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	sigradi2023_439
id	sigradi2023_439
authors	Chornobai, Sara Regiane, Paiva Ponzio, Angelica, Miotto Bruscato, Léia and Machado Fagundes, Cristian Vinicius
year	2023
title	Fostering Sustainability in the Early Stages of the Architectural Design process: a Creative Exploration of AI Generative Models
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 831–842
summary	The field of architecture is experiencing transformative changes with the rise of Artificial Intelligence (AI). This study investigates the use of generative models like Large Language Models (LLM) and Generative Diffusion Models (GDM) in architectural design, focusing on sustainability. Utilizing the concept of “active human agency”, the research evaluates tools like DALL-E 2 (Bing) and ChatGPT (GPT-4) for creating environmentally responsive references in the early phases of the design process. Employing an explorative and qualitative methodology, the investigation includes architectural characteristics of climatic archetypes and concepts related to architecture-biology, applied to different environmental contexts. Initial findings reveal the AI potential in creating environmentally responsive references, with certain limitations in specific interactions and interpretations. The conclusion emphasizes these tools as collaborative aids in early design stages, and underscores the importance of "active human agency" for meaningful, responsible results, contributing to sustainability in early architectural design processes.
keywords	Artificial Intelligence, Generative Models, Architectural Design Process, Sustainability, Active Human Agency.
series	SIGraDi
email
last changed	2024/03/08 14:07

_id	ijac202321206
id	ijac202321206
authors	Pouliou, Panagiota; Horvath, Anca-Simona; Palamas, George
year	2023
title	Speculative hybrids: Investigating the generation of conceptual architectural forms through the use of 3D generative adversarial networks
source	International Journal of Architectural Computing 2023, Vol. 21 - no. 2, 315–336
summary	Abstract The process of architectural design aims at solving complex problems that have loosely defined formulations, no explicit basis for terminating the problem-solving activity, and where no ideal solution can be achieved. This means that design problems, as wicked problems, sit in a space between incompleteness and precision. Applying digital tools in general and artificial intelligence in particular to design problems will then mediate solution spaces between incompleteness and precision. In this paper, we present a study where we employed machine learning algorithms to generate conceptual architectural forms for site-specific regulations. We created an annotated dataset of single-family homes and used it to train a 3D Generative Adversarial Network that generated annotated point clouds complying with site constraints. Then, we presented the framework to 23 practitioners of architecture in an attempt to understand whether this framework could be a useful tool for early-stage design. We make a three-fold contribution: First, we share an annotated dataset of architecturally relevant 3D point clouds of single-family homes. Next, we present and share the code for a framework and the results from training the 3D generative neural network. Finally, we discuss machine learning and creative work, including how practitioners feel about the emergence of these tools as mediators between incompleteness and precision in architectural design
keywords	computational design, architecture, machine learning, design process, GNN, point cloud, generative design, artificial intelligence
series	journal
last changed	2024/04/17 14:30

_id	cdrf2023_102
id	cdrf2023_102
authors	Alberto Fernandez González, Nikoletta Karastathi
year	2023
title	Threading Cellular Architecture Geometries
doi	https://doi.org/https://doi.org/10.1007/978-981-99-8405-3_9
source	Proceedings of the 2023 DigitalFUTURES The 5st International Conference on Computational Design and Robotic Fabrication (CDRF 2023)
summary	In the massive computer architecture known as cellular automata (CA), finite-state machines, also known as finite-state automata, are arranged in a discontinuous network that permits local interactions between neighbors. As self-organizing artificial systems, such as neural networks and genetic algorithms, developed from vast systems formed with essential elements and just local interactions, CA is mainly related to artificial intelligence (AI) (a seed interacts with its own neighbors, which are usually just the cells closer to the seed as an activator). In order to produce architectural spaces of various sizes, this research develops digital experiments that analyze the interactions between environmental conditions as input, CA as generator/propagator, and geometrical emergent patterns from knitting and weaving processes as translators/mediators. This method functions as a bottom-up strategy in which information from the environment can influence the activation and deactivation of rules, theoretically fostering a reprogrammable structure that can evolve.
series	cdrf
email
last changed	2024/05/29 14:04

