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_208
id	caadria2022_208
authors	Bielski, Jessica, Langenhan, Christoph, Ziegler, Christoph, Eisenstadt, Viktor, Petzold, Frank, Dengel, Andreas and Althoff, Klaus-Dieter
year	2022
title	The What, Why, What-If and How-To for Designing Architecture, Explainability for Auto-Completion of Computer-Aided Architectural Design of Floor Plan Layouting During the Early Design Stages
doi	https://doi.org/10.52842/conf.caadria.2022.2.435
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	In the next thirty years, the world's population is expected to increase to ten billion people, posing major challenges for the construction industry. To meet the growing demands for residential housing in the future, architects need to work faster, more efficiently, and more sustainably, while increasing architectural quality. The hypothetical intelligent design assistant WHITE BRIDGE, based on the methods of the 'metis' projects, suggests further design steps to support the architectural design decision-making processes of the early design phases. This facilitates faster and better decisions early in the process for a more responsible resource consumption, better mental well-being, and ultimately economic growth. Through a case study we investigate if additional information supports the understanding of these suggestions to reduce the cognitive workload of architectural design decisions on the backdrop of their respective representation. The paper contributes an approach for visualising explanations of an intelligent design assistant, their integration into paper prototypes for case studies, and a workflow for data collection and analysis. The results suggest that the cognitive horizon of the architects is broadened by the explanations, while the visualisation methods significantly influence the usefulness and use of the conveyed information within the explanations.
keywords	Explainability, Artificial intelligence, XAI, SDG 3, SDG 8, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	caadria2022_507
id	caadria2022_507
authors	Bolojan, Daniel, Vermisso, Emmanouil and Yousif, Shermeen
year	2022
title	Is Language All We Need? A Query Into Architectural Semantics Using a Multimodal Generative Workflow
doi	https://doi.org/10.52842/conf.caadria.2022.1.353
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. 353-362
summary	This project examines how interconnected artificial intelligence (AI)-assisted workflows can address the limitations of current language-based models and streamline machine-vision related tasks for architectural design. A precise relationship between text and visual feature representation is problematic and can lead to "ambiguity‚ in the interpretation of the morphological/tectonic complexity of a building. Textual representation of a design concept only addresses spatial complexity in a reductionist way, since the outcome of the design process is co-dependent on multiple interrelated systems, according to systems theory (Alexander 1968). We propose herewith a process of feature disentanglement (using low level features, i.e., composition) within an interconnected generative adversarial networks (GANs) workflow. The insertion of natural language models within the proposed workflow can help mitigate the semantic distance between different domains and guide the encoding of semantic information throughout a domain transfer process.
keywords	Neural Language Models, GAN, Domain Transfer, Design Agency, Semantic Encoding, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	sigradi2022_168
id	sigradi2022_168
authors	Koh, Immanuel
year	2022
title	Palette2Interior Architecture: From Syntactic and Semantic Colour Palettes to Generative Interiors with Deep Learning
source	Herrera, PC, Dreifuss-Serrano, C, Gómez, P, Arris-Calderon, LF, Critical Appropriations - Proceedings of the XXVI Conference of the Iberoamerican Society of Digital Graphics (SIGraDi 2022), Universidad Peruana de Ciencias Aplicadas, Lima, 7-11 November 2022 , pp. 187–198
summary	Colour palettes have long played a significant role in not only capturing design ambience (e.g., as mood boards), but more significantly, in translating an abstract intuition into an explicit ordering mechanism for design representation and synthesis, whether it is in the discipline of graphic design, interior design or architectural design. Might this difficult process of design synthesis from a low-dimensional colour input domain to a high-dimensional spatial design output domain be computationally mapped? Using today’s generative adversarial networks (GANs), the paper aims to investigate this plausibility, and in doing so, hoping to envision an AI-augmented design workflow and tooling. Newly-created datasets are made procedurally and used to train three different types of deep learning models in the specific context of generating living room interior layouts. The results suggest that a combination of syntactic and semantic generative processes is necessary for a critical appropriation of such AI models
keywords	Machine Learning, Artificial Intelligence, Deep Neural Networks, Colour Palette, Interior Design
series	SIGraDi
email
last changed	2023/05/16 16:55

