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International Journal of Distributed Sensor Networks
Volume 2012 (2012), Article ID 358286, 2 pages

Sensing Methodologies and Sensor Networks for Health Monitoring of Civil Infrastructures

1Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian, Liaoning 116023, China
2Department of Mechanical Engineering, University of Houston, Houston, TX 77004, USA
3Department of Ocean Engineering, Pukyong National University, Busan 608-737, Republic of Korea

Received 24 December 2012; Accepted 24 December 2012

Copyright © 2012 Hong-Nan Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Large and complex civil infrastructures are especially susceptible to random vibrations, whether they are from high ground accelerations, strong wind forces, or abnormal loads such as explosions. Thus, it is imperative that the robust and continuous health monitoring system needs to be designed and implemented so as to meet the demanding goals of increasing structural safety and reliability, while reducing its operating and maintenance costs. The efficiency of a structural health monitoring system (SHMS) relies on the sensitivity of the acquired data to structural changes that may be obtained by an extended sensor network on structures as shown in “Methodology developments in sensor placement for health monitoring of civil infrastructures” and [1]. Recent advances in sensing technologies have enabled the use of large numbers of sensors for health monitoring of civil infrastructures. The sensors utilized in an SHMS are required to monitor not only the structural status including the stress, displacement, and acceleration but also those influential environmental parameters, such as the wind speed, temperature, and quality of its foundation. Since a large sensor network needs to be involved in an SHMS, how to deploy it in a complicated, spatially extended structure so that the data acquired from those locations will result in the best identification of structural characteristics will be a challenging task. On the other hand, a high cost of data acquisition systems (sensors and their supporting instruments) and accessibility limitation constrain in many cases the wide distribution of a large sensor networks on a structure. Especially, many civil structures have to be tested under operational conditions, in which the sensors are not easily amenable to be removed or changed [2, 3].

It is, therefore, of increasing interests to seek rational placement and design a method of sensor networks that contain as much information as possible about the health state of a structure. This special issue aims to aggregate the latest research efforts contributing to theoretical, methodological, and technological advances in the integration of various aspects of SHMS applications within a broad range of disciplines. Some research topics towards this target include intelligent and efficient algorithms in the design of sensor networks; evaluation criteria for different sensor networks placement methods; inherent relationship between different sensor networks placement methods; uncertainty, sensitivity, reliability, and redundancy in sensor networks; application of large scale sensors for civil infrastructures and others related.

A total of 26 peer-reviewed papers have been presented in this special issue. These include identifying the optimal number and location of sensors, as well as the robustness, which aims at maintaining the stability of the sensor network when some sensors malfunction, and so forth. Without a doubt, the papers reflect the state-of-the-art researches and developments of this subject. The Guest Editor would like to express his sincere appreciation to all the authors who have contributed their work to this special issue.


The Guest Editor would also like to express his whole-hearted thanks to the reviewers around the world for their contributions in shaping this issue. This work was jointly supported by the Science Fund for Creative Research Groups of the NSFC (Grant no. 51121005), the National Natural Science Foundation of China (Grant no. 51178083, 51222806), and the Program for New Century Excellent Talents in University (Grant no. NCET-10-0287).

Hong-Nan Li
Gang-Bing Song
Jeong-Tae Kim
Ting-Hua Yi


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