Data analysis and visualization for the bridge deck inspection and evaluation robotic system

Abstract

Background: Bridge deck inspection is essential task to monitor the health of the bridges. Condition monitoring and timely implementation of maintenance and rehabilitation procedures are needed to reduce future costs associated with bridge management. A number of Nondestructive Evaluation (NDE) technologies are currently used in bridge deck inspection and evaluation, including impact-echo (IE), ground penetrating radar (GPR), electrical resistivity (ER), ultrasonic surface waves (USW) testing, and visual inspection. However, current NDE data collection is manually conducted and thus faces with several problems such as prone to human errors, safety risks due to open traffic, and high cost process. Methods: This paper reports the automated data collection and analysis for bridge decks based on our novel robotic system which can autonomously and accurately navigate on the bridge. The developed robotic system can lessen the cost and time of the bridge deck data collection and risks of human inspections. The advanced software is developed to allow the robot to collect visual images and conduct NDE measurements. The image stitching algorithm to build a whole bridge deck image from individual images is presented in detail. The ER, IE and USW data collected by the robot are analyzed to generate the corrosion, delamination and concrete elastic modulus maps of the deck, respectively. These condition maps provide detail information of the bridge deck quality. Conclusions:The automated bridge deck data collection and analysis is developed. The image stitching algorithm allowed to generate a very high resolution image of the whole bridge deck, and the bridge viewer software allows to calibrate the stitched image to the bridge coordinate. The corrosion, delamination and elastic modulus maps were built based on ER, IE and USW data collected by the robot to provide easy evaluation and condition monitoring of bridge decks.

Development of an autonomous bridge deck inspection robotic system

“…For more information of the image stitching algorithm please refer to Refs. , . The crack detection is performed in the stitched image of Haymarket bridge over an area of a size of 5.5 m × 1.45 m. The top sub‐figures are the zoom‐in images at several crack locations for clear presentation and demonstration.…”

“…The images simultaneously collected by these two cameras have about 30% overlap area that is used for image stitching. The automated image stitching algorithm was reported in the previous work . Each camera is equipped with a flash to remove shadow at night time and mitigate shadow at day time (Fig.…”

STAR: Visual Computing in Materials Science

“…Mayer et al [MLK*08] target to characterize and classify indications of defects in concrete buildings and even go a step further: They target the prevention of faults by estimating cracks as well as the crack growth of concrete materials. Other types of damage are found in corrosion and delamination which are topic of research of La et al [LGKN15] and Yashiro et al [YTT07]. Dimensional Measurements , in contrast to the previous tasks, this task centers around the traceable dimensioning and tolerancing of measurement features such as straightness, evenness, cylindricity, etc., which is essential for industrial quality control.…”

Nondestructive evaluation sensor fusion with autonomous robotic system for civil infrastructure inspection

“…Such a robotic system could reduce the required training for inspection workers, reducing safety requirements, and reducing the cost of civil infrastructure inspection. There have been some research efforts related to the development of automated bridge inspection systems such as Robotic Bridge Inspection Tool (RABIT) (Gucunski et al., ; La et al., , ; Lim, La, Shan, & Sheng, ; Lim, La, & Sheng, ), NDE data processing (Dinh et al., ; Gucunski et al., ; Gucunski, Kee, La, Basily, & Maher, ; La et al., ), automated inspection with drones (Phung, Quach, Dinh, & Ha, ; Scherer, Singh, Chamberlain, & Elgersma, ; Yan et al., ), and mobile sensor network‐based inspection (Zhu, Guo, Cho, Wang, & Lee, ). A recent survey of automated civil infrastructure inspection can be found in Li et al.…”