Exploring Creative AI Thinking in the Design Process: The Design Intelligence Strategy (original) (raw)
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Designing Tomorrow: AI and the Future of Architectural Design Process
Forum A+P, 2023
This essay explores the transformative role of Artificial Intelligence (AI) in the field of architecture, focusing on the impact on design innovation, production processes, as well as the ethical implications arising from inherent biases within these technologies. As AI becomes increasingly integrated into architectural practices, it offers the potential to revolutionize the discipline by enhancing efficiency, creativity, and sustainability. This investigation delves into the history and evolution of AI in architecture, tracing their journey from early computational design experiments to their current applications in generative design and robotic construction, which exemplify the shift towards more innovative and sustainable architectural practices. Furthermore, the essay highlights the involvement of the INNEN research team in integrating AI into academic and research activities within and besides the curriculum. It delves into the ways in which AI technologies are reshaping the boundaries of architectural design and construction, emphasizing on practical applications of AI in architectural design in education and professional work. This analysis uncovers the profound possibilities of AI in formulating groundbreaking design approaches and construction methods, underscoring research's role in propelling architectural thinking and practice forward through the use of technology.
Design Communication Association , 2022
In Architecture, Engineering, and Construction (AEC), Artificial Intelligence (AI) is often used to optimize and automate procedural, repetitive, and deterministic tasks. These tasks do not have a strong affinity with design exercises intentionally characterized by transcending optimization, where qualitative and quantitative aspects coexist and frequently involve both asking the right questions and answering them. In the famous Eames Design Diagram of 1969, Charles Eames postulated that design processes could only be successful when it identifies the overlapping needs of the designer, the client, and "the concerns of society as a whole." While the interplay between the first two stakeholders are well-researched and documented, the third has remained an abstract concept that relies on the designer's interpretation of the gestalt. With social media and big data, we can now aggregate and analyze datasets that provide insight into the concerns and opinions of the broader public at scales previously unfeasible. Accordingly, this paper will consider an iterative human-machine collaboration within a design studio to test how AI algorithms (automatic object identification, Fourier transform, RGB analysis, and Word3Vec) and data analysis (automatic crawlers and data preprocessing) can fit within an architectural design exercise that investigates the relationship of architecture and the natural landscape, through a series of small-scale projects. The design workflow is organized in four steps depending on the data modality analyzed. a), Identification of user needs and context (images and text data from social networks). b) Storytelling and branding (text data from digital books), c) Curating and modeling space (point cloud data from terrestrial laser scanning). d) Materialization of the project (via 3D printing and game engines). We draw parallels between the proposed design workflow and a traditional one that followed a similar series of design operations: a) Site analysis, b) Design brief, c) Design response, and d) Design representation. The results show that the inclusion of AI and big data analysis can augment various creative abilities allowing designers to focus on questions that require human creativity rather than machine productivity. Recognizing that certain phases of the design process require iterative and recursive thinking, the application of AI can be used to augment and expose potentials and possibilities within phases that require parallel and lateral associative thinking akin to brainstorming. These methods are applied to the early phases of schematic design and territories in which the broad use of AI techniques as generative, interpretive tools to convey conceptual and narrative ideas that are in the early stages of exploration. The impact can be seen particularly in the first and third steps of the design process outlined above because they take particular care to the "concerns of society as a whole" through the collaboration with artificial and human intelligence. The design framework presented in this paper brought together tools already well explored in design practices (point clouds, 3D printing, and game engines) with newly explored tools (AI algorithms) that resulted in design solutions articulated through such a joint effort with artificial and human intelligence.
The Role of Artificial Intelligence in Enhancing Design Innovation and Sustainability
Smart Design Policies, 2024
Artificial Intelligence (AI) is reshaping the design landscape, bridging computational efficiency with human creativity to revolutionize fields such as architecture, graphic design, and product development. This paper explores AI’s transformative impact, focusing on its ability to enhance productivity, foster innovation, and personalize user experiences. Objectives include identifying the benefits of AI-driven tools, analyzing their applications across domains such as architecture, graphic design, and product development, and evaluating ethical concerns related to AI in design. The research adopts a qualitative approach, to examine AI’s role as a creative collaborator and its implications for design methodologies. Results reveal that AI optimizes design iterations, accelerates prototyping, and democratizes access to high-quality resources, making design processes more inclusive and efficient. Findings also highlight ethical concerns, such as bias in AI systems and intellectual property disputes, which require balanced and responsible integration strategies. AI serves as a creative collaborator, enhancing ideation and prototyping processes. Despite its benefits, AI integration raises ethical concerns, including data bias, intellectual property disputes, and potential job displacement. These challenges necessitate equitable frameworks to ensure inclusive and responsible AI use. The future of AI in design promises even greater innovation with emerging technologies like augmented reality and the metaverse, fostering collaborative human-machine interactions. By embracing AI, designers can expand creative boundaries, producing solutions that are not only functional and visually compelling but also socially and environmentally sustainable. This study underscores the need for balanced integration, ensuring AI complements human ingenuity while redefining creativity in the evolving design landscape.