_id	caadria2022_120
id	caadria2022_120
authors	Lin, Yuxin
year	2022
title	Rhetoric, Writing, and Anexact Architecture: The Experiment of Natural Language Processing (NLP) and Computer Vision (CV) in Architectural Design
doi	https://doi.org/10.52842/conf.caadria.2022.1.343
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. 343-352
summary	This paper presents a novel language-driven and artificial intelligence-based architectural design method. This new method demonstrates the ability of neural networks to integrate the language of form through written texts and has the potential to interpret the texts into sustainable architecture under the topic of the coexistence between technologies and humans. The research merges natural language processing, computer vision, and human-machine interaction into a machine learning-to-design workflow. This article encompasses the following topics: 1) an experiment of rethinking writing in architecture through anexact form as rhetoric; 2) an integrative machine learning design method incorporating Generative Pre-trained Transformer 2 model and Attentional Generative Adversarial Networks for sustainable architectural production with unique spatial feeling; 3) a human-machine interaction framework for model generation and detailed design. The whole process is from inexact to exact, then finally anexact, and the key result is a proof-of-concept project: Anexact Building, a mixed-use building that promotes sustainability and multifunctionality under the theme of post-carbon. This paper is of value to the discipline since it applies current and up-to-date digital tools research into a practical project.
keywords	Rhetoric and writing, Natural Language Processing, Computer Vision, GPT-2, AttnGAN, Human-computer Interaction, Architectural Design, Post-carbon, SDG3, SDG11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	architectural_intelligence2022_4
id	architectural_intelligence2022_4
authors	Yihui Li, Wen Gao & Borong Lin
year	2022
title	From type to network: a review of knowledge representation methods in architecture intelligence design
doi	https://doi.org/https://doi.org/10.1007/s44223-022-00006-9
source	Architectural Intelligence Journal
summary	With the rise of the next generation of artificial intelligence driven by knowledge and data, the research on knowledge representation in architecture is also receiving widespread attention from the academia. This paper sorts out the evolution of architectural knowledge representation methods in the history of architecture, and summarizes three progressive representation frameworks of their development with type, pattern and network. By searching these three keywords in the Web of Science Core Collection among 4867 publications from 1990 to 2021, the number of publications in the past 5 years raised more than 50%, which show significant research interest in architecture industry in recent years. Among them, the first two are static declarative knowledge representation methods, while the network-based knowledge representation method also includes procedural knowledge representation methods and provides a way for knowledge association. This means the network representation has more advantage in terms of the logical completeness of knowledge representation, and accounts for 67% of the current research on knowledge representation in architecture. In the context of the rapid development of artificial intelligence, this method can realize the construction of architectural knowledge system and greatly improve the work efficiency of the building industry. On the other hand, in the face of carbon-neutral sustainable development scenarios, using knowledge representation, building performance knowledge and design knowledge could be expressed in a unified manner, and a personalized and efficient workflow for performance-oriented scheme design and optimization would be achieved.
series	Architectural Intelligence
email
last changed	2025/01/09 15:00

