Engineering and Design of Sustainable Built Systems
The tremendous growth of green building design and construction, sparked by the U.S. Green Building Council’s Leadership in Energy and Environmental Design (LEED) program, has brought sustainability to the forefront of many design and construction projects.
At Stanford, research on design and construction practices for a more sustainable built environment centers on the creation and widespread application of quantitative tools for measuring the comprehensive economic, social and environmental impacts of building and infrastructure systems.
The design and construction of sustainable building systems begins with modeling the entire life cycle of a building to measure the impacts from material mining and fabrication, construction, building use and placement in a landfill or recycling at end of life. The development of new datasets, modeling techniques and software tools for accurately measuring a full cadre of sustainability metrics is a major research effort. These metrics can range from global warming potential (CO2 equivalents), acidification potential (H mol equivalents), ecotoxicity (2,4-D equivalents), human health criteria pollutants (PM2.5 equivalents), energy use (MJ of nonrenewable and renewable energy), public health impacts, land use and social equity to long-term economic costs.
Building on these analysis and assessment tools, further research focuses on the use of quantitative sustainability metrics to guide design and operation of built systems. These efforts infuse sustainability-focused decision-making throughout the design process, from material design and selection to structural design to building-, neighborhood- and system-level planning, accounting for the fact that design choices at each scale affect overall sustainability performance. Our research includes the creation of design tools that incorporate numerous models for advanced material performance, durability and corrosion phenomena, building construction and operation, structural performance and system behavior with nonlinear optimization models to achieve maximum sustainability performance.