Civil Engineering and Architecture, 2024
The integration of artificial intelligence (AI) in architecture is transforming the design process, making it faster, more efficient, and more sustainable. AI serves as a starting point for conversation, allowing architects to interact with data scientists and engineers. The study recommends merging AI into architecture education to improve designers' comprehension of design-related concepts. The future role of architects in AI design and its application raises questions about how AI design will reflect their creativity and architectural touch. AI is revolutionizing the design process by integrating Building Information Modelling (BIM) techniques and enabling real-time analysis and optimization. It also revolutionizes architectural design education by generating initial project forms and improving interaction with architects. AI applications can revolutionize the way students learn and create, allowing them to explore innovative designs they may not have considered otherwise. As AI applications continue to advance, they have the potential to evolve and provide even more sophisticated design solutions. AI-powered design tools have the potential to revolutionize the way architects approach and create architectural projects. The study explores the potential of artificial intelligence in architectural design. It highlights the integration of AI through generative design algorithms, virtual reality tools, and machine learning algorithms. These tools help architects explore design options, optimize projects for energy efficiency and structural integrity, and analyze large amounts of data for informed decisions. AI-powered design tools can understand aesthetics, architecture schools, and project requirements, enhancing the initial design stage. Integrating Building Information Modeling (BIM) techniques can enable real-time analysis and optimization of designs, saving time and resources. AI applications can also revolutionize architectural design education by generating initial project forms and allowing students to develop innovative designs.
Towards Creative Systems in Architectural Design
The 54th International Conference of the Architectural Science Association, 2020
This position paper describes a pathway and methodology towards creative systems in architectural design. Drawing from creativity research and strategic design methods, an agile approach to exploration of deep learning technology in the context of architectural optimisation was developed. The investigation proposed and defined the nature of a framework, which explored ways of integrating architectural shape design with machine intelligence. Furthermore, the paper elaborates the implications and potential for impact of deep learning techniques on advancing human-computer-interaction for architectural optimisation. However, the described framework might be used as a design scheme for an active tool to drive design processes and support decision-making in early stages of architectural design. The components of the framework defined interfaces and critical points of investigation for application of the presented methodology in creative practice. In this way the research contributes to the theoretical and methodological development of creative systems research. At its heart, this generative design study involved the definition of a clear research trajectory, challenges and opportunities of supporting creative practice by means of design systems. Finally, the potential of machine intelligence to generate creative work with and without human guidance or performance criteria was examined.
MSA Engineering Journal, 2023
The integration of Artificial Intelligence (AI) into architectural design has revolutionized the building industry. AI offers a wide range of algorithmic approaches that can be used to explore abstract conceptual designs and generate an unlimited number of design ideas based on mathematically defined parameters. This paper provides an exploratory study that critically reviews the evolution of AI in architectural design. The study highlights the potentials, limitations, and future vision of this technology within the context of architectural design. AI has transitioned from a tool for functional optimization to an unprecedented resource for design inspiration based on machine intelligence. However, the authors emphasize the importance of a balanced approach that ensures AI-generated designs are human-centric, environmentally responsible, and culturally sensitive. The study concludes that AI has the potential to inspire and enhance architectural design but must be used ethically and responsibly to avoid negative impacts on human creativity and design ethics.
Use of artificial intelligence in the field of sustainable architecture: Current knowledge
ALFA – Architecture Papers of the Faculty of Architecture and Design of the Slovak University of Technology , 2021
SUMMARY There is a trend that artificial intelligence (AI) is a direction to take in various scientific disciplines. The idea of AI originated before the 1960s, and with the development of computer technology, the capacities of AI have multiplied. It currently affects many areas of everyday life where we may not even realize that we are already using AI. We are certain that AI is a challenge in the field of architecture and the entire construction industry, where sustainability is one of the current issues. A notable technological shift in the field of building design is BIM. The participants in the process of design are effectively informed about the current state of the project, but the BIM model should be used for further actions, such as utilising information as interactive tool in the construction, operation or renovation phase. The objective of the study is to acquire knowledge about AI usability in the optimization of sustainable design BIM processes in architecture. The aim of the study is knowledge of the applicability of AI in the conceptual solution for reducing the carbon footprint in the BIM model of the building of Faculty of Architecture and Design STU. An assumption for formulating the hypothesis is to use AI to predict the pattern of users’ behaviour. Due to need in the older buildings, where massive refurbishment is not possible or appropriate because of the historical or cultural value. So, it is nearly impossible to meet current requirements for energy-efficient buildings. The article provides a brief overview of AI usability. The base knowledge is presented with terminology in the field of information technology, the processes of artificial intelligence and their applicability to the field of architecture. The context of historical development refers to the considerations of Stanislas Chaillou, who evaluates the connection between the work of an architect and AI as a logical step in technical development. As examples and sources of information studies of application AI in architecture and building industry were used. The study by Stanislas Chaillou is about architecture and AI, where it focuses on the ways and processes of using GAN (Generative Adversarial Network) in floor plan design. The optimization of design processes in BIM is presented by a study that tries to use BIM and artificial intelligence in the design process of buildings. It creates a project called BIMBOT, which