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Continuing Education: Artificial Intelligence

#artificialintelligence

Depending on the people you talk to, architects approach artificial intelligence (AI) with a range of anticipation, skepticism, or dread. Some say algorithms will handle drudge work and free designers to focus on the more creative aspects of their jobs. Others assert that AI won't live up to its hype--at least not in the near future--and will make only marginal improvements in the profession. And a third group worries that software that learns on its own will put a lot of architects out of work. Science fiction writers have been imagining robots that think like human beings for more than 100 years.


AI, Architecture, and Generative Design

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As technologies such as 3D printing move the discipline forward in remaking the built environment, AI and generative design are having an impact on architecture from a planning and design perspective, where the profession is largely digital and computational. We spoke with Lilli Smith, Senior Product Manager AEC Generative Design at Autodesk, a practitioner in the field of architecture for more than 20 years -- the last 18 of which have been making the software that architects use to design their creations. In architecture, art, and other creative fields, generative design is a methodology that automates the creation of design options that may balance a variety of competing goals. The latest wave of generative design is driven by artificial intelligence. "This is often the case in architectural design problems," Smith says.


DeepCloud. The Application of a Data-driven, Generative Model in Design

arXiv.org Machine Learning

Generative systems have a significant potential to synthesize innovative design alternatives. Still, most of the common systems that have been adopted in design require the designer to explicitly define the specifications of the procedures and in some cases the design space. In contrast, a generative system could potentially learn both aspects through processing a database of existing solutions without the supervision of the designer. To explore this possibility, we review recent advancements of generative models in machine learning and current applications of learning techniques in design. Then, we describe the development of a data-driven generative system titled DeepCloud. It combines an autoencoder architecture for point clouds with a web-based interface and analog input devices to provide an intuitive experience for data-driven generation of design alternatives. We delineate the implementation of two prototypes of DeepCloud, their contributions, and potentials for generative design.


A Knowledge-Based Approach to Problem Formulation for Product Model-Based Multidisciplinary Design Optimization in AEC

AAAI Conferences

The cost-effectiveness and accuracy of a multidisciplinary design optimization (MDO) process is highly dependent on designers’ ability to flexibly formulate the optimization problem for specific challenges. Designers need to rapidly modify how object parameters are assigned to groupings of objects in the product model. Our research has developed a Reference-Based Optimization Method (RBOM) to enable this type of flexible problem formulation. However, the responsibility still falls on the designer to manage the problem formulation and MDO process, which can lead to inefficient and costly design decisions. By means of artificial intelligence, in particular knowledge-based systems, these potential barriers to MDO adoption in the Architecture, Engineering, and Construction (AEC) industry could be mitigated, resulting in more efficient design processes and, ultimately, energy-efficient built environments.


What Can AI Do For You?

#artificialintelligence

Designing a building, developing a constructible model from a design or working out how to go about constructing a complicated model are all tasks that already contain some degree of automation. So when researchers and others in the architectural, engineering and construction world start talking about bringing artificial intelligence into the mix, many say it's already here. But recent advances in generative design, safety analysis and 5D scheduling are only the first hints of what sophisticated algorithms and deep-learning AI can bring to construction. Getting smart algorithms and other AI-derived technologies onto the project team may not be as far-fetched an idea as it once was. But rather than having a computer that takes over the existing job duties of an architect or engineer, those professions may soon have some form of AI-based assistant offering options and providing clarifications all along the way.