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	ecaade2024_35
id	ecaade2024_35
authors	Agkathidis, Asterios; Song, Yang; Symeonidou, Ioanna
year	2024
title	AI-Assisted Design: Utilising artificial intelligence as a generative form-finding tool in architectural design studio teaching
doi	https://doi.org/10.52842/conf.ecaade.2024.2.619
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 2, pp. 619–628
summary	Artificial Intelligence (AI) tools are currently making a dynamic appearance in the architectural realm. Social media are being bombarded by word-to-image/image-to-image generated illustrations of fictive buildings generated by tools such as ‘Midjourney’, ‘DALL-E’, ‘Stable Diffusion’ and others. Architects appear to be fascinated by the rapidly generated and inspiring ‘designs’ while others criticise them as superficial and formalistic. In continuation to previous research on Generative Design, (Agkathidis, 2015), this paper aims to investigate whether there is an appropriate way to integrate these new technologies as a generative tool in the educational architectural design process. To answer this question, we developed a design workflow consisting of four phases and tested it for two semesters in an architectural design studio in parallel to other studio units using conventional design methods but working on the same site. The studio outputs were evaluated by guest critics, moderators and external examiners. Furthermore, the design framework was evaluated by the students through an anonymous survey. Our findings highlight the advantages and challenges of the utilisation of AI image synthesis tools in the educational design process of an architectural design approach.
keywords	AI, GAI, Generative Design, Design Education
series	eCAADe
email
last changed	2024/11/17 22:05

_id	ecaade2024_47
id	ecaade2024_47
authors	Alymani, Abdulrahman Ahmed A
year	2024
title	Integrating Artificial Intelligence Rendering Tools in Design: Integrating AI as teaching methods in architectural education
doi	https://doi.org/10.52842/conf.ecaade.2024.2.629
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 2, pp. 629–638
summary	This paper introduces an innovative teaching approach for architectural design studios, emphasizing the integration of AI-rendering tools to enhance student learning and creativity. The method begins with conventional site analysis, followed by an in-depth study of a micro-home case study to deepen understanding. Students’ progress from traditional 2D plans to conceptual 3D massing, facing challenges in integrating case studies into their designs. To address this, an AI-rendering engine is incorporated, allowing students to add intricate details and apply various case studies directly onto their 3D models. This visual approach aids understanding and application of architectural concepts. The paper discusses how this approach helps students overcome integration challenges and fosters creative exploration. Findings suggest that this method enriches architectural education, offering a new dimension to design studio learning.
keywords	Architectural Pedagogy, AI-Rendering Tools, Architecture Precedents, Architecture Case Study, Design Studios
series	eCAADe
email
last changed	2024/11/17 22:05

_id	caadria2024_202
id	caadria2024_202
authors	Ataman, Cem, Tunçer, Bige and Perrault, Simon
year	2024
title	Digital Participation in Urban Design and Planning: Addressing Data Translation Challenges in Urban Policy- and Decision-Making through Visualization Techniques
doi	https://doi.org/10.52842/conf.caadria.2024.2.201
source	Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 2, pp. 201–210
summary	Digital technologies and online platforms, such as e-participation and crowdsourcing tools, are revolutionizing citizen engagement in urban design and planning by enabling large-scale, asynchronous, and individual participation processes. This evolution towards more inclusive and representative decision- and policy-making, however, presents a significant challenge: the effective utilization of the vast amounts of textual data generated. This difficulty arises from distilling the most relevant information from the extensive datasets and the lack of suitable methodologies for the visual representation of qualitative data in urban practices. Addressing this, the paper deploys AI-based analysis methods, including Natural Language Processing (NLP), Topic Modeling (TM), and sentiment analysis, to efficiently analyze these datasets and extract relevant information. It then advances into the realm of data representation, proposing innovative approaches for the visual translation of this textual data into multi-layered narratives. These approaches, designed to comply with a comprehensive set of both quantitative and qualitative interpretation criteria, aim to offer deeper insights, thus fostering equitable and inclusive governance. The goal of this research is to harness the power of qualitative textual data derived from online participation platforms to inform and enhance decision- and policy-making processes in urban design and planning, thereby contributing to more informed, inclusive, and effective urban governance.
keywords	Digital Participation, Textual Big Data, Natural Language Processing, Spatial Data Analytics, Data Visualization
series	CAADRIA
email
last changed	2024/11/17 22:05

