DREAMS
Data REvolution for Asset Management & Sustainability
Sustainable & Healthy Schools
Using the pandemic of COVID-19 to set the epidemiological context, we conducted scenario analyses to examine the relationship between reduced infection risk and increased energy cost incurred from improved ventilation in 111,485 public and private schools in U.S. Employing the epidemiology modeling, infection risk prediction, energy simulation, and cost estimation, a series of important insights have been derived to reduce infection risks and save energy costs with limited budgets.
Building Energy - Health - Comfort Nexus
We performed the trade-off analysis among health (indoor pathogen transmission and infection risks), building energy consumption, and human thermal comfort via integrated modeling and simulation, and developed AI-based building management and occupant interaction solutions based on real-time simulation and internet of things sensing.
Indoor Microbiome and Public Health
Based on indoor microbiome type, abundance, and distribution obtained from high-throughput sequencing, we developed coupled digital twin with building environment modeling, occupant sensing, and microbiome dynamics to predict and visualize the pathogen transmission within buildings and the associate infection risks to inform public health interventions at fine resolutions and in real time.
Facility Management
We developed digital twin and AI-based methods to inspect building exteriors and interiors in different scenarios.
Building Exterior Inspection
Building Interior Inspection
Building Damage Assessment
Building Object & Asset Detection and Tracking
Revolutionize Airspace & Infrastructure Systems
To Revolutionize Airspace and Infrastructure Systems for Evolution with Advanced Air Mobility (RAISE-AAM), we aim to understand the emergent dynamics from the system of systems interdependencies in AAM planning, optimize the design and operation of AAM infrastructure systems and networks with the modeled interdependencies, and exploit the simulations in a digital twin paradigm for AI-based control, coordination, and cooperation in airspace management.
Mapping the City Subsurface
We developed a deep learning based method to fully automate the processing of ground penetrating radar data to reconstruct the city subsurface, and integrate the subsurface map with surface landmarks to produce an accurate and complete map for digital twin based infrastructure management.