_id	ijac202321413
id	ijac202321413
authors	Ayoub, Mohammed
year	2023
title	Estimating the received solar irradiances by traditional vaulted roofs using optimized neural networks and transfer learning
source	International Journal of Architectural Computing 2023, Vol. 21 - no. 4, 795-820
summary	Traditional vaulted roof-forms have long been utilized in hot-desert climate for better indoor environmental quality. Unprecedently, this research investigates the possible contribution of machine learning to estimate the received solar irradiances by those roofs, based on simulation-derived training and testing datasets, where two algorithms were used to reduce their higher-dimensionality. Then, four models of ordinary least-squares and artificial neural networks were developed. Their ability to accurately estimate solar irradiances was confirmed, with R2 of 95.599–98.794% and RMSE of 12.437–23.909 Wh/m2. Transfer Learning was also applied to pass the stored knowledge of the best-performing model into another one for estimating the performance of new roof-forms. The results demonstrated that transferred models could provide better estimations with R2 of 87.416–97.889% and RMSE of 79.300–13.971 Wh/m2, compared to un-transferred models. Machine learning shall redefine the practice of building performance, providing architects with flexibility to rapidly make informed decisions during the early design stages.
keywords	Solar irradiance, prediction, simulation, machine learning, transfer learning
series	journal
last changed	2024/04/17 14:30

_id	ecaade2023_436
id	ecaade2023_436
authors	Bank Stigsen, Mathias, Moisi, Alexandra, Rasoulzadeh, Shervin, Schinegger, Kristina and Rutzinger, Stefan
year	2023
title	AI Diffusion as Design Vocabulary - Investigating the use of AI image generation in early architectural design and education
doi	https://doi.org/10.52842/conf.ecaade.2023.2.587
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 2, Graz, 20-22 September 2023, pp. 587–596
summary	This paper investigates the potential of Text-to-Image AI in assisting the ideation phase in architectural design and education. The study proposes a structured workflow and tests it with first-year architecture students. It aims to create a comprehensive design vocabulary by using AI-generated images as primary design references and incorporating them into a modelling workflow. The paper implements a process combining specific vocabulary extraction, image generation, 2D to 3D translation, and spatial composition within a six weeklong design course. The findings suggest that such a process can enhance the ideation phase by generating new and diverse design inspirations, improve spatial understanding through the exploration of various design elements, and provide students with a targeted visual vocabulary that helps define design intention and streamlines the modelling process.
keywords	Artificial Intelligence, Text-to-Image, Midjourney, Architectural design, Design ideation, 2D to 3D
series	eCAADe
email
last changed	2023/12/10 10:49

_id	acadia23_v3_195
id	acadia23_v3_195
authors	Gandia, Augusto; Iverson, Aileen
year	2023
title	Hybrid Making: Physical Explorations with Computational Matter
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 3: Proceedings of the 43rd Annual Conference for the Association for Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9891764-1-0]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 24-32.
summary	This publication introduces hybrid making as the subject of a workshop conducted at the ACADIA Conference 2023 (See Fig. 1). We contextualize hybrid making in today’s design digitalization marked by the opening of Artificial Intelligence (AI), wherein AI is seen as an accelerant in the ongoing digital evolution. In design-related practice and research, digital design is increasingly dominant (See Fig. 2); as shown in a quick survey of ACADIA 2022 wherein 10 out of 14 workshops focused on topics related to digitalization. Given this context, the subject of our workshop, hybrid making, highlights that which is excluded in purely digital processes, namely a richness of designing associated with the qualities of materials and fabrication (See Fig. 3). Hybrid making seeks to influence digital evolution with aspects of analogue processes such as the integration of constraints related to actual physical materials and their context. The task of hybrid making, therefore, is to introduce actual constraints into digital ones (See Fig. 4).
series	ACADIA
type	workshop
email
last changed	2024/04/17 14:00