_id	architectural_intelligence2024_35
id	architectural_intelligence2024_35
authors	Azuka Odiah & Samuel D. Gosling
year	2024
title	Laying the foundations for using generative AI images in architectural research: do images convey the intended spaces and ambiances?
doi	https://doi.org/https://doi.org/10.1007/s44223-024-00076-x
source	Architectural Intelligence Journal
summary	The advent of Generative Artificial Intelligence (GenAI) models, such as Stable Diffusion, Open AI's DALL-E2, and MidJourney, has opened the door to a huge range of new possibilities in architectural research and practice. Before architects and researchers can fully leverage these models, it is crucial to assess their proficiency in generating images that accurately depict real spaces and ambiances. Here, we assessed the proficiency of DALL-E2 in depicting intended spaces and intended ambiances, using real photographs from Google Images as a comparative benchmark. Images of eighteen distinct home spaces, each intended to evoke three different ambiances, were generated and presented to human observers. The images were evaluated in terms of their perceived realism, their accuracy in depicting the space and ambiance intended. Findings highlight GenAI's capability in depicting spaces and point to its potential in convincingly conveying ambiances. Beyond the substantive findings, this research highlights the importance of evaluating the rapidly emerging tools enabled by recent developments in AI.
series	Architectural Intelligence
email
last changed	2025/01/09 15:05

_id	ecaade2024_57
id	ecaade2024_57
authors	Bank, Mathias; Schlusche, Johannes; Rasoulzadeh, Shervin; Schinegger, Kristina; Rutzinger, Stefan
year	2024
title	Diffused Tomography - Design ideation on architectural models through image sequences
doi	https://doi.org/10.52842/conf.ecaade.2024.2.537
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 2, pp. 537–546
summary	The paper outlines a novel methodology for applying AI-driven style transfer to complex 3D architectural models. It involves a sequential process of slicing, training, video-guided diffusion, and reconstruction to transform existing 3D models based on textual descriptions into new stylistic forms. This approach enables architects to explore diverse design concepts, focusing on spatial composition, visual appearance, and tectonics. The results demonstrate the potential of AI in enhancing early-stage design ideation, offering new perspectives on interior-exterior relationships in architecture through AI-generated 3D models.
keywords	AI, Video Diffusion, Architectural Design, Form finding, Concept model, Ideation
series	eCAADe
email
last changed	2024/11/17 22:05

_id	ijac202322202
id	ijac202322202
authors	Baudoux, Gaëlle
year	2024
title	The benefits and challenges of artificial intelligence image generators for architectural ideation: Study of an alternative human-machine co-creation exchange based on sketch recognition
source	International Journal of Architectural Computing 2024, Vol. 22 - no. 2, 1-15
summary	This paper deals with creative co-design between human and machine. It presents an alternative design method based on an emerging technology of sketch interpretation to support co-creation and collaborative creativity in architecture. This technology embraces spontaneity in design by generating inspirational images linked to the architect’s sketches. Our research aims to determine the benefits and challenges of this alternative instrumentation. We are developing a Wizard of Oz test method by immersing several designers in a studio instrumented by this human-machine co-creation technology. We analyze quantitatively and qualitatively the single-designer ideation activity of these subjects. We then investigate the integration of this co-creation instrumentation within the framework of a team design involving several architects. This confirms known benefits such as speeding-up and freeing-up of ideation and highlights the need for designers to evaluate sketched ideas by means of images simulating their real-life rendering, as well as the need for inspiration to materialize the premises of ideas that are still vague.
keywords	Architectural creativity, analogical reasoning, AI image generator, co-design, design ideation
series	journal
last changed	2024/07/18 13:03

