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

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. 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.National Science Foundation Grant No. CMS-0600433National Science Foundation Grant No. CMMI-0928886National Science Foundation Grant No. OISE-1107526National Science Foundation Grant No. CMMI- 0724172 (NEESR-SD)Federal Railroad Administration BAA 2010-1 projectOpe

Decentralized identification and multimetric monitoring of civil infrastructure using smart sensors

Wireless Smart Sensor Networks (WSSNs) facilitates a new paradigm to structural identification and monitoring for civil infrastructure. Conventionally, wired sensors and central data acquisition systems have been used to characterize the state of the structure, which is quite challenging due to difficulties in cabling, long setup time, and high equipment and maintenance costs. WSSNs offer a unique opportunity to overcome such difficulties. Recent advances in sensor technology have realized low-cost, smart sensors with on-board computation and wireless communication capabilities, making deployment of a dense array of sensors on large civil structures both feasible and economical. However, as opposed to wired sensor networks in which centralized data acquisition and processing are common practice, WSSNs require decentralized algorithms due to the limitation associated with wireless communication; to date such algorithms are limited. This research develops new decentralized algorithms for structural identification and monitoring of civil infrastructure. To increase performance, flexibility, and versatility of the WSSN, the following issues are considered specifically: (1) decentralized modal analysis, (2) efficient decentralized system identification in the WSSN, and (3) multimetric sensing. Numerical simulation and laboratory testing are conducted to verify the efficacy of the proposed approaches. The performance of the decentralized approaches and their software implementations are validated through full-scale applications at the Irwin Indoor Practice Field in the University of Illinois at Urbana-Champaign and the Jindo Bridge, a 484 meter-long cable-stayed bridge located in South Korea. This research provides a strong foundation on which to further develop long-term monitoring employing a dense array of smart sensors. The software developed in this research is opensource and is available at: http://shm.cs.uiuc.edu/.NSF Grant No. CMS-060043NSF Grant No. CMMI-0724172NSF Grant No. CMMI-0928886NSF Grant No. CNS-1035573Ope

Dynamic Voltage Scaling Techniques for Energy Efficient Synchronized Sensor Network Design

Building energy-efficient systems is one of the principal challenges in wireless sensor networks. Dynamic voltage scaling (DVS), a technique to reduce energy consumption by varying the CPU frequency on the fly, has been widely used in other settings to accomplish this goal. In this paper, we show that changing the CPU frequency can affect timekeeping functionality of some sensor platforms. This phenomenon can cause an unacceptable loss of time synchronization in networks that require tight synchrony over extended periods, thus preventing all existing DVS techniques from being applied. We present a method for reducing energy consumption in sensor networks via DVS, while minimizing the impact of CPU frequency switching on time synchronization. The system is implemented and evaluated on a network of 11 Imote2 sensors mounted on a truss bridge and running a high-fidelity continuous structural health monitoring application. Experimental measurements confirm that the algorithm significantly reduces network energy consumption over the same network that does not use DVS, while requiring significantly fewer re-synchronization actions than a classic DVS algorithm.unpublishedis peer reviewe

Cross-Correlation-Based Structural System Identification Using Unmanned Aerial Vehicles.

Computer vision techniques have been employed to characterize dynamic properties of structures, as well as to capture structural motion for system identification purposes. All of these methods leverage image-processing techniques using a stationary camera. This requirement makes finding an effective location for camera installation difficult, because civil infrastructure (i.e., bridges, buildings, etc.) are often difficult to access, being constructed over rivers, roads, or other obstacles. This paper seeks to use video from Unmanned Aerial Vehicles (UAVs) to address this problem. As opposed to the traditional way of using stationary cameras, the use of UAVs brings the issue of the camera itself moving; thus, the displacements of the structure obtained by processing UAV video are relative to the UAV camera. Some efforts have been reported to compensate for the camera motion, but they require certain assumptions that may be difficult to satisfy. This paper proposes a new method for structural system identification using the UAV video directly. Several challenges are addressed, including: (1) estimation of an appropriate scale factor; and (2) compensation for the rolling shutter effect. Experimental validation is carried out to validate the proposed approach. The experimental results demonstrate the efficacy and significant potential of the proposed approach