_id	caadria2023_26
id	caadria2023_26
authors	Karsan, Zain
year	2023
title	Desk Mate: A Collaborative Drawing Platform
doi	https://doi.org/10.52842/conf.caadria.2023.2.521
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 521–530
summary	Machine Learning (ML) in architecture is an emerging field with myriad potentials to impact the design process. Despite its many possibilities, ML is typically employed when the design problem is sufficiently defined, and further, is only integrated within software environments. Desk Mate is collaborative drawing machine that can be used early in the design process by coupling tangible tools like pens and trace paper with ML driven feedback and generation. Embedding physical tools that are familiar and intuitive with digital intelligence offers designers new ways of engaging with ML algorithms interactively, potentially changing the way the architectural industry approaches design problems. Desk Mate chains together image retrieval methods from machine vision with generative ML models like variational autoencoders (VAE) and generative adversarial networks (GANS) to react to design sketches as they are drawn. This pipeline allows Desk Mate to iterate through designs with the designer. Thus, Desk Mate demonstrates an interactive platform that collocates designer and machine as creative agents, facilitating drawing with ML driven feedback, potentially accelerating design iteration in the early stages of ideation.
keywords	human machine interaction, machine learning and artificial intelligence, interactive machine learning, robotics and autonomous systems
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	ecaade2023_204
id	ecaade2023_204
authors	Lacroix, Igor, Güzelci, Orkan Zeynel and Sousa, José Pedro
year	2023
title	Evolutive Dataset for Social Housing Design Projects through Artificial Intelligence: From pixel to BIM through deep learning
doi	https://doi.org/10.52842/conf.ecaade.2023.2.629
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 2, Graz, 20-22 September 2023, pp. 629–638
summary	Establishing an evolutive dataset for architectural rationalization of social housing is technically achievable through artificial intelligence based on deep learning (DL). However, concerning the sensitive quality of social housing, the application of such technology needs to preserve the human factor and relate ethically to architectural design. A reference on this subject is historic Portuguese research from the 1960s and the 1970s. By then, pioneering research at the National Laboratory of Civil Engineering (LNEC), based in Lisbon, explored early computing methods to aid the design process by considering deontological concerns. The authors studied these works to refactor those goals and concerns of technological application and sociological interaction with current digital technologies. When digitizing their processes of creating architectural design instruments for social housing a problem emerged with parsing a dataset of floor plans and using it to generate building information models. Thus, a DL process was explored to achieve an evolutive dataset in the most automated way at the architectural level. The paper presents the implementation of a DL process that recognizes floor plans of social housing and consequently enables the development of an instrument for direct architectural rationalization.
keywords	Artificial Intelligence, Machine Learning, Deep Learning, BIM, Social Housing, Evolutive Dataset
series	eCAADe
email
last changed	2023/12/10 10:49

_id	caadria2023_292
id	caadria2023_292
authors	Langenhan, Christoph, Bielski, Jessica, Ziegler, Christoph, Eisenstadt, Viktor, Althoff, Klaus-Dieter and Dengel, Andreas
year	2023
title	Cross-Disciplinary Semantic Building Fingerprints ‚ AI Knowledge Graphs to Store Topological Building Information Derived From Semantic Building Models (BIM) to Apply Methods of Artificial Intelligence (AI)
doi	https://doi.org/10.52842/conf.caadria.2023.1.129
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 129–138
summary	The advancing digitalization in the building sector with the possibility to store and retrieve large amounts of data has the potential to digitally support planners with extensive design and construction information. Large amounts of semi-structured three-dimensional geometric data of buildings are usually available today, but the topological relationships are rarely explicitly described and thus not directly usable with computational methods of AI. To this end, we propose methods for indexing spatial configurations inspired by the similarity analysis of incomplete human fingerprints, since the early design stage of architectural design is characterized by incomplete information. For this, the topology of spatial configurations is extracted from Building Information Modelling (BIM) data and represented as graphs. In this paper, Semantic Building Fingerprints (SBFs) and Semantic Urban Fingerprints (SUFs), as well as use cases for AI methods are described.
keywords	Conceptual design, building information modelling, knowledge graph, artificial intelligence
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	sigradi2023_416
id	sigradi2023_416
authors	Machado Fagundes, Cristian Vinicius, Miotto Bruscato, Léia, Paiva Ponzio, Angelica and Chornobai, Sara Regiane
year	2023
title	Parametric environment for internalization and classification of models generated by the Shap-E tool
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 1689–1698
summary	Computing has been increasingly employed in design environments, primarily to perform calculations and logical decisions faster than humans could, enabling tasks that would be impossible or too time-consuming to execute manually. Various studies highlight the use of digital tools and technologies in diverse methods, such as parametric modeling and evolutionary algorithms, for exploring and optimizing alternatives in architecture, design, and engineering (Martino, 2015; Fagundes, 2019). Currently, there is a growing emergence of intelligent models that increasingly integrate computers into the design process. Demonstrating great potential for initial ideation, artificial intelligence (AI) models like Shap-E (Nichol et al., 2023) by OpenAI stand out. Although this model falls short of state-of-the-art sample quality, it is among the most efficient orders of magnitude for generating three-dimensional models through AI interfaces, offering practical balance for certain use cases. Thus, aiming to explore this gap, the presented study proposes an innovative design agency framework by employing Shap-E connected with parametric modeling in the design process. The generation tool has shown promising results; through generations of synthetic views conditioned by text captions, its final output is a mesh. However, due to the lack of topological information in models generated by Shap-E, we propose to fill this gap by transferring data to a parametric three-dimensional surface modeling environment. Consequently, this interaction's use aims to enable the transformation of the mesh into quantifiable surfaces, subject to collection and optimization of dimensional data of objects. Moreover, this work seeks to enable the creation of artificial databases through formal categorization of parameterized outputs using the K-means algorithm. For this purpose, the study methodologically orients itself in a four-step exploratory experimental process: (1) creation of models generated by Shap-E in a pressing manner; (2) use of parametric modeling to internalize models into the Grasshopper environment; (3) generation of optimized alternatives using the evolutionary algorithm (Biomorpher); (4) and classification of models using the K-means algorithm. Thus, the presented study proposes, through an environment of internalization and classification of models generated by the Shap-E tool, to contribute to the construction of a new design agency methodology in the decision-making process of design. So far, this research has resulted in the generation and classification of a diverse set of three-dimensional shapes. These shapes are grouped for potential applications in machine learning, in addition to providing insights for the refinement and detailed exploration of forms.
keywords	Shap-E, Parametric Design, Evolutionary Algorithm, Synthetic Database, Artificial Intelligence
series	SIGraDi
email
last changed	2024/03/08 14:09

