Smart City Infrastructure

2016 ~ Present | United States Department of Transportation (USDOT), National Academies of Science (NAS) National Cooperative Highway Research Program (NCHRP), Georgia Department of Transportation (GDOT), National Science Foundation (NSF)

Goals

To develop smart city infrastructure health and safety condition monitoring, assessment, and diagnosis with the use of emerging sensor technologies (e.g. smart phones, 2D imaging, 3D laser scanning, 3D LiDAR, 3D printing, UAV, GPS, Telematics, CV/AV, etc.) with machine learning, computer vision, artificial intelligence, pattern recognition, signal processing, multi-source/scale/frequency/resolution data fusion, crowdsourcing, data analytics, and GIS spatial-temporal analyses for innovatively providing smart location-based services and mapping and data analytics, and forecasting & optimization.

1)     Dynamic mapping and forecasting of transportation infrastructure health and safety condition for smart maintenance.  To identify roadway asset deficiencies, such as potholes, cracking, and dangerous roadway spots/sections that require safety improvement, etc.  Predictive and proactive safety analysis and safety improvement.

2)     To cost-effectively and sustainably preserve and manage the infrastructure assets (pavements, signs, etc.) at the right time, right location, and right method by analysis of multiple sensors at multiple scales.

 

Issues Involved or Addressed

  1. Big data analytics, including multi-source/scale/frequency/resolution data fusion and GIS spatial-temporal data analysis for infrastructure health and safety condition monitoring, assessment, and diagnosis;
  2. Study of fundamental characteristics of different sensing data, including smart phone accelerometer data, 3D laser scanning , 3D printing, mobile LiDAR, GPS/Inertia Measurement Unit (IMU), etc.;
  3. Innovative integration of sensor hardware, algorithms, and procedures to develop innovative solutions (e.g. mobile applications/tools);
  4. Real-world and real-time infrastructure behavior study of its health & safety condition and deterioration behavior in support of smart and resilient cities development.

 

Methods and Technologies

  • Machine learning/deep learning
  • Big data Analytics
  • GIS spatial-temporal data analysis
  • Smart Phone / Tablet / PC
  • 3D Scanning/3D printing/3D LidarCrowdsourcing
  • Cloud Computing
  • Parallel Computing
  • Image/Signal Processing
  • Computer Vision
  • Non-destructive Testing

Academic Majors of Interest

  • General Management
  • Computational Media
  • Computer Science
  • OMSCS synchronous
  • Civil Engineering
  • Computer Engineering
  • Electrical Engineering
  • Environmental Engineering
  • Industrial Engineering
  • Mechanical Engineering

Preferred Interests and Preparation

ECE, CS, ME - Background/interest in mobile computing, Android/iOS development, cloud computing, database development (smart services development). Background/interest image processing, computer vision, pattern recognition, GPS/GIS (intelligence service development).

CEE, ISyE - Transportation asset management, pavement management, roadway safety

To ensure deep enough engagement, OMSCS students are asked to participate for 2 to 3 credit hours.

Meeting Schedule & Location

Time 
9:30-10:20
Meeting Location 
TBD
Meeting Day 
Wednesday

Team Advisors

Dr. James Tsai
  • Civil and Environmental Engineering
Dr. Tony Yezzi
  • Electrical and Computer Engineering

Partner(s) and Sponsor(s)

United States Department of Transportation (USDOT), National Academies of Science (NAS) National Cooperative Highway Research Program (NCHRP), Georgia Department of Transportation (GDOT), National Science Foundation (NSF)