231108_GDM_xArch_preprints

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[27] Critical Social Computing for Digital Design Muhammad Talha Muftee 1,2 1 Ghent University, Ghent 9000, Belgium 2 COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan Advancements in computational technology have ushered in a new era of architectural design, characterized by the integration of digital tools. However, this rapid pace of technological innovation has also widened the digital divide, making advanced design solutions less accessible, particularly in underprivileged regions and marginalized communities. Meanwhile, data and algorithmic processes profoundly impact the post-digital built environment. With increasingly complex problems of the built environment, such communities are at risk unless this gap is addressed. This paper underscores the significance of regional computing systems, referred to as social computers, and their emergence in addressing these disparities. To allow architects and experts to act as facilitators of resilience and transformation, critical social computing for design is proposed as a strategy to develop meaningful collaborative practices through digital tools. Keywords: Digital Design, Social Computing, Participatory Design
[28] Explorations of Conceptual Design Generation based on the Deep Learning Model - Discussing the Application of AI Generator to the Preliminary Architectural Design process Yuxin Bao1 1 and Changying Xiang 1 1 The Hong Kong University of Science and Technology, Hong Kong The rapid advancement and heightened awareness of Artificial Intelligence (AI) have stimulated a substantial surge in the growth of deep learning models. This has led to an increasingly prevalent debate surrounding the possibility of AI replacing human architects. Nevertheless, it is foreseeable that architects will explore an AI-aided design process at this stage. Several deep learning models, including NovelAi, DELL-E·2, Midjourney, and Stable Diffusion, have emerged, making it easier to generate images efficiently without the need for multidisciplinary knowledge related to algorithms and programming.During the conceptual design phase of an architectural project, it is crucial for architects to present several massing proposals in various styles within a limited timeframe. It means a huge amount of modelling and drawing works. This paper focuses on Stable Diffusion, Midjourney, and DALL-E 2, as primary examples to discuss the approaches used in the preliminary design process as a smart assistant. These AI platforms are expected to optimize the conceptual design work by reducing the time of transforming hand-drawn sketches into rendering photos and enhancing the visualization of massing diagrams. This article analyzes the impact of AI work-related activities carried out by architects and architectural students based on a designed survey containing various images generated by AI programs. The survey aims to investigate the performance of several popular AI programs and architects' perspectives towards AI. The findings indicate that AI has the potential to assist human architects to some extent with satisfactory performance. The effective application of AI generators can significantly optimize the design process, allowing architects to explore more creative and aesthetic aspects. Keywords: Machine learning, AI-aided, Architecture, Efficiency, work flow.
Page 2: GENERAL INFORMATION WI-FI ACCESS Ne
Page 5 and 6: KEYNOTE SPEAKERS
Page 7 and 8: The AI plot twist AI has been advan
Page 9 and 10: Creativity in Design Creativity in
Page 11 and 12: AI in Architecture The marriage of
Page 13 and 14: Alien Intelligence: AI and Creativi
Page 15 and 16: NOVEMBER 11, 2023 COMPUTATIONAL DES
Page 17 and 18: NOVEMBER 11, 2023 DIGITAL EXPERIENC
Page 19 and 20: NOVEMBER 12, 2023 AI AND ENVIRONMEN
Page 21 and 22: ABSTRACTS (IN NUMERIC ORDER)
Page 23 and 24: [7] Cellular Automata as Design Too
Page 25 and 26: [10] A Language Prompt Model for Ar
Page 27 and 28: [20] Exploring an evolving architec
Page 29: [25] Digital hybridities: Theorisin
Page 33 and 34: [33] The Taste of Textures Artifici
Page 35 and 36: [44] Optimization and Design of Bui
Page 37 and 38: [47] Digital Creativity in urban in
Page 39 and 40: [50] UAV-based geometry data acquis
Page 41 and 42: [57] Structuring Gamified participa
Page 43 and 44: [65] Analysis of Differences in Str
Page 45 and 46: [72] Meta-morphing architectural do
Page 47 and 48: [77] Challenges and opportunities i
Page 49 and 50: [79] Strategies of Interconnecting
Page 51 and 52: [81] AI Machine Learning in Creativ
Page 53 and 54: WORKSHOPS
Page 55 and 56: WORKSHOP 2 Crafting Large-Scale Ins

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