Nondestructive evaluation of select bridges in Iowa, USA using high-speed and ground-coupled robotic NDE platforms

Abstract

This paper intends to provide a detailed assessment of the condition of two select bridge decks in the state of Iowa, USA. The field-data collection was obtained by a novel data collection system called “Infratek insight” which consists of the High Definition (HD) and High Speed (HS) platforms. The data collection took place in two phases. Phase one (HS) used air coupled NDE sensors at traffic speed while phase two (HD) utilized ground coupled NDE sensors using robotic deployment, autonomous navigation and driving systems to eliminate location variability. The data acquisition during both phases was subject to real-time data quality control by a custom data acquisition software that was developed by Infratek. Manual chain dragging was also performed at select areas to inspect the bridge deck condition and to validate data obtained by the insight system. A suite of NDE technologies were utilized on the High-Speed (HS) and High Definition (HD) platforms including Impact Echo (IE), Ultrasonic Surface Waves (USW), Electrical Resistivity (ER), Ground-Coupled GPR, Air-Coupled GPR, Surface Laser Profilers, 360-Degree Visual Imaging, Infrared thermography (IR), High-Definition Line Scan Imaging and Automated Crack Mapping with Artificial Intelligence Engines, LiDAR and Automated High-Speed Sounding (Chain Drag). ER and GPR are used to assess the corrosion potential throughout the bridge deck. GPR is also used to determine cover depth. IE, Chain Drag and IR are the primary delamination evaluation tools, and USW enables evaluation of concrete quality and degradation. Results indicated a high degree of correlation between the HD and HS platforms and also between individual sensors. The results and maps produced by the HS and HD platforms were utilized in quantifying repairs and providing the asset owner with data-driven recommendations on future rehabilitation actions based on best practices and owner specific criteria.