Coastal infrastructure, such as bridges, are susceptible to many forms of coastal hazards: particularly hurricane surge and wave loading. These two forms of loading can cause catastrophic damage to aging highway infrastructure. It is estimated that storm damage costs the United States about $50 Billion per year. In light of this, it is crucial that we understand the damaging forces placed on infrastructure during storm events so that we can develop safer and more resilient coastal structures. This paper presents the ongoing research to enable the efficient collection of extreme event loads acting on both the substructure and superstructure of low clearance, simple span, reinforced concrete bridges. Bridges of this type were commonly constructed during the 1950’s and 60’s and are particularly susceptible to deck unseating caused by hurricane surge and wave loading. The sensing technology used to capture this data must be ruggedized to survive in an extremely challenging environment, be designed to allow for redundancy in the event of sensors or other network components being lost in the storm, and be relatively low cost to allow for more bridges to be instrumented per storm event. The prototype system described in this paper includes wireless technology, rapid data transmission, and, for the sensors, self-contained power. While this specific application focuses on hurricane hazards, the framework can be extended to include other natural hazards.
Post-tensioned segmental bridges are common throughout the US; however, in recent years, the incidence of tendon failure in bonded post-tensioned bridges has raised questions regarding their design, construction, and maintenance. These failures have led to the investigation of the applicability of using replaceable unbonded tendons in segmental construction and new methods for monitoring their condition. This paper presents a damage detection algorithm to identify strand breakage in unbonded tendons based on the relative variation of strains in the anchorage. In unbonded construction, the anchorage assembly usually undergoes a severe stress-state condition as the entire prestressing force only passes through the deviator and end anchorage locations. The strain distribution in the anchorage mechanism, therefore, goes through significant changes in response to the breakage of an individual wire or an entire strand in a multi-strand arrangement. In this way, breakage of a post-tensioning strand can be identified by observing a non-uniform variation of the strain field over the anchorage region in contrast to a uniform variation of strains due to environmental or traffic loading. A reduced scale laboratory experiment is performed followed by an extensive finite element simulation to conduct a parametric study with wire/strand breakages at different locations on multi-strand anchorages commonly used in industry. Based on the observed strain variations from simulation, a damage detection model is proposed that enables the adoption of an automated monitoring strategy to characterize the breakage programmatically.
KEYWORDS: Radar, Radar sensor technology, Sensor networks, Calibration, Sensors, Signal to noise ratio, Demodulation, Structural health monitoring, Bridges, Signal processing
This paper presents a multiple input multiple output (MIMO) wireless radar sensor network capable of measuring lower-frequency vibration and static deflection in bridges. An integrated simulation model that combines a multi degree-of-freedom structural model with a realistic model of the radar sensor network is introduced and used to characterize and predict the network’s functionality in different measurement conditions. In addition, a series of laboratory experiments have been performed for comparison with the simulation model. Finally, challenges associated with achieving accurate measurements from the radar network in a range of testing environments are discussed.
KEYWORDS: Radar, Sensors, Bridges, Radar sensor technology, Transponders, Structural health monitoring, Backscatter, Sensor networks, Signal to noise ratio, Signal processing
The development of effective structural health monitoring (SHM) strategies is critical as aging infrastructure remains a
national concern with widespread impact on the quality of our daily lives. Wireless smart sensor networks (WSSNs) are
an attractive alternative to traditional SHM systems for their lower deployment cost and their ability to enable new
methods of distributed data processing. While acceleration has been the primary measurement utilized in most WSSN
SHM applications, practically and accurately capturing structural deflections has been proven much more challenging.
Displacement sensors produce reliable low-frequency measurements but are often difficult to implement in long-term
field deployments. Conventional technologies for measuring deflection, both dynamic and static, are either too bulky or
expensive to be integrated into WSSNs or lack sufficient accuracy. This paper presents the validation and
characterization of a network of low-cost, wireless radar-based sensors for the enhancement of low-frequency vibrationbased
bridge monitoring and the measurement of static bridge deflections. Experimental results utilizing a laboratoryscale
truss bridge are presented and the performance of the wireless radar sensors is compared to conventional vibration
and displacement transducers. In addition, challenges associated with detection distance, interference rejection and
signal processing are discussed.
Wireless smart sensor technology offers many opportunities to advance infrastructure monitoring and maintenance by
providing pertinent information regarding the condition of a structure at a lower cost and higher density than traditional
monitoring approaches. Many civil structures, especially long-span bridges, have low fundamental response frequencies
that are challenging to accurately measure with sensors that are suitable for integration with low-cost, low-profile, and
power-constrained wireless sensor networks. Existing displacement sensing technology is either not practical for
wireless sensor implementations, does not provide the necessary accuracy, or is simply too cost-prohibitive for dense
sensor deployments. This paper presents the development and integration of an accurate, low-cost radar-based sensor for
the enhancement of low-frequency vibration-based bridge monitoring and the measurement of static bridge deflections.
The sensors utilize both a nonlinear vibrometer mode and an arctangent-demodulated interferometry mode to achieve
sub-millimeter measurement accuracy for both periodic and non-periodic displacement. Experimental validation results
are presented and discussed.
