As the number of long-span bridges is increasing worldwide, maintaining their structural integrity and safety become an important issue. Because the stay cable is a critical member in most long-span bridges and vulnerable to wind-induced vibrations, vibration mitigation has been of interest both in academia and practice. While active and semi-active control schemes are known to be quite effective in vibration reduction compared to the passive control, requirements for equipment including data acquisition, control devices, and power supply prevent a widespread adoption in real-world applications. This study develops an integrated system for vibration control of stay-cables using wireless sensors implementing a semi-active control. Arduino, a low-cost single board system, is employed with a MEMS digital accelerometer and a Zigbee wireless communication module to build the wireless sensor. The magneto-rheological (MR) damper is selected as a damping device, controlled by an optimal control algorithm implemented on the Arduino sensing system. The developed integrated system is tested in a laboratory environment using a cable to demonstrate the effectiveness of the proposed system on vibration reduction. The proposed system is shown to reduce the vibration of stay-cables with low operating power effectively.
System identification is a fundamental process for developing a numerical model of a physical structure. The system
identification process typically involves in data acquisition; particularly in civil engineering applications accelerometers
are preferred due to its cost-effectiveness, low noise, and installation convenience. Because the measured acceleration
responses result in translational degrees of freedom (DOF) in the numerical model, moment-resisting structures such as
beam and plate are not appropriately represented by the models. This study suggests a system identification process that
considers both translational and rotational DOFs by using accelerometers and gyroscopes. The proposed approach
suggests a systematic way of obtaining dynamic characteristics as well as flexibility matrix from two different
measurements of acceleration and angular velocity. Numerical simulation and laboratory experiment are conducted to
validate the efficacy of the proposed system identification process.
KEYWORDS: Bridges, Sensors, Smart sensors, Sensor networks, Structural health monitoring, Antennas, Data processing, Solar cells, Connectors, Data transmission
Cables are critical load carrying members of cable-stayed bridges; monitoring tension forces of the cables provides valuable information for SHM of the cable-stayed bridges. Monitoring systems for the cable tension can be efficiently realized using wireless smart sensors in conjunction with vibration-based cable tension estimation approaches. This study develops an automated cable tension monitoring system using MEMSIC’s Imote2 smart sensors. An embedded data processing strategy is implemented on the Imote2-based wireless sensor network to calculate cable tensions using a vibration-based method, significantly reducing the wireless data transmission and associated power consumption. The autonomous operation of the monitoring system is achieved by AutoMonitor, a high-level coordinator application provided by the Illinois SHM Project Services Toolsuite. The monitoring system also features power harvesting enabled by solar panels attached to each sensor node and AutoMonitor for charging control. The proposed wireless system has been deployed on the Jindo Bridge, a cable-stayed bridge located in South Korea. Tension forces are autonomously monitored for 12 cables in the east, land side of the bridge, proving the validity and potential of the presented tension monitoring system for real-world applications.
An electromagnetic energy harvesting device, which converts a translational base motion into a rotational motion by using a rigid bar having a moving mass pivoted on a hinged point with a power spring, has been recently developed for use of civil engineering structures having low natural frequencies. The device utilizes the relative motion between moving permanent magnets and a fixed solenoid coil in order to harvest electrical power. In this study, the performance of the device is enhanced by introducing a rotational-type generator at a hinged point. In addition, a mechanical stopper, which makes use of an auxiliary energy harvesting part to further improve the efficiency, is incorporated into the device. The effectiveness of the proposed hybrid energy harvesting device based on electromagnetic mechanism is verified through a series of laboratory tests.
Wireless Smart Sensor Networks (WSSNs) facilitates a new paradigm to structural identification and monitoring for
civil infrastructure. Conventional monitoring systems based on wired sensors and centralized data acquisition and
processing have been considered to be challenging and costly due to cabling and expensive equipment and maintenance
costs. WSSNs have emerged as a technology that can overcome such difficulties, 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 is common practice, WSSNs require decentralized computing
algorithms to reduce data transmission due to the limitation associated with wireless communication. Thus, several
system identification methods have been implemented to process sensor data and extract essential information, including
Natural Excitation Technique with Eigensystem Realization Algorithm, Frequency Domain Decomposition (FDD), and
Random Decrement Technique (RDT); however, Stochastic Subspace Identification (SSI) has not been fully utilized in
WSSNs, while SSI has the strong potential to enhance the system identification. This study presents a decentralized
system identification using SSI in WSSNs. The approach is implemented on MEMSIC's Imote2 sensor platform and
experimentally verified using a 5-story shear building model.