_id	caadria2024_128
id	caadria2024_128
authors	Bauscher, Erik, Dai, Anni, Elshani, Diellza and Wortmann, Thomas
year	2024
title	Learning and Generating Spatial Concepts of Modernist Architecture via Graph Machine Learning
doi	https://doi.org/10.52842/conf.caadria.2024.1.159
source	Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 1, pp. 159–168
summary	This project showcases a use case away from most other research in the field of generative AI in architecture. We present a workflow to generate new, three-dimensional spatial configurations of buildings by sampling the latent space of a graph auto-encoder. Graph representations of three-dimensional buildings can store more data and hence reduce the loss of information from building to machine learning model compared to image- and voxel-based representations. Graphs do not only represent information about elements (nodes/pixels/etc.) but also the relationships between elements (edges). This is specifically helpful in architecture where we define space as an assemblage of physical elements which are all somehow connected (i.e., wall touches floor). Our method generates valuable, logical and original geometries that represent the architectural style chosen in the training data. These geometries are highly different from any image-based generation process and justify the importance of graph-based 3D geometry generation of architecture via machine learning. The method also introduces a novel conversion process from architecture to graph, an adapted decoder architecture, and a physical prototype to control the generation process, all making generative machine learning more applicable to a real-world scenario of designing a building.
keywords	generative 3D architecture, generative graph machine learning, graph-based architecture, human-computer interaction, graph autoencoder, latentwalk
series	CAADRIA
email
last changed	2024/11/17 22:05

_id	caadria2024_284
id	caadria2024_284
authors	Calixto, Victor and Croffi, Juliana
year	2024
title	Back to Black Boxes? An Urgent Call for Discussing the Impacts of the Emergent AI-Driven Tools in the Architecture Design Education
doi	https://doi.org/10.52842/conf.caadria.2024.3.039
source	Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 3, pp. 39–48
summary	In recent years, the advances in data science and Artificial Intelligence (AI) are disrupting all sectors, impacting the industry and academic fields. In the AEC sector, there have been a rising number of user-friendly "computational design services" generative and parameterised solutions driven by AI engines. However, if in one hand these services provide rapid solutions with minimal cognitive load, on the other hand, they obscure logical processes from computational design thinking, transforming them into black boxes and limiting the designer on making use of technology to create novel solutions. To overcome these challenges, the teaching of computational design thinking should be integrated in architecture education on undergraduate and master programs. This study conducts a critical literature review and proposes a framework to be implemented in architecture education, discussing the complexity involved in the learning process. The framework provides a layered approach that unfold the levels of abstraction of the nested black boxes of computational design and AI in an educational context.
keywords	Computational Design Thinking, Architecture Education, Black Box, AI
series	CAADRIA
email
last changed	2024/11/17 22:05

_id	ecaade2024_363
id	ecaade2024_363
authors	Doumpioti, Christina; Huang, Jeffrey
year	2024
title	Collaborative Design with Generative AI and Collage: Enhancing creativity and participation in education
doi	https://doi.org/10.52842/conf.ecaade.2024.2.445
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 2, pp. 445–454
summary	This study explores the integration of visual generative artificial intelligence (VGI) tools in the early stages of urban design and communication within a UX design course. Students developed urban design concepts from initial ideas to visual prototypes, promoting creativity and collaboration. The research investigates the role of VGI in the ideation process and its potential as a participatory tool for urban regeneration. Collage, reappropriated as a collaborative technique, works alongside generative AI to aid in creating and refining ideas. Additionally, the study presents a method for creating before-and-after images, providing a dynamic way to visualise urban evolution. Through an analysis of student feedback and project outcomes, the study identifies both the benefits and limitations of using generative AI in design education, aiming to improve AI interfaces and their application in collaborative design contexts. The findings suggest that VGI can impact design thinking and practice, serving as both a creative tool and a means to facilitate inclusive and insightful decision-making processes.
keywords	Visual Generative Artificial Intelligence (VGI), Collage, Blending, Midjourney, Text-to-image, Image-to-image, Creativity, Ideation, Prompt Engineering, Design
series	eCAADe
email
last changed	2024/11/17 22:05

