Architectural artificial intelligence: exploring and developing strategies, tools, and pedagogies toward the integration of deep learning in the architectural profession

The growing incessance for data collection is a trend born from the basic promise of data: “save everything you can, and someday you’ll be able to figure out some use for it all” (Schneier 2016, p. 40). However, this has manifested as a plague of information overload, where “it would simply be impossible for humans to deal with all of this data” (Davenport 2014, p. 151). Especially within the field of architecture, where designers are tasked with leveraging all available sources of information to compose an informed solution. Too often, “the average designer scans whatever information [they] happen on, […] and introduces this randomly selected information into forms otherwise dreamt up in the artist’s studio of mind” (Alexander 1964, p. 4). As data accumulates— less so the “oil”, and more the “exhaust of the information age” (Schneier 2016, p. 20)—we are rapidly approaching a point where even the programmers enlisted to automate are inadequate. Yet, as the size of data warehouses increases, so too does the available computational power and the invention of clever algorithms to negotiate it. Deep learning is an exemplar. A subset of artificial intelligence, deep learning is a collection of algorithms inspired by the brain, capable of automated self-improvement, or “learning”, through observations of large quantities of data. In recent years, the rise in computational power and the access to these immense databases have fostered the proliferation of deep learning to almost all fields of endeavour. The application of deep learning in architecture not only has the potential to resolve the issue of rising complexity, but introduce a plethora of new tools at the architect’s disposal, such as computer vision, natural language processing, and recommendation systems. Already, we are starting to see its impact on the field of architecture. Which raises the following questions: what is the current state of deep learning adoption in architecture, how can one better facilitate its integration, and what are the implications for doing so? This research aims to answer those questions through an exploration of strategies, tools, and pedagogies for the integration of deep learning in the architectural profession

Integrated Simulation-based Framework for Parametric Open Space Design with Focus on Sustainable Mobility and Climate Resilience

Recent advances in the application of computational design show great potential in the holistic assessment of design scenarios. To tackle the challenges of climate change and urbanisation, we need intelligent planning methods to design sustainable urban development and resilient open spaces. Therefore, this paper presents an integrated simulation-based framework for parametric urban design with focus on sustainable mobility and climate resilience. Precisely, aspects from mobility, water management and microclimate are used for the evaluation of open space planning. The result is the framework including interfaces and the exemplary application to real-world scenarios in Aspern at Nelson Mandela Square

Machine learning for optimized buildings morphosis

International audienceThe world is rapidly urbanizing, with an increasing number of new building constructions. This involves increasing the world's energy consumption and its associated greenhouse gas emissions. Computational tools are playing an increasing impact on the architectural design process. Recently, Machine learning (ML) has been applied to building design and has evinced its potential to improve building performance. This paper tries to review the use of ML for the building morphosis. We then forecast the use of machine learning for building optimized morphosis in the early design stage particularly for ensuring summer shading and winter solar access between neighbors

CoBuilt : towards a novel methodology for workflow capture and analysis of carpentry tasks for human-robot collaboration

Advanced manufacturing and robotic fabrication for the housing construction industry is mainly focused on the use of industrial robots in the pre-fabrication stage. Yet to be fully developed is the use on-site of collaborative robots, able to work cooperatively with humans in a range of construction trades. Our study focuses on the trade of carpentry in small-to-medium size enterprises in the Australian construction industry, seeking to understand and identify opportunities in the current workflows of carpenters for the role of collaborative robots. Prior to presenting solutions for this problem, we first developed a novel methodology for the capture and analysis of the body movements of carpenters, resulting in a suite of visual resources to aid us in thinking through where, what, and how a collaborative robot could participate in the carpentry task. We report on the challenges involved, and outline how the results of applying this methodology will inform the next stage of our research

CoBuilt 4.0 : investigating the potential of collaborative robotics for subject matter experts

Human-robot interactions can offer alternatives and new pathways for construction industries, industrial growth and skilled labour, particularly in a context of industry 4.0. This research investigates the potential of collaborative robots (CoBots) for the construction industry and subject matter experts; by surveying industry requirements and assessments of CoBot acceptance; by investing processes and sequences of work protocols for standard architecture robots; and by exploring motion capture and tracking systems for a collaborative framework between human and robot co-workers. The research investigates CoBots as a labour and collaborative resource for construction processes that require precision, adaptability and variability. Thus, this paper reports on a joint industry, government and academic research investigation in an Australian construction context. In section 1, we introduce background data to architecture robotics in the context of construction industries and reports on three sections. Section 2 reports on current industry applications and survey results from industry and trade feedback for the adoption of robots specifically to task complexity, perceived safety, and risk awareness. Section 3, as a result of research conducted in Section 2, introduces a pilot study for carpentry task sequences with capture of computable actions. Section 4 provides a discussion of results and preliminary findings. Section 5 concludes with an outlook on how the capture of computable actions provide the foundation to future research for capturing motion and machine learning