KEYWORDS: Structural health monitoring, Data acquisition, Sensors, Clocks, Sensor networks, Analog electronics, Data communications, Microcontrollers, Smart sensors, Data processing
Researchers have made significant progress in recent years towards realizing long-term structural health monitoring
(SHM) utilizing wireless smart sensor networks (WSSNs). These efforts have focused on improving the
performance and robustness of such networks to achieve high quality data acquisition and in-network processing.
One of the primary challenges still facing the use of smart sensors for long-term monitoring deployments is their
limited power resources. Periodically accessing the sensor nodes to change batteries is not feasible or economical
in many deployment cases. While energy harvesting techniques show promise for prolonging unattended network
life, low-power design and operation are still critically important. This research presents a new, fully integrated
ultra-low power wireless smart sensor node and a flexible base station, both designed for long-term SHM applications.
The power consumption of the sensor nodes and base station has been minimized through careful
hardware selection and the implementation of power-aware network software, without sacrificing flexibility and
functionality.
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.
The demands of aging infrastructure require effective methods for structural monitoring and maintenance. Wireless
smart sensor networks offer the ability to enhance structural health monitoring (SHM) practices through the utilization of
onboard computation to achieve distributed data management. Such an approach is scalable to the large number of
sensor nodes required for high-fidelity modal analysis and damage detection. While smart sensor technology is not new,
the number of full-scale SHM applications has been limited. This slow progress is due, in part, to the complex network
management issues that arise when moving from a laboratory setting to a full-scale monitoring implementation. This
paper presents flexible network management software that enables continuous and autonomous operation of wireless
smart sensor networks for full-scale SHM applications. The software components combine sleep/wake cycling for
enhanced power management with threshold detection for triggering network wide tasks, such as synchronized sensing
or decentralized modal analysis, during periods of critical structural response.
KEYWORDS: Sensors, Structural health monitoring, Smart sensors, Interference (communication), Sensor networks, Magnesium, Analog electronics, Power supplies, Microelectromechanical systems, Signal to noise ratio
State-of-the-art wireless smart sensor technology enables a dense array of sensors to be distributed through a
structure to provide an abundance of structural information. However, the relatively low resolution of the
MEMS sensors that are generally adopted for wireless smart sensors limits the network's ability to measure lowlevel
vibration often found in the ambient vibration response of building structures. To address this problem,
development of a high-sensitivity acceleration board for the Imote2 platform using a low-noise accelerometer is
presented. The performance of this new sensor board is validated through extensive laboratory testing. In
addition, the use of the high-sensitivity accelerometer board as a reference sensor to improve the capability to
capture structural behavior in the smart sensor network is discussed.
KEYWORDS: Bridges, Smart sensors, Structural health monitoring, Sensors, Sensor networks, Wind measurement, Data communications, Energy harvesting, Solar cells, Head
This paper presents a structural health monitoring (SHM) system using a dense array of scalable smart wireless sensor
network on a cable-stayed bridge (Jindo Bridge) in Korea. The hardware and software for the SHM system and its
components are developed for low-cost, efficient, and autonomous monitoring of the bridge. 70 sensors and two base
station computers have been deployed to monitor the bridge using an autonomous SHM application with consideration of
harsh outdoor surroundings. The performance of the system has been evaluated in terms of hardware durability, software
reliability, and power consumption. 3-D modal properties were extracted from the measured 3-axis vibration data using
output-only modal identification methods. Tension forces of 4 different lengths of stay-cables were derived from the
ambient vibration data on the cables. For the integrity assessment of the structure, multi-scale subspace system
identification method is now under development using a neural network technique based on the local mode shapes and
the cable tensions.
KEYWORDS: Sensors, Structural health monitoring, Smart sensors, Linear filtering, Digital filtering, Optical filters, Clocks, Data acquisition, Analog electronics, Amplifiers
The declining state of civil infrastructure has motivated researchers to seek effective methods for real-time structural
health monitoring (SHM). Decentralized computing and data aggregation employing smart sensors allow the
deployment of a dense array of sensors throughout a structure. The Imote2, developed by Intel, provides enhanced
computation and communication resources that allow demanding sensor network applications, such as SHM of civil
infrastructure, to be supported. This study explores the development of a versatile Imote2 sensor board with onboard
signal processing specifically designed for the demands of SHM applications. The components of the accelerometer
board have been carefully selected to allow for the low-noise and high resolution data acquisition that is necessary to
successfully implement SHM algorithms.
KEYWORDS: Smart sensors, Structural health monitoring, Sensors, Receivers, Data communications, Data processing, Head, Sensor networks, Clocks, Correlation function
The computational and wireless communication capabilities of smart sensors densely distributed over structures can
provide rich information for structural monitoring. While smart sensor technology has seen substantial advances during
recent years, interdisciplinary efforts to address issues in sensors, networks, and application specific algorithms are
needed to realize their potential. This paper first discusses each of these issues, and then reports on research that
combines the results to develop a structural health monitoring (SHM) system suitable for implementation on a network
of smart sensors. Experimental verification is provided using Intel's Imote2 smart sensors installed on a threedimensional
truss structure. The Imote2 is employed herein because it has the high computational and wireless
communication performance required for advanced SHM applications. This SHM system is then investigated from
sensing, network, and SHM algorithm perspectives.
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