_id	caadria2023_320
id	caadria2023_320
authors	Ponzio, Angelica Paiva, Chornobai, Sara Regiane, Fagundes, Cristian Vinicius Machado, Rodrigues, Ricardo Cesar and Cunha Hafez José, Gustavo
year	2023
title	Exploring Creative AI Thinking in the Design Process: The Design Intelligence Strategy
doi	https://doi.org/10.52842/conf.caadria.2023.2.049
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 49–58
summary	This article is part of an exploratory and experimental applied research that seeks to discuss different design strategies with significant potential to stimulate creativity and innovation in the architectural design process. Envisioning a future in which machines are not merely used as tools for creating data, but also to play a role that can enhance the design process itself, this research presents as its fundamental question the possibility of employing a combinatorial use of diffusion models, associated to parametric modeling as a means of predicting, developing, and ultimately optimizing environmentally conscious design proposals. Thus, our ultimate goal is to outline a novel methodology not only capable of stimulating creativity, but also enriching critical thinking and problem-solving skills for sustainable solutions in the early stages of the design process. The strategy here called Design Intelligence Strategy, uses referential design thinking concepts and processes to generate, analyze, compare and (re)systematize data. The object of study is a small house unit with limited constraints, to be implemented in a climatic location through formally adaptive characteristics. The results indicate that the AI generated images have potential to guide the process to climate-effective solutions, besides also being able to be implemented in academic studios.
keywords	Artificial Intelligence, Diffusion Models, Computational Design, Design Process, Creative Methodologies
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	architectural_intelligence2023_5
id	architectural_intelligence2023_5
authors	Qiaoming Deng, Xiaofeng Li, Yubo Liu & Kai Hu
year	2023
title	Exploration of three-dimensional spatial learning approach based on machine learning–taking Taihu stone as an example
doi	https://doi.org/https://doi.org/10.1007/s44223-023-00023-2
source	Architectural Intelligence Journal
summary	Under the influence of globalization, the transformation of traditional architectural space is vital to the growth of local architecture. As an important spatial element of traditional gardens, Taihu stone has the image qualities of being “thin, wrinkled, leaky and transparent” The “transparency” and “ leaky” of Taihu stone reflect the connectivity and irregularity of Taihu stone’s holes, which are consistent with the contemporary architectural design concepts of fluid space and transparency. Nonetheless, relatively few theoretical studies have been conducted on the spatial analysis and design transformation of Taihu stone. Using machine learning, we attempt to extract the three-dimensional spatial variation pattern of Taihu stone in this paper. This study extracts 3D spatial features for experiments using artificial neural networks (ANN) and generative adversarial networks (GAN). In order to extract 3D spatial variation patterns, the machine learning model learns the variation patterns between adjacent sections. The trained machine learning model is capable of generating a series of spatial sections with the spatial variation pattern of the Taihu stone. The purpose of the experimental results is to compare the performance of various machine learning models for 3D space learning in order to identify a model with superior performance. This paper also presents a novel concept for machine learning to master continuous 3D spatial features.
series	Architectural Intelligence
email
last changed	2025/01/09 15:00

