KEYWORDS: Earthquakes, Bridges, Reliability, Sensors, Structural health monitoring, Solar cells, Inspection, Data processing, Failure analysis, Data modeling
The ability to quantitatively assess the condition of railroad bridges facilitates objective evaluation of their robustness in the face of hazard events. Of particular importance is the need to assess the condition of railroad bridges in networks that are exposed to multiple hazards. Data collected from structural health monitoring (SHM) can be used to better maintain a structure by prompting preventative (rather than reactive) maintenance strategies and supplying quantitative information to aid in recovery. To that end, a wireless monitoring system is validated and installed on the Harahan Bridge which is a hundred-year-old long-span railroad truss bridge that crosses the Mississippi River near Memphis, TN. This bridge is exposed to multiple hazards including scour, vehicle/barge impact, seismic activity, and aging. The instrumented sensing system targets non-redundant structural components and areas of the truss and floor system that bridge managers are most concerned about based on previous inspections and structural analysis. This paper details the monitoring system and the analytical method for the assessment of bridge condition based on automated data-driven analyses. Two primary objectives of monitoring the system performance are discussed: 1) monitoring fatigue accumulation in critical tensile truss elements; and 2) monitoring the reliability index values associated with sub-system limit states of these members. Moreover, since the reliability index is a scalar indicator of the safety of components, quantifiable condition assessment can be used as an objective metric so that bridge owners can make informed damage mitigation strategies and optimize resource management on single bridge or network levels.
KEYWORDS: Bridges, Analytics, Structural health monitoring, Data modeling, Data conversion, Databases, Smart structures, Sensors, Optical inspection, Reliability, Data storage
Structural Health Monitoring (SHM) can be a vital tool for effective bridge management. Combining large data
sets from multiple sources to create a data-driven decision-making framework is crucial for the success of SHM.
This paper presents a big data analytics framework that combines multiple data sets correlated with functional
relatedness to convert data into actionable information that empowers risk-based decision-making. The
integrated data environment incorporates near real-time streams of semi-structured data from remote sensors,
historical visual inspection data, and observations from structural analysis models to monitor, assess, and
manage risks associated with the aging bridge inventories. Accelerated processing of dataset is made possible
by four technologies: cloud computing, relational database processing, support from NOSQL database, and in-memory
analytics.
The framework is being validated on a railroad corridor that can be subjected to multiple hazards. The
framework enables to compute reliability indices for critical bridge components and individual bridge spans. In
addition, framework includes a risk-based decision-making process that enumerate costs and consequences of
poor bridge performance at span- and network-levels when rail networks are exposed to natural hazard events
such as floods and earthquakes. Big data and high-performance analytics enable insights to assist bridge owners
to address problems faster.
A worthy goal for the structural health monitoring field is the creation of a scalable monitoring system architecture that abstracts many of the system details (e.g., sensors, data) from the structure owner with the aim of providing “actionable” information that aids in their decision making process. While a broad array of sensor technologies have emerged, the ability for sensing systems to generate large amounts of data have far outpaced advances in data management and processing. To reverse this trend, this study explores the creation of a cyber-enabled wireless SHM system for highway bridges. The system is designed from the top down by considering the damage mechanisms of concern to bridge owners and then tailoring the sensing and decision support system around those concerns. The enabling element of the proposed system is a powerful data repository system termed SenStore. SenStore is designed to combine sensor data with bridge meta-data (e.g., geometric configuration, material properties, maintenance history, sensor locations, sensor types, inspection history). A wireless sensor network deployed to a bridge autonomously streams its measurement data to SenStore via a 3G cellular connection for storage. SenStore securely exposes the bridge meta- and sensor data to software clients that can process the data to extract information relevant to the decision making process of the bridge owner. To validate the proposed cyber-enable SHM system, the system is implemented on the Telegraph Road Bridge (Monroe, MI). The Telegraph Road Bridge is a traditional steel girder-concrete deck composite bridge located along a heavily travelled corridor in the Detroit metropolitan area. A permanent wireless sensor network has been installed to measure bridge accelerations, strains and temperatures. System identification and damage detection algorithms are created to automatically mine bridge response data stored in SenStore over an 18-month period. Tools like Gaussian Process (GP) regression are used to predict changes in the bridge behavior as a function of environmental parameters. Based on these analyses, pertinent behavioral information relevant to bridge management is autonomously extracted.