_id	caadria2022_503
id	caadria2022_503
authors	Yousif, Shermeen and Vermisso, Emmanouil
year	2022
title	Towards AI-Assisted Design Workflows for an Expanded Design Space
doi	https://doi.org/10.52842/conf.caadria.2022.2.335
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. 335-344
summary	The scope of this paper is to formulate and evaluate the structure of a viable design workflow that combines a variety of computational tools and uses artificial intelligence (AI) to enhance the designer‚s capacity to explore design options within an expanded design space. In light of the autonomous and progressively post-anthropocentric generative capability of recent AI strategies for architectural design, we are interested in investigating some of the challenges involved in the insertion of such AI strategies into a new generative design system, involving data curation and the placement of any AI-assisted model in the overall workflow, as well as its (AI‚s) reciprocity with other computational methods such as discrete assembly and agent-based modeling. The paper presents our interrogation of the proposed AI-assisted framework, demonstrated in experiments of formulating multiple design workflows following different strategies. The workflow strategies show that integrating AI networks into a framework with other computational tools affords a different kind of design exploration than other methods; the prospect of novel solutions is heavily dependent on the interconnectedness of such methods and the dataset curation process. Collectively, the work contributes to innovation in architectural education and practice through enhancing scientific research (in line with UN Sustainable Development Goal 9).
keywords	Artificial Intelligence, Deep Learning, AI-assisted Design Workflows, Design Space Exploration, Generative Systems, SDG 9
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ecaade2023_317
id	ecaade2023_317
authors	Zamani, Alireza, Mohseni, Alale and Bertug Çapunaman, Özgüç
year	2023
title	Reconfigurable Formwork System for Vision-Informed Conformal Robotic 3D Printing
doi	https://doi.org/10.52842/conf.ecaade.2023.1.387
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 1, Graz, 20-22 September 2023, pp. 387–396
summary	Robotic additive manufacturing has garnered significant research and development interest due to its transformative potential in architecture, engineering, and construction as a cost-effective, material-efficient, and energy-saving fabrication method. However, despite its potential, conventional approaches heavily depend on meticulously optimized work environments, as robotic arms possess limited information regarding their immediate surroundings (Bechthold, 2010; Bechthold & King, 2013). Furthermore, such approaches are often restricted to planar build surfaces and slicing algorithms due to computational and physical practicality, which consequently limits the feasibility of robotic solutions in scenarios involving complex geometries and materials. Building on previous work (Çapunaman et al., 2022), this research investigates conformal 3D printing of clay using a 6 degrees-of-freedom robot arm and a vision-based sensing framework on parametrically reconfigurable tensile hyperbolic paraboloid (hypar) formwork. In this paper, we present the implementation details of the formwork system, share findings from preliminary testing of the proposed workflow, and demonstrate application feasibility through a design exercise that aims to fabricate unique components for a poly-hypar surface structure. The formwork system also offers parametric control over generating complex, non-planar tensile surfaces to be printed on. Within the scope of this workflow, the vision-based sensing framework is employed to generate a digital twin informing iterative tuning of the formwork geometry and conformal toolpath planning on scanned geometries. Additionally, we utilized the augmented fabrication framework to observe and analyze deformations in the printed clay body that occurs during air drying. The proposed workflow, in conjunction with the vision-based sensing framework and the reconfigurable formwork, aims to minimize time and material waste in custom formwork fabrication and printing support materials for complex geometric panels and shell structures.
keywords	Robotic Fabrication, Conformal 3D Printing, Additive Manufacturing, Computer-Vision, Reconfigurable Formwork
series	eCAADe
email
last changed	2023/12/10 10:49

_id	ascaad2022_004
id	ascaad2022_004
authors	Falih, Zahraa; Mahdavinejad, Mohammadjavad; Tarawneh, Deyala; Al-Mamaniori, Hamza
year	2022
title	Solar Energy Control Strategy using Interactive Modules
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. 117-138
summary	The concept of interactive canopy emerged as a notable manifestation of smart buildings in architectural endeavors, using artificial intelligence applications in computational architecture, interactive canopies came as a potential response for living organisms to combat external environmental changes as well as reduce energy consumption in buildings. This research aims to explore architecture with higher efficiency through the impact of environmentally technological factors on the design form by introducing solar energy into the design process through the implementation of interactive curtains that interact with the sun in the form of an umbrella. The main objective of the umbrellas is to protect the users from the sun's harmful rays. After designing an interactive cell using Grasshopper, the methodology follows an analytical and experimental approach, the analytical section is summarized by conducting a case study of multiple models and analyzing the techniques used in these models to discover the significant advantages and disadvantages of the design. While the experimental section demonstrates the mechanism for implementing the interactive modules. The research suggests that by designing an interactive canopy that responds to external changes and senses solar radiation in ways that when the intensity of solar radiation increases and the sun is perpendicular to the dynamic units, will lead to maintaining a more balanced level of illumination. The work efficiency is studied by simulating it by Climate Studio.
series	ASCAAD
email
last changed	2024/02/16 13:24