_id	cdrf2023_3
id	cdrf2023_3
authors	Sandra Manninger, Matias del Campo
year	2023
title	Deep Mining Authorship
doi	https://doi.org/https://doi.org/10.1007/978-981-99-8405-3_1
source	Proceedings of the 2023 DigitalFUTURES The 5st International Conference on Computational Design and Robotic Fabrication (CDRF 2023)
summary	Considering the emerging field of architecture and artificial intelligence, it might be necessary to contemplate the remodeling of the concept of authorship entirely. The invention of authorship is a complex historical process that can be traced back to the emergence of print culture in Europe in the 15th century. Prior to this period, most literary and artistic works were created anonymously or attributed to collective or anonymous sources, such as folklore or religious traditions. However, with the rise of printing, texts became more easily reproducible and marketable, and there emerged a need for individual authors to take credit for their works. The notion of authorship was closely tied to the idea of originality and ownership, as authors sought to assert their exclusive rights to their works and to distinguish themselves from other writers. This was supported by the development of copyright law, which granted legal protection to authors and their works, and helped to establish a market for literary and artistic works. The idea of the author as a singular, autonomous figure gained further prominence in the 18th and 19th centuries, with the emergence of romanticism and the cult of the individual. This period saw the rise of the idea of the artist as a genius, whose works were the product of their own unique creativity and imagination. This idea was further reinforced by the rise of literary criticism, which focused on the interpretation and analysis of individual works and their authors. However, as Michel Foucault and other scholars have argued, the notion of authorship is not a universal or timeless concept, but rather a historically contingent and culturally specific one. Different societies ad cultures have different understandings of authorship, and these have shifted over time in response to changes in technology, culture, and social values. As it stands now, authorship in its traditional form can hardly be applied in a context where automated collaborations provide more than 50% of the generated material. This is true for multiple art fields. Visual Arts (Mario Klingemann, Sofia Crespo, Memo Atken, Ooouch, etc.), Music (Dadabots, YACHT, Holly Herndon), Literature, etc. Very soon this will also be true for Architecture. The consequence is also an entire rethinking of the concept of the sole genius. This notion, developed by German Romanticists in the early 19th century, is, in the current context of AI-assisted creativity, completely obsolete, as we are drawing from the genius of hundreds of thousands of artists and artworks in order to interrogate the latent space for unseen artistic opportunities. More akin to an archeological dig leading to the discovery of a next-generation jet fighter plane.
series	cdrf
email
last changed	2024/05/29 14:04

_id	ecaade2023_250
id	ecaade2023_250
authors	Sebestyen, Adam, Özdenizci, Ozan, Hirschberg, Urs and Legenstein, Robert
year	2023
title	Generating Conceptual Architectural 3D Geometries with Denoising Diffusion Models
doi	https://doi.org/10.52842/conf.ecaade.2023.2.451
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 2, Graz, 20-22 September 2023, pp. 451–460
summary	Generative deep learning diffusion models have been attracting mainstream attention in the field of 2D image generation. We propose a prototype which brings a diffusion network into the third dimension, with the purpose of generating geometries for conceptual design. We explore the possibilities of generating 3D datasets, using parametric design to overcome the problem of the current lack of available architectural 3D data suitable for training neural networks. Furthermore, we propose a data representation based on volumetric density grids which is applicable to train diffusion networks. Our early prototype demonstrates the viability of the approach and suggests future options to develop deep learning generative 3D tools for architectural design.
keywords	Artificial Intelligence, Generative Deep Learning, Neural Networks, Diffusion Models, Parametric Design, 3D Data Representations
series	eCAADe
email
last changed	2023/12/10 10:49

