Probabilistic Life Cycle Analysis as a Sustainability-Focused Design Tool for Industrialized Construction

Graduate Researcher: Tess Hegerty
Faculty Advisor: Michael Lepech

The Architecture, Engineering, and Construction (AEC) industry is undergoing transformation in two key areas: (1) increased focus on performance-based sustainability metrics to quantify and reduce carbon emissions produced by the built environment and (2) integration of industrialized construction (IC) approaches such as process automation. This research is the first application of probabilistic life cycle analysis (LCA) as a sustainability-focused design tool for an early-stage IC concept case study (CarbonCondo). To produce probabilistic LCA results, the Pedigree Matrix was used to quantify uncertainty in LCA inputs to a Monte Carlo simulation (MCS). The SIPMath Excel® plug-in is used to perform nearly instantaneous, low-cost MCS automatically in Excel®. This is the first use of Stochastic Information Packets (SIPs) as a medium for capturing and using uncertainty information in the LCA of a building. This probabilistic LCA approach improves sustainability metrics in a highly uncertain design space, relative to the initial IC design concept. Research findings show that the probabilistic LCA model in-formed the CarbonCondo design team’s decision making by providing rapid, targeted, high-level feedback on decisions such as material choice. Preliminary results indicate a 12% reduction in predicted mean lifecycle carbon dioxide emissions achieved through the IC approach relative to an equivalent conventional construction approach. Future work applying the probabilistic LCA approach to additional IC case studies can improve the reliability of the sustainability value proposition of IC. Additionally, future development and integration of the sustainable target value (STV) framework can provide a more rigorous goal for reduction targets.