RESEARCH ARTICLE


Structural Health Monitoring of Bridges Via Energy Harvesting Sensor Nodes



N. Bonessio1, P. Zappi2, G. Benzoni1, T. Simunic Rosing2, G. Lomiento3, *
1 Department of Structural Engineering, University of California at San Diego, La Jolla, California, USA
2 Computer Science Department, University of California at San Diego, La Jolla, California, USA
3 Department of Civil Engineering, California State Polytechnic University, Pomona, USA


Article Metrics

CrossRef Citations:
3
Total Statistics:

Full-Text HTML Views: 4051
Abstract HTML Views: 2235
PDF Downloads: 915
ePub Downloads: 861
Total Views/Downloads: 8062
Unique Statistics:

Full-Text HTML Views: 1710
Abstract HTML Views: 1056
PDF Downloads: 611
ePub Downloads: 571
Total Views/Downloads: 3948



Creative Commons License
© Bonessio et al; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/legalcode), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Department of Civil Engineering, California State Polytechnic University, Pomona, 3801 West Temple Avenue, Pomona, California, 91768 USA; Tel: +1(909) 979-5586; Fax: +1(909) 869-4342; E-mail: glomiento@cpp.edu


Abstract

This paper deals with the application of novel sensing technologies to an existing Structural Health Monitoring (SHM) system for bridges. A vibration based SHM algorithm already in use to detect the structural performance degradation of a suspension highway bridge is modified to investigate the feasibility of replacing traditional wired accelerometers with state of the art wireless energy-harvesting sensors. The remodeled SHM algorithm benefits from the sensor nodes’ ability to support automated triggering and data pre-processing. The Random Decrement technique was included in the algorithm as a pre-processing tool to simultaneously reduce noise and amount of stored and transmitted data. Simulations based on available data were used to calibrate the triggering strategy, to verify the effectiveness of the data pre-processing, and to demonstrate power consumption improvements arising from the algorithm modification.

Keywords: Distributed sensor network, energy efficiency, intelligent infrastructure, structural health monitoring, wireless sensor networks.