With the advent and development of low-cost wireless structural health monitoring systems, the task of routinely
assessing the in-service condition of highway bridges through distributed sensor-based measurements is an increasingly
feasible component of bridge safety and management practice. Bridge monitoring encompasses placement of often a
limited number of distributed sensors across a relatively large and complex structural system. Consequently, the
selection of proper sensor locations is imperative to extraction of the most value from the recorded measurements. An
experimental investigation is presented wherein sensor placement on the superstructure girders or primary beams is
contrasted to the response measured on the surface of the bridge deck. The effect on the dataset richness, as evidenced
by the modal content, is presented and conclusions regarding optimal placement for this structure type are presented. To
affirm the plausibility of the observed responses and conclusions drawn, a finite element analysis is also performed on a
model developed from the as-built drawings.
With nearly one quarter of today's highway bridges rated as structurally deficient or functionally obsolete, it is ever more
important to quantify the safe level of performance using in-situ structural health monitoring techniques. This paper
discusses the experimental testing of a single simply supported span composite superstructure under various controlled
progressive damage cases. The single span is part of a three span bridge located in northern New York. Using strain
transducers mounted at the midspan and support locations, changes in load distribution factors and neutral axis locations
are calculated to detect changes in load shedding behavior and structural capacity of the bridge span as diaphragm
connections were severed and support conditions were altered to different levels. The results of the testing show that
changes in the bridge response can be tracked as damage is introduced to the superstructure. The measurements are used
to identify the diagnostic load testing parameters that can be used as structural health indicators that can in turn be used
to complement current inspection protocols. Furthermore, the measurements also help provide a basis for future
development of a performance index to be used in conjunction with existing condition rating measures for improved
bridge condition assessment.
Discussed in this paper is the deployment of a universal and low-cost dense wireless sensor system for structural
monitoring, load rating and condition assessment of bridges. The wireless sensor system developed is designed
specifically for diagnostic bridge monitoring, providing independent conditioning for both accelerometers and strain
transducers in addition to high-rate wireless data transmission. The system was field deployed on a three span simply
supported bridge superstructure, where strain and acceleration measurements were obtained simultaneously and in realtime
at critical locations under several loading conditions, providing reliable quantitative information as to the actual
performance level of the bridge. Monitoring was also conducted as the bridge was subjected to various controlled
damage scenarios on the final day of testing. Select cases of detected damage using strain and modal based analysis are
presented.
Wind power is a renewable source of energy that is quickly gaining acceptance by many.
Advanced sensor technologies have currently focused solely on improving wind turbine
rotor aerodynamics and increasing of the efficiency of the blade design and
concentration. Alternatively, potential improvements in wind plant efficiency may be
realized through reduction of reactionary losses of kinetic energy to the structural and
substructural systems supporting the turbine mechanics. Investigation of the complete
dynamic structural response of the wind plant is proposed using a large-scale, high-rate
wireless sensor network. The wireless network enables sensors to be placed across the
sizable structure, including the rotating blades, without consideration of cabling issues
and the economic burden associated with large spools of measurement cables. A large
array of multi-axis accelerometers is utilized to evaluate the modal properties of the
system as well as individual members and would enable long-term structural condition
monitoring of the wind turbine as well. Additionally, environmental parameters,
including wind speed, temperature, and humidity, are wirelessly collected for correlation.
Such a wireless system could be integrated with electrical monitoring sensors and
actuators and incorporated into a remote multi-turbine centralized plant monitoring and
control system.
KEYWORDS: Bridges, Sensors, Sensor networks, Damage detection, Analog electronics, Digital filtering, Cements, Linear filtering, Instrument modeling, System identification
The development of low-cost wireless sensor networks has resulted in resurgence in the development of ambient
vibration monitoring methods to assess the in-service condition of highway bridges. However, a reliable approach
towards assessing the health of an in-service bridge and identifying and localizing damage without a priori knowledge of
the vibration response history has yet to be formulated. A two-part study is in progress to evaluate and develop existing
and proposed damage detection schemes. The first phase utilizes a laboratory bridge model to investigate the vibration
response characteristics induced through introduction of changes to structural members, connections, and support
conditions. A second phase of the study will validate the damage detection methods developed from the laboratory
testing with progressive damage testing of an in-service highway bridge scheduled for replacement. The laboratory
bridge features a four meter span, one meter wide, steel frame with a steel and cement board deck composed of sheet
layers to regulate mass loading and simulate deck wear. Bolted connections and elastomeric bearings provide a means
for prescribing variable local stiffness and damping effects to the laboratory model. A wireless sensor network
consisting of fifty-six accelerometers accommodated by twenty-eight local nodes facilitates simultaneous, real-time and
high-rate acquisition of the vibrations throughout the bridge structure. Measurement redundancy is provided by an array
of wired linear displacement sensors as well as a scanning laser vibrometer. This paper presents the laboratory model
and damage scenarios, a brief description of the developed wireless sensor network platform, an overview of available
test and measurement instrumentation within the laboratory, and baseline measurements of dynamic response of the
laboratory bridge model.