_id	cdrf2023_356
id	cdrf2023_356
authors	Shahin Vassigh, Biayna Bogosian, Eric Peterson
year	2023
title	Performance-Driven VR Learning for Robotics
doi	https://doi.org/https://doi.org/10.1007/978-981-99-8405-3_30
source	Proceedings of the 2023 DigitalFUTURES The 5st International Conference on Computational Design and Robotic Fabrication (CDRF 2023)
summary	The building industry is facing environmental, technological, and economic challenges, placing significant pressure on preparing the workforce for Industry 4.0 needs. The fields of Architecture, Engineering, and Construction (AEC) are being reshaped by robotics technologies which demand new skills and creating disruptive change to job markets. Addressing the learning needs of AEC students, professionals, and industry workers is critical to ensuring the competitiveness of the future workforce. In recent years advancements in Information Technology, Augmented Reality (AR), Virtual Reality (VR), and Artificial Intelligence (AI) have led to new research and theories on virtual learning environments. In the AEC fields researchers are beginning to rethink current robotics training to counteract costly and resource-intensive in-person learning. However, much of this work has been focused on simulation physics and has yet to adequately address how to engage AEC learners with different learning abilities, styles, and diverse backgrounds. This paper presents the advantages and difficulties associated with using new technologies to develop virtual reality (VR) learning games for robotics. It describes an ongoing project for creating performance driven curriculum. Drawing on the Constructivist Learning Theory, the affordances of Adaptive Learning Systems, and data collection methods from the VR game environment, the project provides a customized and performance-oriented approach to carrying out practical robotics tasks in real-world scenarios.
series	cdrf
email
last changed	2024/05/29 14:04

_id	ecaade2023_57
id	ecaade2023_57
authors	Taºdelen, Merve, Güleç Özer, Derya and Akçay Kavakoglu, Ayºegül
year	2023
title	The Quest of Spatial Presence by Puzzle-Solving Games in VR
doi	https://doi.org/10.52842/conf.ecaade.2023.2.833
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 2, Graz, 20-22 September 2023, pp. 833–842
summary	The experience of artificial objects in the virtual environment and the illusion of being there is a primary affordance of the virtual reality (VR) environment. The conviction of being located in a mediated environment is referred to as spatial presence. Although some studies investigate the relationship between VR and spatial intelligence, how users build a spatial presence in VR game environments remains ambiguous. Regarding that, this study tries to elaborate on the spatial presence experience construction and its characteristics in virtual reality (VR) puzzle-solving games by revealing the relationships between game mechanics and spatial presence notion. In this study, the presence-spatial performance relations are initially investigated based on previous works and analyzed in terms of spatial definition. Suppose the VR task performance depends on spatial abilities, people with higher spatial ability finish tasks faster, and their spatial presence score will be higher than people with lower spatial ability. A VR game called Golden Gate VR will be used as a case study to test and elaborate on the hypothesis above. This ongoing study has five steps: (1) Development of the game environment, (2) pre-psychometric assessment for visuo-spatial ability (Pre-Test), (3) Experience of the VR Game, (4) Evaluation of the experiences, (5) Re-development of the game environment. Experiences of the players’ will be evaluated in terms of Mental Imagery, Mental Rotation and Spatial Orientation regarding Spatial Presence Experience Scale (SPES). The first four steps will be elaborated on in this paper.
keywords	immersive virtual reality, spatial presence, spatial ability, puzzle-solving game
series	eCAADe
email
last changed	2023/12/10 10:49

_id	sigradi2023_499
id	sigradi2023_499
authors	Wedekin, Gabriela, Rodrigues, Ricardo, Montenegro, Clara, Gonçalves, Laura and Duarte, Rovenir
year	2023
title	AI Style Recognition Technological Artifact on Smart Campus UEL: Design of the Evaluation Photo-taking-impairment Effect on Architectural Heritage
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 227–238
summary	This article aims to understand the critical points in the experiments of the photo-taking-impairment effect, normally researched in the act of photographing, with the use of an artifact in recognition of styles and elements in AI. For the development of the experiment, a heritage route was simulated in the UEL Smart Campus, such as Living Lab, with three wooden houses from the 1940s, and an AI application for cell phones developed in Android Studio, with models trained in Google's Teachable Machine cloud. The experiment was carried out in a pre-test with three different designs. A strong attentional disengagement was pointed out with the concern to apply more volitional experiment designs and with less conceptual codification.
keywords	Digital Heritage, photo-taking-impairment, artificial intelligence
series	SIGraDi
email
last changed	2024/03/08 14:06