_id	architectural_intelligence2024_24
id	architectural_intelligence2024_24
authors	Giuseppe Bono
year	2024
title	Text-to-building: experiments with AI-generated 3D geometry for building design and structure generation
doi	https://doi.org/https://doi.org/10.1007/s44223-024-00060-5
source	Architectural Intelligence Journal
summary	The paper seeks to investigate novel potentials for building design and structure generation that arise at the intersection of computational design and AI-generated 3D geometries. Although the use of AI technologies is exponentially increasing inside the architectural discipline, the design of spatial building configurations using AI-generated 3D geometries is still limited in its applications and represents an ongoing field of investigation in advanced architectural research. In this regard, several questions still need to be answered: how can we design new building typologies from AI-generated 3D geometries? And how can we use these typologies to shape both the real and the virtual world? The paper proposes a new approach to architectural design where artificial intelligence is used as the starting point for design exploration, while computational design procedures are employed to convert AI-generated 3D geometries into building elements – such as columns, beams, horizontal and vertical surfaces. The paper starts with a general overview of the current use of artificial intelligence inside the architectural discipline, and then it moves towards the explanation of specific AI generative models for 3D geometry reconstruction and representation. Subsequently, the proposed working pipeline is analysed in more detail – from the creation of 3D geometries using generative AI models to the conversion of such geometries into building elements that can be further designed and optimised using computational design tools and methods. The results shown in the paper are achieved using Shap-E as the main AI model, though the proposed pipeline can be implemented with multiple AI models. The paper ends by showing some of the generated results, finally adding some considerations to the relationship between human and artificial creativity inside the architectural discipline. The work presented in the paper suggests that the use of computational design tools and methods combined with the tectonics of the latent space opens new opportunities for topological and typological explorations. In a time where traditional architectural typologies are moving towards stagnation due to their inability to satisfy new human needs and ways of living, exploring AI-based working pipelines related to architectural design allows the definition of new design solutions for the generation of new architectural spaces. In doing so, the serendipitous aspect of AI biases is used as an auxiliary force to inform design decisions, promoting the discovery of a new inbuilt dynamism between human and artificial creativity. In a time where AI is everywhere, understanding the measure of such dynamism represents a key aspect for the future of the architectural discipline.
series	Architectural Intelligence
email
last changed	2025/01/09 15:05

_id	caadria2024_64
id	caadria2024_64
authors	Hadiatmadja, Juniarto
year	2024
title	Review on the Use of Conversational AI NPC Avatars in Teaching and Learning BIM: A Preliminary Observation of Its Introduction in a Built Environment Related Course in Singapore
doi	https://doi.org/10.52842/conf.caadria.2024.1.261
source	Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 1, pp. 261–270
summary	Abilities in the use of BIM are critically needed in many industries but there are major challenges to current BIM training. It is unrealistic to assume that the current predominantly teacher directive mode of BIM training is sufficient or responsive enough to tackle rapidly changing challenges and cater to individual pursuits. This article reviews the findings of a research deploying conversational AI NPC avatars and BIM models in a game engine environment as a complementary learning tool that is non-directive and more enquiry based in nature. Enabling learners to autonomously converse and spatially direct the avatar movements to parts of the BIM model they wish to focus on. This article answers some ways the use of AI NPC avatars could benefit the learning of students that are newly introduced to BIM. The research compares tangible results as well as the learner's perceptions toward the use AI NPC avatars. The findings shed light on the technology's current utility and limitations in various aspects of the current topic. Some directions for development of future related research will be also be discussed.
keywords	BIM training, conversational AI NPC avatars, game engine environment, individual enquiry, learning tools
series	CAADRIA
email
last changed	2024/11/17 22:05

_id	ecaade2024_257
id	ecaade2024_257
authors	Haridis, Alexandros
year	2024
title	Formulating the Generative: History, logic, and status of computing designs in a latent space
doi	https://doi.org/10.52842/conf.ecaade.2024.2.261
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 2, pp. 261–270
summary	This paper separates out and finds relations between individuals and the historical contexts in which they operated who formulated conceptual and computational interpretations of the term generative. It synthesizes literature published from the 1500s to the 1950s, beginning with the Art of Combinations (1500s—) and the pursuit of universal generative languages, leading to the information-processing systems view and heuristic programming (1950s—), the first long-term research program in AI and cognitive science. The present data-driven formulation of the generative enabled by engineering achievements in AI technology has not fundamentally changed a long-standing vision: design or creation as computation remains a kind of mechanized search of design options in a ‘latent space of possibilities.’ Understanding the generative in this way will enable researchers and educators in design, art and science to resolve various controversies that rage today—between cognitivist and connectionist approaches to creativity, between “rules” and “data.”
keywords	Generative, Design Computing, Artificial Intelligence, Computing History
series	eCAADe
email
last changed	2024/11/17 22:05