Wireless Smart Sensor Networks (WSSN) facilitates a new paradigm to structural health monitoring (SHM) for civil
infrastructure. Conventionally, SHM systems employing wired sensors and central data acquisition have been used to
characterize the state of a structure; however, wide-spread implementation has been limited due to difficulties in cabling
and data management, high equipment cost, and long setup time. 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. Wireless smart sensors have shown their tremendous potential for SHM in recent full-scale
bridge monitoring examples. However, structural damage identification in WSSNs, a primary objective of SHM, has yet
to reach its full potential. This paper presents a full-scale validation of the decentralized damage identification
application on the Imote2 sensor platform on a historic steel truss bridge. The SHM application for WSSN developed in the previous research is further combined with continuous and autonomous monitoring application. In total, 144 sensor channels and one base station have been deployed on the bridge for damage localization. The efficacy of the developed application has been demonstrated to compare the damage identification results with the traditional centralized processing.
Rapid advancement of sensor technology has been changing the paradigm of Structural Health Monitoring (SHM)
toward a wireless smart sensor network (WSSN). While smart sensors have the potential to be a breakthrough to current
SHM research and practice, the smart sensors also have several important issues to be resolved that may include robust
power supply, stable communication, sensing capability, and in-network data processing algorithms. This study is a
hybrid WSSN that addresses those issues to realize a full-scale SHM system for civil infrastructure monitoring. The
developed hybrid WSSN is deployed on the Jindo Bridge, a cable-stayed bridge located in South Korea as a continued
effort from the previous year's deployment. Unique features of the new deployment encompass: (1) the world's largest
WSSN for SHM to date, (2) power harvesting enabled for all sensor nodes, (3) an improved sensing application that
provides reliable data acquisition with optimized power consumption, (4) decentralized data aggregation that makes the
WSSN scalable to a large, densely deployed sensor network, (5) decentralized cable tension monitoring specially
designed for cable-stayed bridges, (6) environmental monitoring. The WSSN implementing all these features are
experimentally verified through a long-term monitoring of the Jindo Bridge.
KEYWORDS: Structural health monitoring, Sensors, Smart sensors, Damage detection, Bridges, System identification, Correlation function, Data processing, Matrices, Head
Wireless Smart Sensor Networks (WSSN) facilitates a new paradigm to structural health monitoring (SHM) for civil
infrastructure. Conventionally, SHM systems employing wired sensors and central data acquisition have been used to
characterize the state of a structure; however, wide-spread implementation has been limited due to difficulties in cabling,
high equipment cost, and long setup time. 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. Wireless smart sensors have shown their tremendous potential for SHM in recent full-scale bridge
monitoring examples. However, structural damage identification in WSSNs, a primary objective of SHM, has yet to
reach its full potential. This paper presents an implementation of the stochastic dynamic damage locating vector
(SDDLV) method on the Imote2 sensor platform and experimental validation in a laboratory environment. The WSSN
application is developed based on the Illinois SHM Project (ISHMP) Services Toolsuite (http://shm.cs.uiuc.edu),
combining decentralized data aggregation, system identification, receptance-based damage detection, and global damage
assessment. The laboratory experiment uses a three-dimensional truss structure with a network of Imote2 sensors for
decentralized damage identification. Future efforts to deploy a long-term structural health monitoring system for a fullscale
steel truss bridge are also described.
Smart sensors have been recognized as a promising technology with the potential to overcome many of the inherent
difficulties and limitations associated with traditional wired structural health monitoring (SHM) systems. The unique
features offered by smart sensors, including wireless communication, on-board computation, and cost effectiveness,
enable deployment of the dense array of sensors that are needed for monitoring of large-scale civil infrastructure.
Despite the many advances in smart sensor technologies, power consumption is still considered as one of the most
important challenges that should be addressed for the smart sensors to be more widely adopted in SHM applications.
Data communication, the most significant source of the power consumption, can be reduced by appropriately selecting
data processing schemes and the related network topology. This paper presents a new decentralized data aggregation
approach for system identification based on the Random Decrement Technique (RDT). Following a brief overview of
the RDT, which is an output-only system identification approach, a decentralized hierarchical approach is described and
shown to be suitable for implementation in the intrinsically distributed computing environment found in wireless smart
sensor networks (WSSNs). RDT-based decentralized data aggregation is then implemented on the Imote2 smart sensor
platform based on the Illinois Structural Health Monitoring Project (ISHMP) Services Toolsuite. Finally, the efficacy of
the RDT method is demonstrated experimentally in terms of the required data communication and the accuracy of
identified dynamic properties.
Understanding the dynamic behavior of civil engineering structures is important to adequately resolve problems related
to structural vibration. The dynamic properties of a structure are commonly obtained by conducting a modal survey that
can be used for model updating, design verification, and improvement of serviceability. However, particularly for largescale
civil structures, modal surveys using traditional wired sensor systems can be quite challenging to carry out due to
difficulties in cabling, high equipment cost, and long setup time. Smart sensor networks (SSN) 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 a common practice, the SSN requires decentralized algorithms due to the limitation
associated with wireless communication; to date such algorithms are limited. This paper proposes a new decentralized
hierarchical approach for modal analysis that reliably determines the global modal properties and can be implemented on
a network of smart sensors. The efficacy of the proposed approach is demonstrated through several numerical examples.
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