Structural Control Strategies for Earthquake Response Reduction of Buildings

Destructive seismic events continue to demonstrate the importance of mitigating these hazards to building structures. To protect buildings from such extreme dynamic events, structural control has been considered one of the most effective strategies. Structural control strategies can be divided into four classes: passive, active, semi-active, and hybrid control. Because passive control systems are well understood and require no external power source, they have been accepted widely by the engineering community. However, these passive systems have the limitation of not being able to adapt to varying conditions. While active systems are able to do that, they require a significant amount of power to generate large control forces. Moreover, the stability of active systems is not ensured. The focus of this report is the improvement and the validation of semi-active control strategies, especially with MR dampers, for building protection from severe earthquakes. To make semi active control strategies more practical, further studies on both the numerical and experimental aspects of the problem are conducted. The research presented in this report contributes the improvement and prevalence of semi-active control strategies in building structures to mitigate seismic damage.Financial support for this research was provided in part by the Long Term Fellowship for Study Abroad by the MEXT (Ministry of Education, Culture, Sports, Science, and Technology, Japan) and the Newmark Account.Ope

Framework for Consequence-based Management and Safety of Railroad Bridge Infrastructure Using Wireless Smart Sensors (WSS)

To increase overall profitability, add capacity to rail operations, and comply with new federal regulations on bridge safety, North American railroads are exploring means to improve the management of their bridge networks. Current maintenance, repair, and replacement (MRR) decisions are informed by bridge inspections and ratings, which recommend observing the response of bridges under trains. However, an objective relationship between bridge responses, bridge service state condition, and the associated impact to railroad operations has yet to be established. If the consequences of MRR decisions could be better determined, then the railroads could more effectively allocate their limited resources. This paper develops an approach for consequence-based management of bridge networks, adopted from the field of seismic risk assessment, for making MRR decisions on a network-wide basis. The proposed framework employs fragility curves to relate service condition limit-states to bridge displacement traffic. The operational costs associated with these service conditions can be used to estimate the total costs of a given MRR policy. In this way, optimum MRR decisions can minimize the total network costs. Additionally, measured bridge data can be used to update periodically the fragilities. This framework provides a consistent approach for the prioritization of railroad bridge MRR decisionsFinancial support for this research was provided in part the American Association of Railroads (AAR) Technology Scanning Program; the O. H. Ammann Research Fellowship of the Structural Engineering Institute (SEI) - American Society of Civil Engineers (ASCE); the Talentia Fellowship (Junta de Andalucía, Spain); the Structural Engineering Association of Illinois (SEAOI); the Illinois Graduate College Dissertation Travel Committee at the University of Illinois at Urbana-Champaign (UIUC); the Federal Railroad Administration (FRA); the Foreign Language and Area Studies (FLAS) Fellowships program (from the Department of Education of the United States); the Center for Global Studies and the Center for East Asian and Pacific Studies at the University of Illinois. In-kind funding was provided by the CN, BNSF, and NS railroads.Ope

Multi-scale Structural Health Monitoring using Wireless Smart Sensors

Tremendous progress has been made in recent years in the wireless smart sensor (WSS) technology to monitor civil infrastructures, shifting focus away from traditional wired methods. Successful implementations of such WSS networks for full-scale SHM have demonstrated the feasible use of the technology. Much of the previous research and application efforts have been directed toward single-metric applications. Multi-metric monitoring, in combination with physics-based models, has great potential to enhance SHM methods; however, the efficacy of the multi-metric SHM has not been illustrated using WSS networks to date, due primarily to limited hardware capabilities of currently available smart sensors and lack of effective algorithms. This research seeks to develop multi-scale WSSN strategies for advanced SHM in cost effective manner by considering: (1) the development of hybrid SHM method, which combine numerical modeling and multi-metric physical monitoring, (2) multi-metric and high-sensitivity hardware developments for use in WSSNs, (3) network software developments for robust WSSN, (4) algorithms development to better utilize the outcomes from SHM system, and (5) fullscale experimental validation of proposed research. The completion of this research will result in an advanced multi-scale WSS framework to provide innovate ways civil infrastructure is monitored.Financial support for this research was provided in part by the National Science Foundation under NSF Grants No. CMS-0600433 and CMMI-0928886.Ope