  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.
