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Modeling and Monitoring of the Dynamic Response of Railroad Bridges Using Wireless Smart Sensors

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Modeling and Monitoring of the Dynamic Response of Railroad Bridges Using Wireless Smart Sensors NSEL Report Series Report No. NSEL-044 September 2015 Modeling and Monitoring of the Dynamic Response of Railroad Bridges using Wireless Smart Sensors Robin E. Kim and Billie F. Spencer, Jr. Department of Civil and Environmental Engineering University of Illinois at Urbana-Champaign UILU-ENG-2015-1810 ISSN: 1940-9826 The Newmark Structural Engineering Laboratory (NSEL) of the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign has a long history of excellence in research and education that has contributed greatly to the state-of-the-art in civil engineering. Completed in 1967 and extended in 1971, the structural testing area of the laboratory has a versatile strong-floor/wall and a three-story clear height that can be used to carry out a wide range of tests of building materials, models, and structural systems. The laboratory is named for Dr. Nathan M. Newmark, an internationally known educator and engineer, who was the Head of the Department of Civil Engineering at the University of Illinois [1956-73] and the Chair of the Digital Computing Laboratory [1947-57]. He developed simple, yet powerful and widely used, methods for analyzing complex structures and assemblages subjected to a variety of static, dynamic, blast, and earthquake loadings. Dr. Newmark received numerous honors and awards for his achievements, including the prestigious National Medal of Science awarded in 1968 by President Lyndon B. Johnson. He was also one of the founding members of the National Academy of Engineering. Contact: Prof. B.F. Spencer, Jr. Director, Newmark Structural Engineering Laboratory 2213 NCEL, MC-250 205 North Mathews Ave. Urbana, IL 61801 Telephone (217) 333-8630 E-mail: [email protected] This technical report is based on the first author’s doctoral dissertation of the same title, which was completed in April 2015. The second author served as the dissertation advisor for this work. Financial support for this research was provided in part by the National Science Foundation under Grants No. CMS-0600433, CMMI-0928886, Grant No. OISE-1107526, Grant CMMI- 0724172 (NEESR-SD), and the Federal Railroad Administration under the BAA 2010-1 project. Finally, we would like to thank the numerous collaborators on this work, including Sung Han Sim, Shinae Jang, Hongki Jo, Professor Gul Agha, Kirill Mechitov, Jian Li, and Hyungchul Yoon, and Lauren Linderman. The cover photographs are used with permission. The Trans-Alaska Pipeline photograph was provided by Terra Galleria Photography (http://www.terragalleria.com/). ABSTRACT Railroad bridges form an integral part of railway infrastructure in the USA, carrying approximately 40 % of the ton-miles of freight. The US Department of Transportation (DOT) forecasts current rail tonnage to increase up to 88 % by 2035. Within the railway network, a bridge occurs every 1.4 miles of track, on average, making them critical elements. In an effort to accommodate safely the need for increased load carrying capacity, the Federal Railroad Association (FRA) announced a regulation in 2010 that the bridge owners must conduct and report annual inspection of all the bridges. Until now, visual inspection has been the most prevalent practice in monitoring this infrastructure, although high-cost and unreliability can limit the efficiency and accuracy of such assessments. With recent advances in sensing technology, structural health monitoring can be a promising solution for providing a reliable and inexpensive ways for assessing the bridges. Nonetheless, because damage is a local phenomenon, to be able to detect/ monitor existing/potential damage, densely deployed sensors are required, which can be inefficient and still expensive. Alternatively, model-based monitoring strategies can help identifying critical elements using fewer sensors, utilizing a numerical model that has been calibrated with measured field data. Such approaches have been adopted and applied for highway bridges, while railroad bridges have received comparably less attention. The main reason for the limited number of studies is due, in part, to fundamental differences between the loading being applied to highway bridges and railroad bridges. Usually, the mass of the vehicles crossing highway bridges is assumed to be relatively small compared to the mass of the bridge itself; as a result, the mass of the vehicles are often neglected in the problem. In contrast, the mass of a train crossing a railroad bridge can be as large as the mass of the bridge itself. Moreover, trains are typically composed of an engine, followed by multiple cars resulting in a nearly deterministic moving mass/load being applied to the bridge. As a consequence, numerous models have been developed to understand the dynamic response of bridges under in-service train loads, but most fail to provide a simple, yet flexible, representation of the salient features of the responses of the bridge. The objective of this research is to develop appropriate modeling and monitoring techniques for railroad bridges toward understanding the dynamic responses under a moving train. To achieve the research objective, the following issues are considered specifically. For modeling, a simple, yet effective, model is developed to capture salient features of the bridge responses under a moving train. A new hybrid model is then proposed, which is a flexible and efficient tool for estimating bridge responses for arbitrary train configurations and speeds. For monitoring, measured field data is used to validate the performance of the numerical model. Further, interpretation of the proposed models showed that those models are efficient tools for predicting response of the bridge, such as fatigue and resonance. Finally, fundamental software, hardware, and algorithm components are developed for providing synchronized sensing for geographically distributed networks, as can be found in railroad bridges. The results of this research successfully demonstrate the potentials of using wirelessly measured data to perform model development and calibration that will lead to better understanding the dynamic responses of railroad bridges and to provide an effective tool for prediction of bridge response for arbitrary train configurations and speeds.. i CONTENTS Page CHAPTER 1 INTRODUCTION .................................................................................... 5 1.1 Motivation ................................................................................................... 5 1.2 Overview of dissertation ............................................................................. 6 CHAPTER 2 LITERATURE REVIEW ......................................................................... 9 2.1 Railroad bridge dynamics ........................................................................... 9 2.1.1 Numerical models ......................................................................... 10 2.1.2 Parametric studies on railroad bridges .......................................... 12 2.1.3 Experiment efforts on vehicle-bridge interaction system ............. 15 2.2 Structural health monitoring ..................................................................... 16 2.3 Wireless smart sensor (WSS).................................................................... 17 2.3.1 Overview of WSS networks .......................................................... 17 2.3.2 The ISHMP service toolsuites ....................................................... 18 2.3.3 Remaining challenges in the use of WSSs for railroad bridge applications ............................................................................................... 20 2.4 Monitoring railroad bridges ...................................................................... 21 2.4.1 Railroad bridges in the United States ............................................ 21 2.4.2 FRA regulations in inspections ..................................................... 23 2.4.3 Common monitoring strategies for railroad bridge ....................... 26 2.4.4 System identification of a bridge .................................................. 27 2.4.5 System identification of an in-service railroad bridge .................. 28 2.5 Challenges on wireless sensing technologies for monitoring railroad bridges ......................................................................................... 29 2.5.1 Time synchronization on WSSs .................................................... 29 2.5.2 Radio communication quality on WSSs........................................ 32 2.6 Summary ................................................................................................... 34 CHAPTER 3 MODELS FOR PREDICTING BRIDGES DYNAMIC RESPONSE 35 3.1 Simple beam model................................................................................... 35 3.1.1 Model formulation......................................................................... 35 3.1.2 Numerical example ....................................................................... 37 3.2 Hybrid model ............................................................................................ 45 3.2.1 Bridge/track models ...................................................................... 45 3.2.2 Bridge-track interconnection ......................................................... 48 3.3 Conclusion ................................................................................................ 54 CHAPTER 4 MODEL VALIDATION ........................................................................
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