With the increased demand placed on aging infrastructure, there is great interest in new condition assessment tools for
bridges. The routine deterioration that bridges undergo causes a loss in the intended performance that, if undetected or
unattended, can eventually lead to structural failure. Currently the primary method of bridge condition assessment
involves a qualitative bridge inspection routine based on visual observations. Discussed in this paper are methods of in-situ
quantitative bridge condition assessment using a dense wireless sensor array. At the core of the wireless system is
an integrated network which collects data from a variety of sensors in real-time and provides analysis, assessment and
decision-making tools. The advanced wireless sensor system, developed at Clarkson University for diagnostic bridge
monitoring, provides independent conditioning for both accelerometers and strain transducers with high-rate wireless
data transmission in a large-scale sensor network. Results from a field deployment of a dense wireless sensor network
on a bridge located in New York State are presented. The field deployment and testing aid to quantify the current bridge
response as well as demonstrate the ability of the system to perform bridge monitoring and condition assessment.
This study proposes the use of an innovative array of accelerometers for inertial tracking that is enabled through the
use of a non-Cartesian hyper-coordinate frame. Traditional inertial tracking technologies employ an array of
accelerometers and gyroscopes oriented in the orthogonal axes of the Cartesian coordinate system. The gyroscope
sensors are responsible for deducing the relative orientation of the instrumented object, while the accelerometer
measurements are double integrated to approximate the change in linear position relative to the local coordinate
frame. Since the position determination is dependent on the orientation derivation, the accuracy and stability of the
gyroscope sensors to a large extent determines the overall system performance. Consequently, high-performance
gyroscopes are generally used in inertial tracking systems, thereby driving the system cost significantly higher. The
proposed approach exclusively utilizes accelerometers in an innovative six axis orientation that, through linear
algebra, resolves linear and angular accelerations. The functional layout is processed in the context of hyperdimensional
coordinates which ultimately produce an inherent vector redundancy when resolved in the Cartesian
coordinate frame. This revised architecture is anticipated to alleviate many of the issues plaguing traditional inertial
tracking that stem from the stability of derived orientation from gyroscope readings. In addition, the exclusion of
gyroscopes from the design significantly reduces the unit cost of the system.
This paper additionally presents the development of a wireless system that incorporates the above described, unique
array of dedicated sensors for inertial tracking to provide accurate determination of position and orientation of the
sensor over time. The system permits access for additional channels of sensors for application specific monitoring
tasks. This allows sensing on objects in motion and in regions or flow patterns that cannot be easily instrumented
with traditional wired systems while maintaining knowledge of instantaneous position relative to the initial location.
To date, the majority of wireless sensor network deployments have enabled instrumentation of widespread sites,
such as civil structures, to alleviate the expense associated with the lengths of cable necessary to connect the sensors
to a central acquisition station. The alternative approach sought utilizes the unrestrained nature of the wireless
sensor to extend the use of this technology beyond static monitoring into applications in which the sensor node
travels across an area without a priori knowledge of the sensor motion. Documentation of the hardware
development of the proposed wireless sensing node as well as assessment of the system performance will be
provided.
KEYWORDS: Sensors, Bridges, Transceivers, Sensor networks, Digital filtering, Structural health monitoring, Transducers, Microcontrollers, Data communications, Analog electronics
The introduction and development of wireless sensor network technology has resulted in rapid growth within the
field of structural health monitoring (SHM), as the dramatic cable costs associated with instrumentation of large
civil structures is potentially alleviated. Traditionally, condition assessment of bridge structures is accomplished
through the use of either vibration measurements or strain sensing. One approach is through quantifying dynamic
characteristics and mode shapes developed through the use of relatively dense arrays of accelerometers. Another
widely utilized method of condition assessment is bridge load rating, which is enabled through the use of strain
sensors. The Wireless Sensor Solution (WSS) developed specifically for diagnostic bridge monitoring provides a
hybrid system that interfaces with both accelerometers and strain sensors to facilitate vibration-based bridge
evaluation as well as load rating and static analysis on a universal platform.