_id	caadria2024_486
id	caadria2024_486
authors	Hartanto, Elissa, Chen, Ashley and Koh, Immanuel
year	2024
title	Empirical Insights into Architectural Aesthetics: A Neuroscientific Perspective
doi	https://doi.org/10.52842/conf.caadria.2024.3.069
source	Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 3, pp. 69–78
summary	What makes a design beautiful? Design styles developed during past eras such as byzantine, classical, gothic, renaissance and baroque are universally admired as being cultural icons and are widely appreciated by people from all walks of life. Throughout the years, many philosophers, architects and physicists have come up with theories and frameworks to measure the subjective topic of aesthetics, but none stood out like Birkhoff’s aesthetic measure which used a mathematical approach to quantifying beauty. In this paper, we investigate the aesthetic appeal of generative AI model outputs, trained on datasets recognized for their aesthetic quality, and by employing biometric data analysis to cross-reference these results with Birkhoff's aesthetic measurement framework. Stemming from Neuroarchitecture, wearable technologies offer an insight into the correlation between spatial qualities and human perception that can be extended into aiding us, architects in designing better for the built environment. In our experiment, we generated a set of interior images in assorted styles following current interior design trends. The generated outputs are first scored based on Birkhoff’s measurements of aesthetics and cross referenced with data obtained from wearable technologies such as an eye tracker and electroencephalogram (EEG) headset. Eye tracking glasses can detect fixations, saccade patterns, and pupil dilation, which can reflect subconscious thoughts from the user. The EEG is also utilised to complement the eye tracking data as a means to reflect on positive or negative impressions towards a particular subject. Overall, this innovative approach adapts Birkhoff's aesthetic measurement in a human-centric and evidence-based way, providing architects with a framework to systematically evaluate design. It merges Birkhoff's theorem with unbiased subconscious metrics to compare current and historical aesthetic trends, and behavioural research to pinpoint common aesthetic preferences. This method also leverages biometric data to align architectural design more closely with user perspectives, breaking down traditional communication barriers and offering clearer insights into client preferences.
keywords	Neuroaesthetics, Neuroscience, Generative Design, Eye-tracking, Wearable technology, Biometrics, Machine Learning
series	CAADRIA
email
last changed	2024/11/17 22:05

_id	ecaade2024_78
id	ecaade2024_78
authors	Hermund, Anders; Oxvig, Henrik; Bang, Jacob Sebastian
year	2024
title	Embracing the Creative Nexus: Integrating artificial intelligence, philosophy, and artistic discourse in architectural education
doi	https://doi.org/10.52842/conf.ecaade.2024.2.665
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 2, pp. 665–674
summary	In the realm of architectural education, a multidisciplinary approach that unites art, science, and philosophy can catalyze novel perspectives. This paper outlines the intentions and outcome of a PhD course, "Artificial Intelligence in Architectural Research," inviting exploration of these diverse domains within architectural pedagogy. The research question in relation to establishing novel ways of embracing AI technology while maintaining a focus on the core values of teaching architectural research methodologies is thus: how can we through art, philosophy, and scientific approaches teach the principles and applications of artificial intelligence for architects? The paper will introduce how we establish a didactical framing marrying artistic, scientific, and philosophical facets to empower students to reimagine architectural practice in the age of AI. They gain tools for visionary architecture that embraces technology and reflects on societal and philosophical dimensions. We discuss the outcome of the course by examining the students’ work and feedback and conclude that the intention of the course methodology can be traced throughout the process from analogue to digital and that valuable novel realizations and understandings are created by persistently insisting on a cross-disciplinary approach to AI in relation to architectural research and creation.
keywords	Architectural Education, Artificial Intelligence, A.I. Text-to-Image Generation, Multidisciplinary Approach, Philosophy, Creative Process, Ethical Considerations
series	eCAADe
email
last changed	2024/11/17 22:05