_id	ecaade2024_101
id	ecaade2024_101
authors	Yu, Jiaqi; Guo, Kening; Bai, Zishen; Wen, Zitong
year	2024
title	Application of Artificial Neural Network for Predicting U-Values of Building Envelopes in Temperate Zones
doi	https://doi.org/10.52842/conf.ecaade.2024.1.585
source	Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 1, pp. 585–592
summary	Due to the global energy deficit, building energy consumption has become a significant issue in recent years. Many researchers have focused on building energy consumption simulations to manage energy consumption accurately and provide a comfortable indoor environment for occupants. In building energy simulations, accurate input of building parameters is essential. As important thermal parameters, the thermal transmittance (U-value) of building envelopes can affect building operational energy consumption. In most building energy simulation studies, the U-value was set to the theoretical U-value which was a fixed value. However, the U-value constantly varies due to several environmental impacts, especially fluctuating air temperature and relative humidity (T/RH). Thus, the U-values are dynamic in actual situations, and inputting dynamic U-values into building energy simulations can reduce the gap between the simulation and the actual situation. In this study, the dynamic U-values of conventional cavity envelopes in temperate zones were predicted by an artificial neural network (ANN) model. Firstly, the in-situ dynamic U-value measurement was conducted in Sheffield, the UK, from summer to winter in 2022. The heat flow meter method was applied, and the tested envelope was a conventional cavity envelope widely used in the UK. The indoor and outdoor T/RH were measured and recorded as well. Then, the measured data were applied to train the optimal ANN model. The input parameters included the indoor and outdoor T/RH, and the output parameter was the dynamic U-value. Finally, the prediction results obtained by the optimal ANN model were closely correlated with the measured dynamic U-value. This quantitative study of dynamic U-values examined the relationship between dynamic U-values of conventional cavity envelopes and environmental factors, which can provide reliable information for improving the inputting patterns of building parameters and the accuracy of the building energy simulation.
keywords	Artificial Neural Network Model, In-situ U-value Measurement, Dynamic U-value Prediction, Conventional Cavity Envelopes
series	eCAADe
email
last changed	2024/11/17 22:05

_id	cdrf2022_150
id	cdrf2022_150
authors	Ana Zimbarg
year	2022
title	Mapping Plant Microclimates on Building Envelope Using Environmental Analysis Tools
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_13
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	Can we build our cities not only for humans but also for all living systems? How can we consider other species occupants of the built environment? Planning cities as an element of the natural domain can reshape our relationship with nature and help redefine sustainability in architecture. Although current design strategies of reducing energy use does not rectify past/continuing im-balances in the natural environment. Landscape architect John Tillman Lyle expanded the regenerative design concept based on a range of ecological concepts. The environment's complexity, and the urge to use resources smartly, encouraged him to think about architecture and the environment as a whole system. John Lyle's regenerative design strategies scaffold a conceptual framework of treating the building as part of the landscape. Environmental tools such as Ladybug can map out the different conditions surrounding the building's envelope. This information can assist in selecting and populating a building façade with suitable plant species. The framework presents the building as a feature in the landscape, creating microclimatic conditions for various plant habitats. This conceptual workflow has the potential to become a tool to include regenerative principles in the urban context.
series	cdrf
email
last changed	2024/05/29 14:02

_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_522
id	caadria2022_522
authors	Cheng, Sifan, Leung, Carson Ka Shut and van Ameijde, Jeroen
year	2022
title	Evaluating the Accessibility of Amenities toward Walkable Neighourhoods: an Integrated Method for Testing Alternatives in a Generative Urban Design Process
doi	https://doi.org/10.52842/conf.caadria.2022.1.495
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. 495-504
summary	Studies have shown that walkable communities reduce traffic-related pollution and the risk of chronic illnesses, promote economic growth and prosperity, and stimulate community participation and the growth of social capital. To assess the walkability of urban areas, various methodologies have been developed around shortest-distance calculations between various points of interest (POIs), yet their outcomes do not guide potential urban design improvements. The absence of appropriate measurements and procedures that may give quantitative and actionable feedback to support design decision-making is one of the primary issues in building walkable neighborhoods. The work presented in this paper revolves around a new workflow, that employed Urbano, a mobility simulation and assessment tool, and integrated it within a generative design process to allowing for the quantitative evaluation on amenity accessibility for several alternative design scenarios for a case study site in Mong Kok, Hong Kong. The results show how this data-driven urban design process benefits from generative techniques to produce solutions with improved contextual connectivity, energy-efficient urban form, and good quality public spaces that contribute to the walkability of neighbourhoods.
keywords	Generative Urban Design, Walkability, Urbano, SDG 3, SDG 11
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	ijac202220215
id	ijac202220215
authors	Gámez Bohórquez, Oscar; William Derigent; Hind Bril El-Haouzi
year	2022
title	Parametric point-cloud slicing for facade retrofitting
source	International Journal of Architectural Computing 2022, Vol. 20 - no. 2, pp. 434–452
summary	This work presents a method for retrieving 3D building contours usable in facade retrofitting projects, which uses a parametric modeling workflow that utilizes a point-cloud slicing method to retrieve such 3D contours. Since current commitments by European governments seek to reduce energy consumption as a means to reduce carbon emissions from building stock by 2050, facade retrofitting appears as an alternative for addressing operational and embedded building emissions. Within such a context, the main contribution of this work consists of a workflow and a 3D reconstruction solution that uses a parametric environment for capturing building topology and bypassing ground-level occlusions. A real case study and a strategy for converting 3D building contours into Industry Foundation Classes entities, directly from the parametric modeling environment, served as a scenario for testing the capabilities of a Grasshopper solution and open new perspectives for this approach.
keywords	Facade retrofitting, scan to building information modeling, parametric modeling, terrestrial laser scanning, building lifecycle
series	journal
last changed	2024/04/17 14:29