This paper presents the development and testing of a wireless bridge monitoring system designed within the
Laboratory for Intelligent Infrastructure and Transportation Technologies (LIITT) at Clarkson University. The
system interfaces with low-cost MEMS accelerometers using custom signal conditioning for amplification and
filtering tailored to the spectrum of typical bridge vibrations, specifically from ambient excitation. Additionally, a
signal conditioning and high resolution ADC interface is provided for strain gauge sensors. To permit compensation
for the influence of temperature, thermistor-based temperature sensing is also enabled. In addition to the hardware
description, this paper presents features of the software applications and host interface developed for flexible, user-friendly
in-network control of and acquisition from the sensor nodes. The architecture of the software radio protocol
is also discussed along with results of field deployments including relatively large-scale networks and throughput
rates sufficient for bridge monitoring.
Discussed in this paper is the implementation of a wireless sensor system for performance monitoring of bridges.
The advanced wireless sensor system, developed at Clarkson University's Laboratory for Intelligent Infrastructure
and Transportation Technologies (LIITT), allows for structural monitoring of bridges. A short-span integral-abutment
bridge located in New York State is instrumented with a wireless sensor system measuring acceleration,
and strain to monitor the behavior of the structure under various loading conditions including ambient,
environmental and traffic loading. Strain and acceleration measurements are recorded simultaneously and in real
time to validate various performance characteristics of the bridge, including load distribution along an interior
girder, as well as additional stiffness factors (end fixity and composite action of the beams and bridge deck), using
existing bridge load testing and condition evaluation guidelines used by the New York State Department of
Transportation (NYSDOT) and American Association of State Highway and Transportation Officials (AASHTO).
Additionally, acceleration measurements are used to extract the superstructure's first five natural frequencies and
corresponding mode shapes. Results are compared to a developed Finite Element Method (FEM) model based on
the bridge as built drawings.
The sensitivity and consistency of a damage index based on instantaneous phase values obtained through vibration measurements of a structure is investigated experimentally. An 'empirical mode decomposition' is performed to decompose structural vibrations into a small number of 'intrinsic mode functions' following the methodology generally known as the Hilbert-Huang Transform. Instantaneous phase information is derived through the Hilbert transform of intrinsic mode functions. The damage index is based on the idea that the difference in phase functions between any two points on a structure is altered if the structure is damaged. Experimental investigations are performed on a beam structure with varying excitations (white noise signals), damage levels, and damage locations. The damage index shows generally consistent results, but its sensitivity to damages needs improvements for practical applications.
Presented in this paper is the environmental testing of Wireless Intelligent Sensor and Actuator Network (WISAN) currently under development at Clarkson University for the use of long-term structural health monitoring of civil infrastructure. The wireless sensor nodes will undergo controlled mechanical vibration and environmental testing in the laboratory. A temperature chamber will be used to perform temperature cycle tests on the sensor nodes. The temperature chamber will also houses a small shaker capable of introducing mechanical loading under the controlled temperature
cycle tests. At low temperatures, the resistance of the electronics processing and storage characteristics will be studied. Also, the testing will look at volume expansion and degradation of characteristics due to freezing, degradation of functions and performance, and mechanical characteristics caused by contraction. At high temperatures, temperature-related changes in sensor nodes due to excessively high temperatures will be investigated. Also studied will be the effects of temperature cycles, including the thermal stresses induced in the nodes and housing and the distortion caused due to expansion and contraction, fatigue, cracks, and changes in electrical characteristics due to mechanical displacement. And finally, mechanical vibration loading will be introduced to the WISAN sensor nodes. Mechanical looseness, fatigue destruction, wire disconnection, damage due to harmonic vibration, defective socket contact, joint
wear, destruction due to harmonics, lead breakage, occurrence of noise and abnormal vibration, cracking will be monitored. The eventual goal of the tests is to verify WISAN's performance under anticipated field conditions in which the sensors will be deployed.
Lost Foam Casting (LFC) enables the production of complex castings while offering the advantages of consolidation of components, reduced machining, and recirculation of the casting mold material. In the process, a replica of the desired product is produced of blown polystyrene, coated in refractory slurry, and cast in a dense, unbonded sand mold. In order for the unbonded sand mold to fill into pattern holes and to provide sufficient confining force to prevent the advancing molten front from penetrating beyond the mold boundaries, the sand mold is produced by an overhead raining and flask vibration schedule that encourages fluidization and subsequent densification. The amplitude, frequency, and duration of the flask vibration as well as the rate of sand filling are critical parameters in achieving quality castings. Currently, many foundries use an often-lengthy trial-and-error process for determining an acceptable raining and vibration schedule for each specific mold and rely heavily on simple measurements and operator experience to control the mold making process on the foundry line. This study focuses on developing a wireless sensor network of accelerometers to monitor vibrational characteristics of the casting flask during the mold making stage of LFC. Transformations in the vibrational characteristics of the flask can provide a "signature" for indicating the condition of the unbonded sand mold. Additionally, the wireless nature of the sensor nodes enables the technology to travel across the foundry floor during the casting cycle eliminating the necessity of routine placement and setup.