_id	caadria2024_47
id	caadria2024_47
authors	Hu, Wei
year	2024
title	DSNL in Architecture – A Deep Learning Approach to Deciphering Architectural Sketches and Facilitating Human-AI Interaction
doi	https://doi.org/10.52842/conf.caadria.2024.1.119
source	Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 1, pp. 119–128
summary	The language of interaction between architects and machines has been evolving towards a more user-friendly paradigm. As the capabilities of machines and artificial intelligence have advanced, it has become increasingly feasible for architects to communicate with machines using their customary expressive methods. Consequently, this has led to the development of Domain-Specific Natural Language (DSNL), which, unlike traditional Domain-Specific Language (DSL), places greater emphasis on naturalness. While this naturalness enhances usability for architects, it also presents challenges in machine comprehension. To address this issue, we propose a data-driven approach that utilizes domain-specific data for model training or fine-tuning through unsupervised or weakly supervised methods. Our study, which focuses on teaching AI to learn architectural sketching from architects, demonstrates that our proposed method captures the characteristics of human architectural sketching more effectively than traditional approaches.
keywords	Domain Specific Natural Language, Human-AI interaction, Architectural sketches, AIGC, Deep learning.
series	CAADRIA
email
last changed	2024/11/17 22:05

_id	caadria2024_339
id	caadria2024_339
authors	Kang, Se Yeon, Cho, Ju Eun and Jun, Han Jong
year	2024
title	Electroencephalogram (EEG) based Emotional Lighting Design Using Deep-Learning for a User-Centric Approach
doi	https://doi.org/10.52842/conf.caadria.2024.3.391
source	Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 3, pp. 391–400
summary	This study proposes a methodology for using artificial intelligence (AI) and biometrics in spatial design. The research mainly applies a gated recurrent unit (GRU) model, a recurrent neural network (RNN), to analyze electroencephalogram (EEG) data and dynamically adjust lighting according to the user's emotional state. This study suggests an illumination adjustment system that modifies lighting according to the user's emotional state using the proposed method. Integration of EEG data can overcome the limitations of lighting systems. It can effectively target individual emotional responses. The GRU model represents a significant improvement in lighting design by addressing both cognitive and emotional user needs. The model's effectiveness in processing real-time data and adapting through incremental learning was evaluated. The model has shown a significant impact on emotional architecture and spatial design, with a focus on individual experience.
keywords	Gated Recurrent Unit, EEG, EEG Data Analysis, User-Centric Design, Emotional Lighting, Real-Time Data Processing, Affective Computing, BCI, BMI
series	CAADRIA
email
last changed	2024/11/17 22:05

_id	caadria2024_206
id	caadria2024_206
authors	Kasai, Ai and Ishizawa, Tsukasa
year	2024
title	More Words from Facility Operation Managers: Text-based Building Information for Inclusive Accessibility
doi	https://doi.org/10.52842/conf.caadria.2024.1.241
source	Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 1, pp. 241–250
summary	In the domain of Facility Management (FM), the implementation of Building Information Modelling (BIM) poses significant challenges for the Facility Operation Manager (FOMr) in terms of effective integration into their regular workflow. Employing the Soft Systems Methodology (SSM), this study highlights a conducive environment that enhances the usability of BIM for the FOMr. By expanding the application scenarios of BIM within the FM field, we contribute to completing the lifecycle of building models. We introduce 'Text-based Building Information' (TxBI), a system designed to complement Industry Foundation Classes (IFC) while emphasizing a text-centric approach. TxBI strategically focuses on systematically integrating Mechanical, Electrical, and Plumbing (MEP) components, which are pivotal for daily FM operations. It generates a simple yet versatile dataset derived from the as-built BIM deliverable, channeling it to Computerized Maintenance Management Systems (CMMS) and streamlined 3D systems. The utility of TxBI is exemplified through its application in commercial software environments, showcasing the creation of lightweight digital twins of completed buildings. Significantly enhancing FOMrs’ access to building information, TxBI establishes a more versatile data environment for all building stakeholders. The accumulation of comprehensive information throughout the Operation and Maintenance (O&M) phase culminates in enhanced asset management and extended longevity of buildings.
keywords	BIM for FM, operation and management, facility operation managers, text-based building information, long-life building, real time visualization from text, digital twins
series	CAADRIA
email
last changed	2024/11/17 22:05

