7 research outputs found
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: âsave
everything you can, and someday youâll be able to figure out some use for it allâ (Schneier 2016,
p. 40). However, this has manifested as a plague of information overload, where âit would simply
be impossible for humans to deal with all of this dataâ (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, âthe average designer scans whatever
information [they] happen on, [âŠ] and introduces this randomly selected information into forms
otherwise dreamt up in the artistâs studio of mindâ (Alexander 1964, p. 4). As data accumulatesâ
less so the âoilâ, and more the âexhaust of the information ageâ (Schneier 2016, p. 20)âwe 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 âlearningâ, 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 architectâs 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