TY - GEN
T1 - Dynamic curvature based monitoring in a highway overpass
AU - Kliewer, K.
AU - Glisic, B.
N1 - Funding Information:
This material is based upon work supported by NSF GRFP Grant No. 1148900, NSF CMMI-1362723, and USDOT-RITA DTRT12-G-UTC16. Any opinions, findings, and conclusions or recommendations expressed herein are those of the authors and do not necessarily reflect the views of the funding agencies. The authors would like to thank the following individuals for their assistance with this research: Corrie Kavanaugh, Dorotea Sigurdardottir, Dennis Smith, and Joe Vocaturo. The project on the US202/NJ23 highway overpass in Wayne has been realized with the important support, great help and kind collaboration of several professionals and companies. We would like to thank SMARTEC SA, Switzerland; Drexel University, in particular Professor Emin Aktan, Professor Frank Moon (now at Rutgers University), and graduate student Jeff Weidner (now Assistant Professor at University of Texas, El Paso); New Jersey Department of Transportation (NJDOT), and in particular Nat Kasbekar and Eddy Germain; Long-Term Bridge Performance (LTBP) Program of Federal Highway Administration; PB Americas, Inc., Lawrenceville, NJ, in particular Mr. Michael S Morales, LTBP Site Coordinator; Rutgers University, in particular Professors Ali Maher and Nenad Gucunski; All IBS partners; and Kevin the lift operator. We would also like to thank Yao Yao who helped with the sensor installation.
Publisher Copyright:
© 2017 International Society for Structural Health Monitoring of Intelligent Infrastrucure. All rights reserved.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2017
Y1 - 2017
N2 - With the growing challenge of aging infrastructure and the increasing cost for replacement and repair, structural health monitoring (SHM) offers an approach to address these challenges. It has been found in the literature that curvature and strain based methods may offer a more reliable approach to dynamic SHM compared to other methods such as acceleration and frequency based approaches. This research focuses on the application of a curvature based damage detection method, the normalized curvature ratio (NCR), to an in-service highway overpass. This method is widely applicable to beam like structures because it permits a structure to remain in-service, utilizes the service loads for monitoring, and is adaptable to a variety of sensor arrangements. Additionally, this research will focus on the use of long-gage fiber Bragg grating (FBG) sensors as they offer numerous benefits compared to other sensors currently available, such as low cost, multiplexing capabilities and the ability for both static and dynamic monitoring. Fiber optic sensors also allow for the instrumentation of large areas of a structure with long-gages sensors which helps enable global monitoring of the structure. Previous research applying this curvature based method in both small-scale laboratory testing and applied this method to a girder on an inservice highway overpass, demonstrating the feasibility of this method as a potential damage sensitive feature. This resaerch will focus on the applications of this method to girder 2 of the in-service highway overpass. Long term dynamic strain measurements from vibrations due to traffic loading on the structure have been measured through a series of FBG strain sensors instrumented on the structure. This research shows encouraging results and the potential for the NCR to be used as a simplistic metric for damage detection using FBG strain sensors.
AB - With the growing challenge of aging infrastructure and the increasing cost for replacement and repair, structural health monitoring (SHM) offers an approach to address these challenges. It has been found in the literature that curvature and strain based methods may offer a more reliable approach to dynamic SHM compared to other methods such as acceleration and frequency based approaches. This research focuses on the application of a curvature based damage detection method, the normalized curvature ratio (NCR), to an in-service highway overpass. This method is widely applicable to beam like structures because it permits a structure to remain in-service, utilizes the service loads for monitoring, and is adaptable to a variety of sensor arrangements. Additionally, this research will focus on the use of long-gage fiber Bragg grating (FBG) sensors as they offer numerous benefits compared to other sensors currently available, such as low cost, multiplexing capabilities and the ability for both static and dynamic monitoring. Fiber optic sensors also allow for the instrumentation of large areas of a structure with long-gages sensors which helps enable global monitoring of the structure. Previous research applying this curvature based method in both small-scale laboratory testing and applied this method to a girder on an inservice highway overpass, demonstrating the feasibility of this method as a potential damage sensitive feature. This resaerch will focus on the applications of this method to girder 2 of the in-service highway overpass. Long term dynamic strain measurements from vibrations due to traffic loading on the structure have been measured through a series of FBG strain sensors instrumented on the structure. This research shows encouraging results and the potential for the NCR to be used as a simplistic metric for damage detection using FBG strain sensors.
UR - http://www.scopus.com/inward/record.url?scp=85050162864&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050162864&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85050162864
T3 - SHMII 2017 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Proceedings
SP - 501
EP - 508
BT - SHMII 2017 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Proceedings
A2 - Mahini, Saeed
A2 - Mahini, Saeed
A2 - Chan, Tommy
PB - International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII
T2 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2017
Y2 - 5 December 2017 through 8 December 2017
ER -