KEYWORDS: Sensors, Bridges, Inspection, Data modeling, Data processing, Finite element methods, Structural health monitoring, Antennas, Internet, Sensing systems
The long-term deterioration of large-scale infrastructure systems is a critical national problem that if left unchecked,
could lead to catastrophes similar in magnitude to the collapse of the I-35W Bridge. Fortunately, the past decade has
witnessed the emergence of a variety of sensing technologies from many engineering disciplines including from the
civil, mechanical and electrical engineering fields. This paper provides a detailed overview of an emerging set of sensor
technologies that can be effectively used for health management of large-scale infrastructure systems. In particular, the
novel sensing technologies are integrated to offer a comprehensive monitoring system that fundamentally addresses the
limitations associated with current monitoring systems (for example, indirect damage sensing, cost, data inundation and
lack of decision making tools). Self-sensing materials are proposed for distributed, direct sensing of specific damage
events common to civil structures such as cracking and corrosion. Data from self-sensing materials, as well as from
more traditional sensors, are collected using ultra low-power wireless sensors powered by a variety of power harvesting
devices fabricated using microelectromechanical systems (MEMS). Data collected by the wireless sensors is then
seamlessly streamed across the internet and integrated with a database upon which finite element models can be
autonomously updated. Life-cycle and damage detection analyses using sensor and processed data are streamed into a
decision toolbox which will aid infrastructure owners in their decision making.
There are numerous Structural identification (StrId) methods and techniques. The purpose of StrId in the civil
infrastructure arena include: a) design validation of new structures, b) condition assessment of existing structures, c)
analytical model updating of existing and new structures, and d) damage identification (DmId) of existing structures.
Many StrId researchers utilized StrId methods for DmId. However, not all StrId methods are suited for DmId and not all
damages are possible to be identified by popular StrId methods. This paper investigates the relationship between StrId
methods and DmId. We investigate which StrId method is suited for DmId for the type of damages that may affect civil
infrastructure. It will be shown that some damages can be identified by StrId methods, while some other damages need
specific methods that are not within the conventional realm of StrId methods. Some guidelines for choosing StrId
methods that are appropriate for DmId are given.
Smart structures can be subdivided into two general categories. The first is structures that would perform 'smartly' during a specific event, e.g. earthquake events. The second category is the structures that would perform 'smartly' during any normal and/or abnormal event. The second category includes structures sensitive to vibration levels, structures which are exposed to wind, since wind can affect the structure from numerous directions and bridges, since loads on a bridge can have an arbitrary location at a given time. the distinction between the two structural types will have an important effect on both the optimal sensor number and locations. Recently, researchers have presented several methods for optimal number and location of sensors. These methods are based on different optimization techniques. In most of these published studies, many variable shave been investigated. These include the number and location of sensors, number of structural degrees of freedom, number of tests, and number of structural modes. All these studies assume that the location and the magnitude of damage are known a priori, before locating the sensors. In addition, in most of the published studies, the variability of the location of the loading source has not been well studied. This paper present a simple approach, which address the above issues. First, the stress ratio concept is used to logically estimate the expected location and magnitude of structural damage. Second, the load factor approach in combination with the goal programming algorithm will be presented as a means to find the optimal sensor location when dealing with structures with multiple loading conditions. The examples in this study demonstrate these techniques.
A basic step in the monitoring of health of any structure is detection of damage levels and location of the damage in the system. Several damage detection schemes have been proposed in recent years. Published applications of these methods are typically for simplified models of realistic structures. This leaves open the question of the accuracy and efficiency of the available damage detection methods when applied to large and complex structural models. This paper will investigate the accuracy of different damage detection techniques to complex structural models. A typical multi- jointed steel bridge, which is damaged by cracks of different sizes, is considered. The damage will be simulated analytically in the structural model, and the damage detection algorithms will be applied to both the damaged and the undamaged structures. Three damage detection algorithms are investigated, namely the change of stiffness, the change of flexibility and the damage index methods. Some modifications and extensions of the change of stiffness and change of flexibility methods were incorporated in this study. These extensions helped inaccurate comparisons between different methods. It was found that all three algorithms produced adequate damage detection results. More analytical studies are recommended. Also, experimental testing for complex structures is recommended.
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