Projects

Environmental disasters, such as storms, wild fires, hurricanes, and oil spills, displace and disrupt the lives of millions of Americans every year. For example, in 2018, California wildfires caused the evacuation of 50,000 people; in 2005, Hurricane Katrina destroyed an estimated 300,000 homes leaving more than 1 million people homeless; and more recently in 2017, Hurricane Maria damaged more than 60,000 homes and left 450,000 customers without power (Fig. 1 [1]). Such disasters have two major impacts: • Disasters have a profound psychological impact.

Drones have been, for long, under the scrutiny of building researchers. Drones can reach places where it is difficult or unsafe for humans to reach, such as building facades, under the bridge deck, etc. However, most research is on non-contact inspection, i.e., they collect data through cameras and other sensors. This project evaluates the potential of using drones or Unmanned Aerial Systems (UAS) for contact-based inspections of buildings that require precise localization and path planning.

Our team has been working on forecasting facilities' life-cycle cost (LCC) by implementing machine learning on historical data. We have proposed a comprehensive and generalizable framework for developing facility LCC analysis machine learning models. This framework specifies the data requirements, methods, and expected results in each step of the model development process.

The objective of this project is to develop a systems interoperability that supports a network-based data exchange for collaboration in smart and connected design and construction process. We contribute to developing industry standards, system frameworks, and new automated solutions for seamless network-based data exchange in the process of design and construction.