Spanning 1.7 miles and weighing 887,000 tons, San Francisco’s Golden Gate Bridge requires consistent structural health monitoring. (Photo credit: Jessica Hunt).
Dr. Yang Wang is an assistant professor in CEE who specializes in structural health monitoring and damage detection, optimal decentralized structural control, smart materials and structures, and wireless sensor networks. His work concentrates on structural sensors used to monitor and record the various structural components of buildings and bridges in order to track movement, functionality, and safety.
In one of Dr. Wang’s current projects, he and a team of researchers are investigating an ultra low-cost solution for wireless, batteryless sensors that monitor stress concentration and crack formation on metallic (e.g. steel or aluminum) structures. The sensors operate on radio frequency identification (RFID) principles, and the strain sensor is not only wireless but does not require battery power. In an RFID system, the reader beams electromagnetic energy to the tag, which receives the energy and reflects an electromagnetic signal back to the reader. When the RFID tag is under strain/deformation, the tag antenna shape changes and causes its electromagnetic resonance frequency to shift. This shift in resonance frequency can be measured by the reader, and then used to derive the strain experienced by the RFID tag.
Next, a passive (batteryless) RFID tag is designed and manufactured for wireless strain sensing. The wireless strain sensor (i.e. RFID tag) contains only a piece of copper patch antenna, and a small, low-cost RFID chip. No other electronic components are required at the wireless sensor side. The resonance frequency extracted by an RFID wireless reader shows strong linearity with respect to small strain increments. The slope of the linear regression shows that 1με strain causes -761 Hz shift in the tag’s resonance frequency. The performance of the wireless sensor has also been successfully tested for large strain levels over 20,000 με.
The preliminary results of this research have been very promising. In fact, the team is using the initial data to make the following modifications:
(1) Evaluate the sensor performance for detecting crack formation. Since high sensitivity to small strain has been observed in current prototypes, it is expected that crack formation will cause large resonance frequency shift that is relatively easy to capture by the reader.
(2) Reduce the sensor dimension from 2.5 in. by 2.5 in. to below 0.5 in. by 0.5 in. The objective will be achieved by increasing the operation frequency from 900 MHz to over 5 GHz. The size factor of the sensor is proportional to the wavelength of the electromagnetic signal, and thus, inversely proportional to the operation frequency.
(3) Investigate the performance of simultaneous measurements from multiple passive wireless sensors. Using frequency division techniques, explore the distinguishing responses from various sensors.
(4) Increase the wireless interrogation range from currently achieved 2 ft to over 10 ft. Approaches include further optimizing the antenna shape and exploiting simple photovoltaic or vibration energy harvesting techniques.
Due to the simplicity and promising performance of this research, the proposed technology holds great potential for the future, allowing mass production of low-cost, wireless strain/crack sensors used to monitor, analyze, and evaluate the performance of metallic structures.
Mobile Sensor Network for Structural Health Monitoring: Video
From left to right, Dr. Yang Wang displays a prototype strain sensor; tensile testing results showing the resonance frequency shift of the RFID tag versus strain; power transmission and backscattering in a passive RFID tag-reader system.