrication, rendering decades old industrial CAD-CAM protocols limiting for such fabrication processes. This bespoke demand of components has led to a unified workflow between design strategies and production techniques. Recent advances in computation have allowed us to predict and register the tolerances of fabrication before and while being fabricated. Procedural algorithms are a set of novel problem-solving methods and have been attracting considerable attention for their good performance.They follow a procedural way of iteration with an established way of behavior.In the particular case of Incremental Sheet forming (ISF), these algorithms can realize several functions such as edge detection and segmentation required for optimizing machining time and accuracy.In this context, this paper presents a methodology to optimize long-drawn-out ISF operation by using geometrical intervention informed by supervised machine learning algorithms.
keywords	Procedural Algorithms; Incremental Sheet Forming; Robotic Cold forming; Mass Customization
series	eCAADe
email
last changed	2022/06/07 07:55

_id	caadria2020_446
id	caadria2020_446
authors	Cho, Dahngyu, Kim, Jinsung, Shin, Eunseo, Choi, Jungsik and Lee, Jin-Kook
year	2020
title	Recognizing Architectural Objects in Floor-plan Drawings Using Deep-learning Style-transfer Algorithms
doi	https://doi.org/10.52842/conf.caadria.2020.2.717
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. 717-725
summary	This paper describes an approach of recognizing floor plans by assorting essential objects of the plan using deep-learning based style transfer algorithms. Previously, the recognition of floor plans in the design and remodeling phase was labor-intensive, requiring expert-dependent and manual interpretation. For a computer to take in the imaged architectural plan information, the symbols in the plan must be understood. However, the computer has difficulty in extracting information directly from the preexisting plans due to the different conditions of the plans. The goal is to change the preexisting plans to an integrated format to improve the readability by transferring their style into a comprehensible way using Conditional Generative Adversarial Networks (cGAN). About 100-floor plans were used for the dataset which was previously constructed by the Ministry of Land, Infrastructure, and Transport of Korea. The proposed approach has such two steps: (1) to define the important objects contained in the floor plan which needs to be extracted and (2) to use the defined objects as training input data for the cGAN style transfer model. In this paper, wall, door, and window objects were selected as the target for extraction. The preexisting floor plans would be segmented into each part, altered into a consistent format which would then contribute to automatically extracting information for further utilization.
keywords	Architectural objects; floor plan recognition; deep-learning; style-transfer
series	CAADRIA
email
last changed	2022/06/07 07:56

_id	ecaade2020_272
id	ecaade2020_272
authors	De Luca, Francesco and Wortmann, Thomas
year	2020
title	Multi-Objective Optimization for Daylight Retrofit
doi	https://doi.org/10.52842/conf.ecaade.2020.1.057
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. 57-66
summary	In sustainable building design, daylight improves occupants' wellbeing and reduces electric lighting use, but glazed areas can increase energy consumption for heating and cooling. Conflicting objectives such as daylight and energy consumption are the primary motivation behind multi-objective optimization. This paper presents the multi-objective optimization problem of maximizing daylight availability and minimizing whole energy consumption for the daylight retrofit of Tallinn University of Technology assembly hall, currently windowless. We present benchmark results of six different multi-objective algorithms and analyze the solutions on the best-known Pareto front. The majority of the analyzed solutions allow for adequate daylight provision of the building without additional energy consumption. Results of daylight and energy simulations for the analyzed solutions, are presented and discussed.
keywords	Daylight; Energy efficiency; Retrofit; Parametric design; Multi-objective optimization
series	eCAADe
email
last changed	2022/06/07 07:55

_id	acadia20_406
id	acadia20_406
authors	Duong, Eric; Vercoe, Garrett; Baharlou, Ehsan
year	2020
title	Engelbart
doi	https://doi.org/10.52842/conf.acadia.2020.1.406
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. 406-415.
summary	The internet has long been viewed as a cyberspace of free and collective information, allowing for an increase in the diversity of ideas and viewpoints available to the general public. However, critics argue that the emergence of personalization algorithms on social media and other internet platforms instead reduces information diversity by forming “filter bubbles"" of viewpoints similar to the user’s own. The adoption of these personalization algorithms is due in part to advancements in natural language processing, which allow for textual analysis at unprecedented scales. This paper aims to utilize natural language processing and architectural spatial principles to present social media from a collective viewpoint rather than a personalized one. To accomplish this, the paper introduces Engelbart, a data-driven agent-based system, where real-time Twitter conversations are visualized within a two-dimensional environment. This environment is interacted with by the artificial intelligence (AI) agent, Engelbart, which summarizes crowdsourced thoughts and feelings about current trending topics. The functionality of this web application comes from the natural language processing of thousands of tweets per minute throughout several layers of operations, including sentiment analysis and word embeddings. Presented as an understandable interface, it incorporates the values of cybernetics, cyberspace, agent-based modeling, and data ethics to show the potential for social media to become a more transparent space for collective discussion.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

For more results click below: