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

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

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

Structural Health Monitoring for Bridge Structures using Smart Sensors

Structural health monitoring (SHM) has drawn significant attention in recent decades because of its potential to reduce maintenance costs and increase the reliability of structures. An important class of structures that can potentially benefit from SHM are bridges, many of which are structurally deficient due to lack of adequate maintenance. Through condition assessment of these bridges, an effective plan of maintenance can be determined, offering the possibility to prolong service life, as well as to prevent catastrophic disasters due to sudden collapse. To date, numerous damage detection algorithms have been proposed. Still, challenges remain in applying such algorithms to monitor bridges in the field. In reality, the extent of an SHM system is limited by available budgets, which define the number of sensors that can be deployed. A solution to include many sensors within a limited budget with increased efficiency is to use a Wireless Smart Sensor Network (WSSN) because of the merits of low cost, easy installation, and effective data management. An acceleration-based SHM algorithm for WSSN has been developed with a decentralized network topology. This approach has been implemented into a modularized damage detection service. The SHM application is designed to leverage the on-board computation capability of the WSSN, reducing the transmitted data size by distributing the computation burden. The SHM application for WSSN has been validated in lab-scale experiments on a truss bridge model. Nonetheless, the real challenge of SHM is in the deployment on full-scale bridges for continuous monitoring. The usability and stability of WSSN has been validated on an architectural staircase in the Siebel Center. Based on the usability investigation, the deployment of the world’s largest WSSN on the Jindo Bridge, a cable-stayed bridge has been achieved in South Korea. The main purpose of the deployment was to validate the bridge monitoring system using WSSN and energy harvesting devices in a long-term manner. The ultimate goal of this report is to deploy the developed on-board decentralized damage identification application using WSSN on a historic truss bridge. As a first step, a series of dynamic tests were conducted for modal analysis using both wired and wireless sensor systems. During the tests, the functionality of the wireless sensor system with ISHMP Services Toolsuite was confirmed. For model-based damage identification approach developed herein, a finite element (FE) model was created. The initial FE model was updated based on a visual estimate of the corrosion. The updated model was used to generate baseline information for damage detection. Finally, the WSSN-based autonomous SHM system using the decentralized damage detection application was deployed on the historic bridge. The permanent SHM system was installed on the bridge, and the damage detection application was successfully run on the bridge. The damage detection results using the decentralized comprehensive application will be compared with those from the centralized approach using WSSN. The performance of WSSN and energy harvesting devices will be evaluated. In summary, this report provides a robust SHM system for bridge structures in use of WSSN.Financial support for this research was provided in part by a Samsung Scholarship, the National Science Foundation (NSF) under NSF grant CMS 06-00433 (Dr. S. C. Liu, Program Manager), and the Global Research Network program from the Natural Research Foundation in Korea (NRF-2008-220-D00117).Ope

Multi-axial Real-time Hybrid Simulation Framework for Testing Nonlinear Structural Systems with Multiple Boundary Interfaces

Hybrid simulation is a widely accepted laboratory testing approach that partitions a proposed structure into numerical and physical substructures, for a space- and cost-effective testing method. Structural elements that are expected to remain in the linear elastic range are usually modeled numerically, while computationally intractable nonlinear elements are tested physically. The loads and conditions at the boundaries between the numerical and physical substructures are imposed by servo-hydraulic actuators, with the responses measured by loadcells and displacement transducers. Traditionally, these actuators impose boundary condition displacements at slow speeds, while damping and inertial components for the physical specimen are numerically calculated. This slow application of the boundary conditions neglects rate-dependent behavior of the physical specimen. Real-time hybrid simulation (RTHS) is an alternative to slow speed hybrid simulation approach, where the responses of numerical substructure are calculated and imposed on the physical substructure at real world natural hazard excitation speeds. Damping, inertia, and rate-dependent material effects are incorporated in the physical substructure as a result of real-time testing. For a general substructure, the boundary interface has six degrees-of-freedom (DOF); therefore, an actuation system that can apply multi-axial loads is required. In these experiments, the boundary conditions at the interface between the physical and numerical substructures are imposed by two or more actuators. Significant dynamic coupling can be present between the actuators in such setups. Kinematic transformations are required for operation of each actuator to achieve desired boundary conditions. Furthermore, each actuator possesses inherent dynamics that needs appropriate compensation to ensure an accurate and stable operation. Most existing RTHS applications to date have involved the substructuring of the reference structures into numerical and physical components at a single interface with a one-DOF boundary condition and force imposed and measured. Multi-DOF boundary conditions have been explored in a few applications, however a general six-DOF stable implementation has never been achieved. A major research gap in the RTHS domain is the development of a multi-axial RTHS framework capable of handling six DOF boundary conditions and forces, as well as presence of multiple physical specimens and numerical-to-physical interfaces. In this dissertation, a multi-axial real-time hybrid simulation (maRTHS) framework is developed for realistic nonlinear dynamic assessment of structures under natural hazard excitation. The framework is comprised of numerical and physical substructures, actuator-dynamics compensation, and kinematic transformations between Cartesian and actuator/transducer coordinates. The numerical substructure is compiled on a real-time embedded system, comprised of a microcontroller setup, with onboard memory and processing, that computes the response of finite element models of the structural system, which are then communicated with the hardware setup via the input-output peripherals. The physical substructure is composed of a multi-actuator boundary condition box, loadcells, displacement transducers, and one or more physical specimens. The proposed compensation is a model-based strategy based on the linearized identified models of individual actuators. The concepts of the model-based compensation approach are first validated in a shake table study, and then applied to single and multi-axis RTHS developments. The capabilities of the proposed maRTHS framework are demonstrated via the multi-axial load and boundary condition boxes (LBCBs) at the University of Illinois Urbana-Champaign, via two illustrative examples. First, the maRTHS algorithm including the decoupled controller, and kinematic transformation processes are validated. In this study, a moment frame structure is partitioned into numerical beam-column finite element model, and a physical column with an LBCB boundary condition. This experiment is comprised of six DOFs and excitation is only applied in the plane of the moment frame. Next, the maRTHS framework is subjected to a more sophisticated testing environment involving a multi-span curved bridge structure. In this second example, two LBCBs are utilized for testing of two physical piers, and excitation is applied bi-directionally. Results from the illustrative examples are verified against numerical simulations. The results demonstrate the accuracy and promising nature of the proposed state-of-the-art framework for maRTHS for nonlinear dynamic testing of structural systems using multiple boundary points.Ope

Monitoring, Modeling, and Hybrid Simulation An Integrated Bayesian-based Approach to High-fidelity Fragility Analysis

Fragility functions are one of the key technical ingredients in seismic risk assessment. The derivation of fragility functions has been extensively studied in the past; however, large uncertainties still exist, mainly due to limited collaboration between the interdependent components involved in the course of fragility estimation. This research aims to develop a systematic Bayesian-based framework to estimate high-fidelity fragility functions by integrating monitoring, modeling, and hybrid simulation, with the final goal of improving the accuracy of seismic risk assessment to support both pre- and post-disaster decision-making. In particular, this research addresses the following five aspects of the problem: (1) monitoring with wireless smart sensor networks to facilitate efficient and accurate pre- and post-disaster data collection, (2) new modeling techniques including innovative system identification strategies and model updating to enable accurate structural modeling, (3) hybrid simulation as an advanced numerical experimental simulation tool to generate highly realistic and accurate response data for structures subject to earthquakes, (4) Bayesian-updating as a systematic way of incorporating hybrid simulation data to generate composite fragility functions with higher fidelity, and 5) the implementation of an integrated fragility analysis approach as a part of a seismic risk assessment framework. This research not only delivers an extensible and scalable framework for high fidelity fragility analysis and reliable seismic risk assessment, but also provides advances in wireless smart sensor networks, system identification, and pseudo-dynamic testing in civil engineering applications.Financial support for this research was provided in part by the National Science Foundation under NSF Grants No. CMS-060043, CMMI-0724172, CMMI-0928886, and CNS-1035573.Ope

Enabling smart city resilience: Post-disaster response and structural health monitoring

The concept of Smart Cities has been introduced to categorize a vast area of activities to enhance the quality of life of citizens. A central feature of these activities is the pervasive use of Information and Communication Technologies (ICT), helping cities to make better use of limited resources. Indeed, the ASCE Vision for Civil Engineering in 2025 (ASCE 2007) portends a future in which engineers will rely on and leverage real-time access to a living database, sensors, diagnostic tools, and other advanced technologies to ensure that informed decisions are made. However, these advances in technology take place against a backdrop of the deterioration of infrastructure, in addition to natural and human-made disasters. Moreover, recent events constantly remind us of the tremendous devastation that natural and human-made disasters can wreak on society. As such, emergency response procedures and resilience are among the crucial dimensions of any Smart City plan. The U.S. Department of Homeland Security (DHS) has recently launched plans to invest $50 million to develop cutting-edge emergency response technologies for Smart Cities. Furthermore, after significant disasters have taken place, it is imperative that emergency facilities and evacuation routes, including bridges and highways, be assessed for safety. The objective of this research is to provide a new framework that uses commercial off-the-shelf (COTS) devices such as smartphones, digital cameras, and unmanned aerial vehicles to enhance the functionality of Smart Cities, especially with respect to emergency response and civil infrastructure monitoring/assessment. To achieve this objective, this research focuses on post-disaster victim localization and assessment, first responder tracking and event localization, and vision-based structural monitoring/assessment, including the use of unmanned aerial vehicles (UAVs). This research constitutes a significant step toward the realization of Smart City Resilience.National Science Foundation Grant No. 1030454Association of American RailroadsOpe