_id	caadria2022_239
id	caadria2022_239
authors	Huang, Chenyu, Zhang, Gengjia, Yin, Minggang and Yao, Jiawei
year	2022
title	Energy-driven Intelligent Generative Urban Design, Based on Deep Reinforcement Learning Method With a Nested Deep Q-R Network
doi	https://doi.org/10.52842/conf.caadria.2022.1.233
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. 233-242
summary	To attain "carbon neutrality," lowering urban energy use and increasing the use of renewable resources have become critical concerns for urban planning and architectural design. Traditional energy consumption evaluation tools have a high operational threshold, requiring specific parameter settings and cross-disciplinary knowledge of building physics. As a result, it is difficult for architects to manage energy issues through 'trial and error' in the design process. The purpose of this study is to develop an automated workflow capable of providing urban configurations that minimizing the energy use while maximizing rooftop photovoltaic power potential. Based on shape grammar, parametric meta models of three different urban forms were developed and batch simulated for its energy performance. Deep reinforcement learning (DRL) is introduced to find the optimal solution of the urban geometry. A neural network was created to fit a real-time mapping of urban form indicators to energy performance and was utilized to predict reward for the DRL process, namely a Deep R-Network, while nested within a Deep Q-Network. The workflow proposed in this paper promotes efficiency in optimizing the energy performance of solutions in the early stages of design, as well as facilitating a collaborative design process with human-machine interaction.
keywords	energy-driven urban design, intelligent generative design, rooftop photovoltaic power, deep reinforcement learning, SDG 11, SDG 12
series	CAADRIA
email
last changed	2022/07/22 07:34

_id	architectural_intelligence2022_14
id	architectural_intelligence2022_14
authors	Philip F. Yuan, Xinjie Zhou, Hao Wu, Liming Zhang, Lijie Guo, Yun Shi, Zhe Lin, Jinyu Bai, Youhai Yu & Shanglu Yang
year	2022
title	Robotic 3D printed lunar bionic architecture based on lunar regolith selective laser sintering technology
doi	https://doi.org/https://doi.org/10.1007/s44223-022-00014-9
source	Architectural Intelligence Journal
summary	The lunar base is not only an experimental station for extraterrestrial space exploration but also a dwelling for humans performing this exploration. Building a lunar base presents numerous obstacles and requires environmental perception, feedback design, and construction methods. An integrated fabrication process that incorporates design, 3D printing workflow, and construction details to build a bionic, reconfigurable and high-performance lunar base prototype is presented in this paper. The research comprises the study of the lunar regolith 3D printing mechanism, the real-time control of powder laying and compaction procedure, and the development of a 3D printing tool end system. In this paper, many scientific questions regarding in situ fabrication on the lunar surface are raised and addressed with the proposal of a progressive optimization design method, the molding principle, and gradation strategy of lunar soil-polyaryletherketone (PAEK) hybrid powder, and the principle of dual-light field 3D laser printing. The feasibility of the technical strategy proposed in this paper is verified by the presented empirical samples.
series	Architectural Intelligence
email
last changed	2025/01/09 15:00

_id	ascaad2022_030
id	ascaad2022_030
authors	Sun, Yuan; Wang, Zhu
year	2022
title	Construction Based on Man-Machine Collaboration: A Case Study of a Bamboo Pavilion
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. 503-514
summary	With the development of advanced digital design approaches and mechanical facilities, architectural intelligence liberates conventional construction from conventional paradigms. Computational design and digital fabrication have achieved progress in space innovation, construction efficiency, and material effectiveness. However, those high-tech manufacturing techniques are not widely available in developing countries, where the locals used to carry construction experience from age to age in a nonacademic way. This study explored a collaborative workflow of complex structural design and machine-aided construction in Chinese rural areas. First, we designed a bamboo pavilion parametrically in an irregular site on a hill. Second, its primary structure was optimized based on determining critical load and earthquake resistance to meet local building codes. Then, before material processing, every bamboo component was numbered by algorithm, with its location and morphological data of length and radian calculated accurately on the construction drawings. In the transitional process from the conventional paradigm by experience towards man-machine collaboration, local workers' manual techniques helped minimize construction errors and improve details, which were not adequately predicted and considered beforehand. This study case suggested that respective advantages of both traditional and digital modes should be integrated and balanced based on collaboration between local construction workers and professional researchers, especially as a social role for future vernacular architecture practice.
series	ASCAAD
email
last changed	2024/02/16 13:24

