The Architectural Computational Design and Construction Cluster (ACDCC) within the University of Michigan’s Taubman College of Architecture and Urban Planning consists of faculty and Ph.D. students, whose research domain and design work leverage computational design and digital fabrication processes. Technological and material innovations are directed toward impactful building practices, including resilient infrastructures and environmentally and socially sustainable built environment. ACDCC teams collaborate with interdisciplinary partners and industries to address societal needs at the material, building, and infrastructural scales.
Robotic Construction involves the integration of automation and robotics with architecture and construction to transform the way we design and build towards more sustainable solutions, addressing safety, cost, and labor efficiencies. This includes developing new and existing robotic processes for onsite and offsite construction, as well as design solutions that leverage their potential across every stage of construction, from co-design to site scanning, and component manufacturing to assembly and disassembly processes.
Human-machine collaboration includes two main research topics: developing novel methods coupling human intelligence with computational processes to enhance design exploration and enable integrative design, analysis, and fabrication methods, and investigating human-robot collaborative processes to facilitate safer construction practices. Human-machine collaboration reduces physically demanding or repetitive activities while enabling more inclusive construction practices by expanding human-machine interaction to be more intuitive, requiring less specialized training, and supporting a more equitable workforce.
Design exploration is enhanced by computational approaches to generate an array of viable solutions. In the search for an appropriate design with diverse parameters, alternatives are sought that offer better performance – both qualitatively and quantitatively. Computational techniques generate and supply a pool of options which can then be further explored to uncover the best design solution.
Material Systems Innovation coordinates specific sets of attributes and behaviors of materials with precise design processes, fabrication methods, and system performance. Leading with material selection has enormous influence on the entire design and manufacturing ecosystem. Investigations are targeted across a range of activities: from material experimentation that leverages unique behaviors towards specific design solutions, to custom tooling for manufacturing. Research includes innovation in glass, concrete, plastic, timber, textile, earth, bio-based materials, and composites.
Extended Reality (XR) encompasses three subcategories: augmented reality (AR), virtual reality (VR), and mixed reality (MR). AR consists of graphic content being superimposed on the real world, VR embodies the user in a fully immersive environment, and MR is a combination of the two. XR in architecture and design has afforded new ways of working and learning. Through a medium that fosters co-creation, one-to-one embodied experiences, and a heightened spatial awareness, this technology allows for new opportunities in teaching, learning, and research as the physical and digital realities become ever more intertwined.
Additive Manufacturing (AM) involves developing innovative methods and materials that are appropriate for construction-scaled applications.. AM leverages geometric freedom in the deposition process to enable designs that are previously cost prohibitive and difficult to realize. Integration with automated processes reduces labor and time during construction and customization affords opportunities for sustainable solutions through reduction in material use in relation to optimized performance. Research includes cementitious, bio-based, and plastic printing, with focus on low-carbon building applications.
Machine Learning (ML) is a subset of Artificial Intelligence (AI) research that interrogates large datasets for particular features that can aid in various aspects of architectural production. ML ranges in terms of applications and disciplinary approaches, from artistic representations and aesthetic considerations, to serving as a practical tool for optimization and prediction. As sophisticated AI functionality expands, ML will be integrated furthermore into design and construction processes.
Low-carbon construction reconsiders existing methods of building, by focusing on design and innovative material systems that limit the amount of energy consumed during the material production, transportation, and construction phases of our built environment. Beyond the embodied emissions related to construction, designs aim to reduce operational energy demand for the use phase of the building. Driven by the urgency to address the many challenges of climate change, low-carbon construction imperatives are at the core of the ACDCC’s research and teaching activities.
Deeply embedded in the exchange of matter and energy within our surroundings, Environmentally Responsive Systems are acutely tuned to regulate the shifting conditions of the environment and atmosphere. These systems are active in sensing or prompting changes to the built and natural settings, altering in form or behavior through material, mechanical, or electrical actuation. Responsive systems have the ability to cater to human comfort and health, and automatically optimize energy efficiencies in response to use patterns.
Complex assemblages such as buildings require the integration and interoperation of multiple interdependent systems in part to whole relations. Physical and material systems in resilient and low carbon buildings operate cooperatively and often with informational feedback over time, generating complex behavior through interaction of simple parts. Integrative Systems are explored for both building performance and construction processes, with interrelations implicated across these realms.
Optimization is part of the design process where solutions are weighted in context to interrelated variables. Optimization may straddle across different aspects of design, from topology (form) optimization in relation to structural efficiencies, to spatial organization for maximizing usability. Building performance targets can also be optimized for energy efficiencies, and manufacturing processes can be tuned to reduce material use and processing time. Optimization often includes several objectives which may be conflicting and requires a balanced approach to understand tradeoffs. Heuristic methods such as Genetic Algorithms are employed to analyze complex multi-objective parameters to arrive at an optimal solution. Conflicting parameters can also be explored using Pareto Front analysis to determine the best combination of objectives.