Bridge condition assessment is important to maintain the quality of highway roads for public transport. Bridge deterioration with time is inevitable due to aging material, environmental wear and in some cases, inadequate maintenance. Non-destructive evaluation (NDE) methods are preferred for condition assessment for bridges, concrete buildings, and other civil structures. Some examples of NDE methods are ground penetrating radar (GPR), acoustic emission, and electrical resistivity (ER). NDE methods provide the ability to inspect a structure without causing any damage to the structure in the process. In addition, NDE methods typically cost less than other methods, since they do not require inspection sites to be evacuated prior to inspection, which greatly reduces the cost of safety related issues during the inspection process. In this paper, an autonomous robotic system equipped with three different NDE sensors is presented. The system employs GPR, ER, and a camera for data collection. The system is capable of performing real-time, cost-effective bridge deck inspection, and is comprised of a mechanical robot design and machine learning and pattern recognition methods for automated steel rebar picking to provide realtime condition maps of the corrosive deck environments.
@cite_30: This paper presents the results of the application of GPR to detect the constitution of a concrete pre-stressed runway pavement and the location of the rebars, with the aim of evaluation the bearing capacity of the runway.
Research in automated object detection using GPR is a recent development. Prior to this research, there were two primary methods for detecting objects in GPR scan images: manual detection using the human eye, and using commercial software @cite_30 . These two methods have respective issues. Manually detecting hundreds of rebar in an image is time consuming and requires training to correctly identify rebar, and using commercial software requires purchasing an expensive software license. The goal of research in automated rebar detection is to offer a third option that is less expensive and requires less time from the user.