_id	cdrf2022_488
id	cdrf2022_488
authors	Tomás Vivanco, Juan Eduardo Ojeda, Philip Yuan
year	2022
title	Regression-Based Inductive Reconstruction of Shell Auxetic Structures
doi	https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_42
source	Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022)
summary	This article presents the design process for generating a shell-like structure from an activated bent auxetic surface through an inductive process based on applying deep learning algorithms to predict a numeric value of geometrical features. The process developed under the Material Intelligence Workflow applied to the development of (1) a computational simulation of the mechanical and physical behaviour of an activated auxetic surface, (2) the generation of a geometrical dataset composed of six geometric features with 3,000 values each, (3) the construction and training of a regression Deep Neuronal Network (DNN) model, (4) the prediction of the geometric feature of the auxetic surface's pattern distance, and (5) the reconstruction of a new shell based on the predicted value. This process consistently reduces the computational power and simulation time to produce digital prototypes by integrating AI-based algorithms into material computation design processes.
series	cdrf
email
last changed	2024/05/29 14:03

_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	ecaade2022_146
id	ecaade2022_146
authors	Yin, Zihan, Wang, Likai and Ji, Guohua
year	2022
title	Wind-driven Design Optimization of Building Layouts - A case study of the residential building neighborhoods
doi	https://doi.org/10.52842/conf.ecaade.2022.1.629
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. 629–638
summary	As one of the most important factors for urban microclimate, the wind environment is crucial for conserving energy, reducing carbon emissions, diluting pollutants, and improving human comfort. This paper attempts to explore wind-driven design optimization of building layouts, and a residential neighborhood in Nanjing China is chosen as a case study. The workflow combines parametric modeling, FFD simulation, and optimization tool on the Rhino-Grasshopper platform. To increase the diversity of layout design variants, three design strategies are integrated into the parametric modeling. In particular, the disposition of the open space is taken into account because it plays an important role in the outdoor wind environment. GH_Wind and Octopus plug-ins are used for FFD solving and optimization respectively. The optimization process is carried out under two different wind conditions. The results provide optimal configurations of the building layout parameters for improving the outdoor wind environment.
keywords	Design Optimization, Building Layout, Wind Environment, Fast Fluid Dynamics, Open Space
series	eCAADe
email
last changed	2024/04/22 07:10

_id	ecaade2022_158
id	ecaade2022_158
authors	Zhao, Xingjian, Wang, Tsung-Hsien and Peng, Chengzhi
year	2022
title	Automatic Room Type Classification using Machine Learning for Two-Dimensional Residential Building Plans
doi	https://doi.org/10.52842/conf.ecaade.2022.2.593
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 593–600
summary	Building plan semantic retrieval is of interest in every stage of construction and facility management processes. A conceptual design model with a space layout can be used for the early building evaluation, such as functional spatial validation, circulation and security checking, cost estimation, and preliminary energy consumption simulation. With the development of information technology, existing machine learning methods applied to semantic segmentation of building plan images have successfully identified building elements such as doors, windows, and walls. However, for the higher level of room type/function recognition, the prediction accuracy is low when building plans do not contain sufficient details such as furniture. In this paper, we present a workflow and a predictive model for residential room type classification. Given a building plan image, the building elements are first identified, followed by room feature extraction by connectivity and morphological characterization using a rule-based algorithm. The Multi-Layer Perceptron (MLP) is trained with the feature set and then predicts the room type of test samples. We collected 1,586 residential room samples from 165 building layout plans and categorized rooms into nine types. Finally, our current model can achieve a classification accuracy of 0.82.
keywords	Floor Plan Semantic Retrieval, Room Type Classification, Machine Learning
series	eCAADe
email
last changed	2024/04/22 07:10

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