Development of non-contact passive wireless sensors for detection of corrosion in reinforced concrete bridge decks
MetadataShow full item record
Corrosion of embedded reinforcement is the leading form of deterioration affecting the integrity of reinforced and prestressed concrete bridge members around the world. If undetected, corrosion can limit the service life of the bridge and lead to expensive repairs. The research team at the University of Texas at Austin has developed a new class of passive wireless corrosion sensors. The noncontact (NC) sensor platform provides an economical and nondestructive means for detecting corrosion initiation within concrete. The sensor is powered through the inductive coupling to an external mobile reader that can be handheld or mounted on a vehicle. It is envisioned that the four-dollar sensor will be embedded in concrete during construction and interrogated sporadically over the service life of the structure. The sensor output can be used to detect corrosion initiation within concrete and is expected to enhance the quality information collected during qualitative routine bridge inspections. The NC sensor prototype consists of a resonant circuit that is inductively coupled to a sacrificial transducer. Corrosion of the sacrificial element alters the measured sensor response and is used to detect corrosion within concrete. Electrochemical evaluations were conducted to ensure that the sacrificial element exhibited identical response as the reinforcement steel. In addition, the results of extensive experimental parametric studies were used in conjunction with circuit and electromagnetic finite element models to optimize the NC sensor design. Long-term exposure tests were used to evaluate the reliability of the passive noncontact sensors. Sensors were embedded in reinforced concrete specimens and successfully detected the onset of corrosion in the adjacent reinforcement. Unlike the traditional corrosion evaluation methods, such as half-cell potentials, the sensors output was insensitive to environmental variations.