State of the Art Health Monitoring Systems of Bridges

2019 ◽  
Author(s):  
Waheed Gul
Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3730 ◽  
Author(s):  
Pengcheng Jiao ◽  
King-James I. Egbe ◽  
Yiwei Xie ◽  
Ali Matin Nazar ◽  
Amir H. Alavi

Recently, there has been a growing interest in deploying smart materials as sensing components of structural health monitoring systems. In this arena, piezoelectric materials offer great promise for researchers to rapidly expand their many potential applications. The main goal of this study is to review the state-of-the-art piezoelectric-based sensing techniques that are currently used in the structural health monitoring area. These techniques range from piezoelectric electromechanical impedance and ultrasonic Lamb wave methods to a class of cutting-edge self-powered sensing systems. We present the principle of the piezoelectric effect and the underlying mechanisms used by the piezoelectric sensing methods to detect the structural response. Furthermore, the pros and cons of the current methodologies are discussed. In the end, we envision a role of the piezoelectric-based techniques in developing the next-generation self-monitoring and self-powering health monitoring systems.


2017 ◽  
Vol 64 (3) ◽  
pp. 621-628 ◽  
Author(s):  
Haik Kalantarian ◽  
Costas Sideris ◽  
Bobak Mortazavi ◽  
Nabil Alshurafa ◽  
Majid Sarrafzadeh

Geosciences ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 406
Author(s):  
Kamiński ◽  
Makowska

The article discusses the issue of hydrostatic leveling. Its application is presented in structural health monitoring systems in order to determine vertical displacements of controlled points. Moreover, the article includes a complete computation scheme that utilizes the estimation from observation differences, allowing the elimination of the influence of individual sensors’ systematic errors. The authors suggest two concepts of processing the measurement results depending on the sensors’ connection method. Additionally, the second concept is extended by the elements allowing the prediction of the displacements by means of Kalman filtering.


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