To develop smart city infrastructure health condition monitoring, detection, and diagnosis with the use of emerging technologies (e.g. smart phones, 2D imaging, 3D laser, LiDAR, UAV, GPS/GIS, crowdsourcing, voice recognition, etc.) with artificial intelligence, machine learning, computer vision, pattern recognition, signal processing, multi-source/scale/frequency/resolution data fusion, and spatial-temporal analyses for innovatively providing smart location-based services.
1) To identify roadway asset deficiencies, such as potholes, cracking, and dangerous roadway spots/sections that require safety improvement, etc.
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
- Big data analysis, including multi-source/scale/frequency/resolution data fusion and spatial-temporal data analysis for infrastructure health condition monitoring, detection, and diagnosis;
- Study of fundamental characteristics of different sensing data, including smart phone accelerometer data, 3D laser, mobile LiDAR, GPS/IMU, etc.;
- Innovative integration of hardware, algorithms, and procedures to develop innovative solutions (e.g. mobile applications/tools);
- Real-world and real-time infrastructure behavior study of its health condition and deterioration behavior in support of smart and resilient cities development.
Methods and Technologies
Academic Majors of Interest
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