_id	caadria2024_369
id	caadria2024_369
authors	Katsangelou, Smaro, Wilmoth, Parker, Pados, Dimitris A. and Vermisso, Emmanouil
year	2024
title	Latent Petroglyphs: Pattern Extraction From Prehistoric Rock Art Through Generative Workflows for a Design Project in Greece
doi	https://doi.org/10.52842/conf.caadria.2024.1.149
source	Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 1, pp. 149–158
summary	This paper regards the translation of indigenous rock art (petroglyphs) into training data for deep learning algorithms. Vis-a-vis the recent popularity of pre-trained AI models, the authors examine the potential of domain-specific search procedures to inform the process for a building design in Greece. Petroglyphs are a primitive form of artistic expression which has survived through the ages due to the medium upon which it was engraved. The practical aspect of this art was navigating through nature. The “Rock Art Center” aims to exhibit the narratives and culture behind rock art scattered in the mountains. Considering the adoption of generative adversarial networks (GANs) in the architectural workflow, the landscape and local prehistoric graffiti are viewed as datasets for tackling different design decisions, formally and conceptually interrogating the project’s scope. The existing rock art sites serve as the primary dataset to explore the building’s form, by accessing the 'latent' space of prehistoric rock art and its interpolation with the demands of the project. A number of algorithms and digital tools is employed to interpret the data in question.
keywords	Artificial Intelligence, Deep Learning, Architectural Ideation, Design Workflow, Image Generation
series	CAADRIA
email
last changed	2024/11/17 22:05

_id	caadria2024_325
id	caadria2024_325
authors	Kim, Dongyun and Kim, Hanjun
year	2024
title	Territorial Sabotage: From Tracing Seoul’s Possibilities to Recompositing Its Urban Identity
doi	https://doi.org/10.52842/conf.caadria.2024.2.159
source	Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 2, pp. 159–168
summary	This paper explores the evolution of architecture within an urban scale, utilizing Generative Adversarial Networks (GANs) to increase diversity and suggest various alternatives. Drawing inspiration from Henri Bergson's concepts of creative evolution, GANs' non-deterministic nature echoes Bergson's emphasis on creativity within evolutionary processes in urban design. Leveraging GANs' latent space, this study envisions a framework for AI-driven architectural generation, merging Bergson's ideas of creative intuition with AI's adaptive potential. Using Seoul as a case study, integrating Kevin Lynch's principles and symbolic representation techniques like the Nolli map, the research navigates urban spaces to create cohesive morphologies. Employing 2D GAN-based analysis and integrating 3D GAN, the study discerns urban layouts and building configurations. Additional diffusion models refine the 3D GAN outputs, expediting rendering and visualization phases, suggesting an innovative, data-driven architectural design methodology. By amalgamating diverse AI models into a cohesive workflow, it blends traditional architectural wisdom with cutting-edge computational capabilities, heralding a paradigm shift in architectural innovation.
keywords	Generative Adversarial Networks, 3D GAN, Stable Diffusion, Cartography, Nolli map
series	CAADRIA
email
last changed	2024/11/17 22:05

_id	caadria2024_148
id	caadria2024_148
authors	Kimm, Geoff, White, Marcus and Burry, Mark
year	2024
title	Adapting the Software Design Pattern Model for AI-Enabled Design Computing
doi	https://doi.org/10.52842/conf.caadria.2024.1.049
source	Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 1, pp. 49–58
summary	Exponential AI development requires an adaptation to new technology by traditionally reluctant architects and allied practitioners. This paper examines the potential of the software design pattern (SDP) model, used in software engineering to capture and reapply designs, as one underpinning. Patterns have creativity and pedagogical benefits in parametric modelling, yet consideration of AI and broader design computing as well as the derivation and versatility implied by an SDP model are underexamined. This research questions how, in an AI context, new patterns may evolve for varied AI levels and non-geometrical features. It is undertaken in the Unity game engine with critical application of two prominent extant patterns as a computational workflow design response to a real-world citizen engagement scenario. A novel, feature-agnostic pattern is derived with a simple AI model and is verified for other AI models. The work concludes design computing patterns can abstract existing pattern knowledge to flexibly evolve and apply across rapidly changing AI-enabled design computing contexts and thereby assist practitioners to positively respond to AI advances.
keywords	artificial intelligence, computational design, software design patterns, architectural practice, Unity 3D, intelligent agents
series	CAADRIA
email
last changed	2024/11/17 22:05

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