KEYWORDS: Sensors, Structural health monitoring, Data acquisition, Sensor networks, Actuators, Bridges, Microcontrollers, Intelligent sensors, Temperature metrology, Data storage
This paper presents Wireless Intelligent Sensor and Actuator Network (WISAN) as a scalable wireless platform for
structural health monitoring. Design of WISAN targeted key issues arising in applications of structural health
monitoring. First, scalability of system from a few sensors to hundreds of sensors is provided through hierarchical
cluster-tree network architecture. Special consideration is given to reliable delivery of wireless data in real-world
conditions. Second, a possibility of autonomous operation of sensor nodes from energy harvesters is ensured through
extremely low power consumption in operational and standby modes of operation. Third, all the sensors and actuators
operate in globally synchronized time on the order of a few microseconds through utilization of the beaconing
mechanism of IEEE802.15.4 standard. Fourth, depending on application requirements, the system is capable of
delivering real-time streams of sensor data or performing on-sensor storage and/or processing with result transmission.
Finally, a capability to work with heterogeneous arrays of sensors and actuators is ensured by a variety of analog and
digital interfaces. Results of experimental tests validate the performance of the WISAN.
The lost foam casting (LFC), or expendable pattern casting, process is employed worldwide in foundries as an efficient casting technology that offers the advantages of consolidation of components, reduced machining, and recirculation of casting mold material. Currently, many foundries develop a schedule of sand raining flow rates and flask excitation accelerations for each specific pattern through an often-lengthy trial and error procedure. During casting, a single flask acceleration measurement is typically the only measurement by which the sand compaction is monitored. The current research focuses on developing an array of measurement tools to be used in measuring parameters critical to the sand compaction stage of the lost foam casting process to aid in the development of filling and vibration schedules as well as to provide additional inline measurements during foundry operation. In particular, the study focuses on the use of minimally intrusive transducers placed inline to provide direct feedback that can be then used in both passive and active process control.
Lost Foam Casting (LFC) enables metal casters to produce complex parts by making foam patterns having the same geometry as the desired finished parts. Among the greatest strengths of LFC process is that it allows designers to consolidate parts, reduce machining and minimize assembly operations. One of the key steps in the LFC process takes place in the compaction box, where the foam pattern is suspended in a steel container that is vibrated while sand is added to surround the pattern. The sand provides the mechanical support to the pattern as molten metal is poured into the mold. Discussed in this paper will be the development of an advanced sensor array for the measurement and control of the sand compaction stage. Compaction of the sand is key in controlling casting distortion and is instrumental in the efficiency rating of the LFC process. Too much compaction can cause the foam part to distort or even get crushed. Too little compaction can lead to a defective final product due to inadequate support of the foam part or lack of sand flow into small cavities in the foam part. To understand and control the behavior of the sand compaction stage, the key parameters that must first be measured are: (1) Energy imparted on the compaction box, sand and foam part, (2) compaction of the sand in the casting box, and (3) distortion of the foam part. The sensor array is to be placed inline in order to give direct feedback that can then be used in both passive and active process control.
Life cycle monitoring of civil infrastructure such as bridges and buildings is critical to the long-term operational cost and safety of aging structures. The widespread use of Structural Health Monitoring (SHM) systems is limited due to unavailability of specialized data acquisition equipment, high cost of generic equipment, and absence of fully automatic decision support systems.
The goals of the presented project include: first, design of a Wireless Intelligent Sensor and Actuator Network (WISAN) and creation of an inexpensive set of instrumentation for the tasks of structural health monitoring; second, development of a SHM method, which is suitable for autonomous structural health monitoring.
The design of the wireless sensor network is aimed at applications of structural health monitoring, addressing the issues of achieving a low cost per sensor, higher reliability, sources of energy for the network nodes, energy-efficient distribution of the computational load, security and coexistence in the ISM radio bands. The practical applicability of the sensor network is increased through utilization of computational intelligence and support of signal generation capabilities.
The automated SHM method is based on the method of modal strain energy, though other SHM methods will be supported as well. The automation tasks include automation of the modal identification through ambient vibrations, classification of the acquired mode shapes, and automatic evaluation of the structural health.
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