generates solutions based on defined priorities for a specific project. The most discussed part of the article is the work of Ekaterina Petrova. Aim of the study is: Integrating knowledge discovery and semantic data modelling for support of evidence-based design decisions. Suggesting a solution to systematization of building design using the BIM-based design, she draws attention to BIM building models, which contain a lot of information that will eventually remain unused. Petrova develops the architecture of a comprehensive software “consultant” system using AI and the cooperation of experts in the field of information technology and the building industry. The result of the study will be a “consultant” which collects information from real buildings throughout their life cycle into a robust database. AI methods are used for re-evaluation and sorting of information to gain new knowledge for sustainable building design processes. The main goal of Petrova research is to transfer the acquired knowledge from real constructions back to the design process, in order to create a connection between relevant factual and data-based knowledge within the initial phase of the design of new buildings. In her work, Petrova also dealt with obtaining specific data from case studies. Based on the statistics, she processed a database of collected information. The data was taken from measurements of two case studies of erected buildings, where she had to find a system categorizing information. The issue of sustainable building designing with AI in the study is proposed from the perspective of the functionality of information technology rather than from the architect’s point of view. The study addresses the challenges of data collection, processing of information in databases and its compatibility. However, this is the point of utilising knowledge, because it clarifies the idea of correct data and outputs when working with a database which consists of many BIM models. A dramatic science fiction for which AI is often taken, may be sometimes true. The article outlines challenges and directions of AI development. Some of them are still unrealistic in the current state of scientific knowledge. However, working with AI itself is advancing in various scientific disciplines, such as medicine and disease diagnosis. AI is currently able to find connections in the data based on statistics of the occurrence of a certain phenomenon. AI is also able to recognize that phenomena. To decide based on abstraction, whether to put phenomena in context, AI is still in its infancy and needs the help of a human expert. AI and humans acquire skills through experience. Some knowledge is very difficult to transform so that AI can “understand” it. If we use AI as a chatbot, it is able to learn concepts based on experience, but does not fully understand the meaning. These issues of working with AI are also transferred to the use in the construction industry or architecture. The article outlines some of these possible problems. Few of them have already been named in studies as specific problems for that case. Other problems are of a general nature or related to the overall development of work with AI. Finally, the study evaluates the potential of working with AI in architecture and ideas about future research. Keywords: architecture, sustainability, artificial intelligence, AI, building information model, BIM, generative adversarial network, GAN
A Critical Review of Computational Creativity in Built Environment Design
Buildings
Computational creativity in built environment (BE) design has been a subject of research interest in the discipline. This paper presents a critical review of various ways computational creativity has been and can be defined and approached in BE design. The paper examines a comprehensive body of contemporary literature on the topics of creativity, computational creativity, and their assessment to identify levels of computational creativity. The paper then proceeds to a further review of the implications of these levels specifically in BE design. The paper identifies four areas in BE design where computational creativity is relevant. In two areas—synthesis (generation) and analysis—there is considerable literature on lower levels of computational creativity. However, in two other areas—interfacing and communication—even the definition of computational creativity is not as defined and clear for the discipline, and most works only consider the role of computers as a supporting tool or m...
Integrating energy consciousness in the design process
Automation in Construction, 1999
The design process for an intelligent, energy conscious building which was built, along with the design tools that were applied, is presented. The building, situated in the hot-humid climate of Rehovot, Israel, houses the laboratories and offices of the Weizmann Institute's Environmental Science and Energy Research Department. Alternative bio-climatic design Ž . options were proposed and evaluated throughout the detailed design stage. A building energy performance index BEPI was established for each alternative. This index reflects the total amount of energy consumption for heating, cooling, ventilating and lighting used per square meter of floor area. Thermal modeling for the different design alternatives were carried out by means of an hourly dynamic simulation model. The model solves simultaneously the heat transfer equations through all exterior walls, taking into account the thermal mass of each external wall as well as internal partitions. The model was extended to include hourly calculations of daylighting and geometrical shading coefficient of the windows, as well as automated and 'smart' control strategies. q 0926-5805r99r$ -see front matter q 1999 Elsevier Science B.V. All rights reserved.
A course on creative computing in architecture and design Design Ecologies Laboratory 2014-2015
Algorithmic Tectonics is a course on creative computing in architecture and design. By learning to create computational design artifacts (such as experimental software, responsive objects and robotic fabrication applications) participants explore computation as a territory for speculative, critical and poetic thinking about design (rather than merely as an instrument of production or representation). Departing from the conventional approach of programming courses based on lectures and problem-sets, the course introduces each topic in a project-oriented fashion through design questions. Organized in three modules, design, visualize and make, the class prompts students to develop an appreciation for current developments in computational design, and to create their own projects with an incremental degree of sophistication: from simple interactive computer graphics to architectural robotics applications. This book reports on the course as taught for the first time at Penn State in the Spring of 2015. It is not a conclusive work but rather snapshots of an ongoing process. Together, the assignments, projects, and their descriptions, reflect a fledgling imaginary of design that continues to evolve around software and other technological infrastruc-tures. In combination with the online code repositories and blogs that accompanied the course, this book may be useful for others confronting questions about pedagogies of computing in design.