Beam Lab

Existing buildings and historic structures play key roles in the communities they are a part of. Not only are they an important part of cultural legacy, but they also are crucial in surmounting engineering challenges like the design of sustainable infrastructure, space shortages, increased costs of construction, and the lowering life cycle costs. By reusing or recycling existing and historic structures, communities can reduce their carbon costs, decrease the amount of solid waste they are contributing to landfills, and save on new construction materials. Before existing structures can be preserved or renovated however, an assessment of the existing building must be completed. This process must comprise documenting where damages are, understanding how existing damages originated and assessing the current capacity of a structure. Our research is in hybrid analytics which lies at the intersection of civil engineering, computer science, and historic preservation. Hybrid analytics, a nascent field, is the combination of physics-based modeling and data-driven modeling for the end goal of making real-time predictions and monitoring in the context of Digital Twin a reality. This new field leverages the decipherability and clear-box nature of physics-based modeling, with accuracy and pattern recognition techniques of data-driven machine learning algorithms. More specifically, our research focuses on the following aspects for preservation and adaptive reuse of existing and historic structures as a sustainable infrastructure solution