_id	ecaade2023_183
id	ecaade2023_183
authors	Werker, Ines and Beneich, Kinza
year	2023
title	Open AI in the Design Process - To what extent can text-to-image software support future architects in the early design process?
doi	https://doi.org/10.52842/conf.ecaade.2023.2.577
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 2, Graz, 20-22 September 2023, pp. 577–586
summary	The laborious creation of digital images could soon be a thing of the past. Text-to-image software generates images from text descriptions through artificial intelligence, the AI can map entirely new concepts and create images in a variety of artistic styles. Existing text-to-image software is already publicly available, but does it live up to its promise, and can it be more useful to architects in their search for inspiration than previous software that uses visual search to display images? In this paper, we address the opportunities and problems of text-to-image software. To answer our question, we use a key study, this is divided into two user groups. The subjects of group A are to use DALL·E 2 to search for inspiration for a design whose task is: Design a museum with a boat dock. The same design task is also given to the subjects of group B, with the difference that they are to use Pinterest to find inspiration.We will then contrast the results of these surveys. We will document the differences of the user experience and the output of DALL·E 2 to Pinterest as well as about advantages and disadvantages of DALL·E 2 and possible future developments, and application areas of text-to-image software.
keywords	text-to-image, DALL·E 2, Pinterest, early design process, picture generating, inspirational searching, AI
series	eCAADe
email
last changed	2023/12/10 10:49

_id	ascaad2023_075
id	ascaad2023_075
authors	Aljhadali, Abdulrahman; Megahed, Yasser; Gwilliam, Julie
year	2023
title	Artificial Intelligence (AI) and Machine Learning (ML) in Practice: A Comprehensive Investigation into the Utilization of Generative Artificial Intelligence (AI) and Machine Learning (ML) in Architectural Practice
source	C+++: Computation, Culture, and Context – Proceedings of the 11th International Conference of the Arab Society for Computation in Architecture, Art and Design (ASCAAD), University of Petra, Amman, Jordan [Hybrid Conference] 7-9 November 2023, pp. 324-343.
summary	This study offers a comprehensive investigation into the utilization of artificial intelligence (AI) and machine learning (ML) technologies within architectural practices. Since the introduction of computer-aided design (CAD), technology has had a significant impact on the way architects conduct their work. This study explores the potential of AI/ML in actual architectural workflows, with a particular emphasis on the capacity of deep neural networks to assist in the design process.The outcome will help to develop a clearer picture of the opportunities and barriers associated with AI for architects; they will also inform the prioritization of focus for future development of this technology in architectural practice, as well as identifying the specific tasks and project phases in which ML could play a role. This research reviewed literature to explore various approaches for applying AI/ML technologies within the field of architecture. Also , complemented by a number of interviews to investigate the ways in which participants are currently using AI/ML in their work, framing the current feedback and the future potential of AI/ML technologies in architecture. The data collection methods adopted involved semi-structured one-on-one interviews with professionals from multi-regional architecture firms and AI developers. The architects interviewed exhibited diverse ways of benefiting from AI/ML technology, with varying approaches and some common trends. The findings demonstrate that AI has played a pivotal role in expediting the design process and enhancing visualization within the field. However, it has also raised concerns, particularly in the realm of privacy.
series	ASCAAD
email
last changed	2024/02/13 14:40

_id	architectural_intelligence2023_3
id	architectural_intelligence2023_3
authors	Areti Markopoulou & Oana Taut
year	2023
title	Urban mining. Scoping resources for circular construction
doi	https://doi.org/https://doi.org/10.1007/s44223-023-00021-4
source	Architectural Intelligence Journal
summary	Operating with an abundance mindset – rather than from a place of “scarcity” – is a new paradigm, relevant to the practices of design and construction, which expands the definition of “resources” as well as where resources, both raw and non-raw materials, can be found and “mined”. Within three scales of design and planning, the current research – developed at the Institute for Advanced Architecture of Catalonia (IAAC) – examines the applications of computational technologies and life cycle assessment with the goal of setting up protocols for enhancing processes of urban mining and material reuse in future circular construction. In the material scale (i), selected projects experiment with up-cycled waste for the creation of new engineered composites for construction. In the building scale (ii), robotic technologies and computer vision are used to scan and sort the materials from existing buildings or demolition sites. Finally, in the urban scale (iii), google images, satellite data and ML are used to index the existing material stock in building façades in cities. The research calls for agents involved in design, planning and construction to shift their focus to the anthroposphere as a source of, rather than just a destination for, processed goods. The concept of “urban mining” is revisited to manage the material stock in urban systems and the use of anthropogenic resources in new production cycles. Through a multi-scalar approach, the outcome challenges the foundation of our material practices, presenting the potential to disrupt linear patterns of design and making in the built environment.
series	Architectural Intelligence
email
last changed	2025/01/09 15:00

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