Self-Powered Wireless Sensor Network for Automated Corrosion Prediction of Steel Reinforcement

Joint Authors

Xia, Ye
Su, Dan
Yuan, Robert

Source

Journal of Sensors

Issue

Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2018-01-08

Country of Publication

Egypt

No. of Pages

10

Main Subjects

Civil Engineering

Abstract EN

Corrosion is one of the key issues that affect the service life and hinders wide application of steel reinforcement.

Moreover, corrosion is a long-term process and not visible for embedded reinforcement.

Thus, this research aims at developing a self-powered smart sensor system with integrated innovative prediction module for forecasting corrosion process of embedded steel reinforcement.

Vibration-based energy harvester is used to harvest energy for continuous corrosion data collection.

Spatial interpolation module was developed to interpolate corrosion data at unmonitored locations.

Dynamic prediction module is used to predict the long-term corrosion based on collected data.

Utilizing this new sensor network, the corrosion process can be automated predicted and appropriate mitigation actions will be recommended accordingly.

American Psychological Association (APA)

Su, Dan& Xia, Ye& Yuan, Robert. 2018. Self-Powered Wireless Sensor Network for Automated Corrosion Prediction of Steel Reinforcement. Journal of Sensors،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1201432

Modern Language Association (MLA)

Su, Dan…[et al.]. Self-Powered Wireless Sensor Network for Automated Corrosion Prediction of Steel Reinforcement. Journal of Sensors No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1201432

American Medical Association (AMA)

Su, Dan& Xia, Ye& Yuan, Robert. Self-Powered Wireless Sensor Network for Automated Corrosion Prediction of Steel Reinforcement. Journal of Sensors. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1201432

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1201432