The use of strain gauges is foundational to structural health monitoring, allowing infrastructure to continuously observe strain, infer stress, and potentially detect fatigue/fracture cracks. However, traditional strain gauges have drawbacks. In addition to being costly, a single-element strain gauge will only detect strain in a single direction and must be mounted on smooth surfaces to ensure good adhesion. Soft Elastomeric Capacitors (SECs) have been proposed as a low-cost alternative to traditional strain gauges while allowing for a broader range of applications. They are flexible and can be modeled with different dimensions based on the monitored structure. Each SEC consists of three layers; the two outer layers act as electrodes and are made of a styrene-ethylene-butylene-styrene polymer in a matrix with carbon black. The inner (dielectric) layer comprises titanium oxide in a matrix with SEBS. The use of the SECs is not limited by the geometry of the surface being monitored, and it can, therefore, be adhered to a variety of surfaces as its flexibility allows it to conform to the irregularity and complexity of the monitored structure. The change experienced by a structure will correlate directly to the change in capacitance observed across the sensor, which can be used to predict the monitored structure’s state. While SECs have been studied for applications on various materials, experiments have been limited to adhering the sensor to smooth surfaces. However, concrete structures have various surface finishes that are not uniform, often deriving from an architect’s aesthetic desire. This work tests a corrugated SEC through compression tests on concrete samples with different surface finishing to investigate the effect of surface finishing on the SEC-measured strain. Each concrete sample is subjected to loading by a dynamic testing system, and the data collected from the SEC are compared to off-the-shelf resistive strain gauges. The results show that the performance of the cSEC on the different surfaces is not hindered by different concrete finishes, where a high signal-to-noise ratio of 21 dB and low mean absolute error of 22 μϵ is seen on the concrete specimen with a rough concrete surface. The strain metrics and surface effect on SEC performance are discussed.
Surface strain sensors, such as linear variable differential transformers, fiber Bragg gratings, and resistive strain gauges, have seen significant use for monitoring concrete infrastructure. However, spatial monitoring of concrete structures using these sensor systems is limited by challenges in the surface coverage provided by a specific sensor or issues related to mounting and maintaining numerous mechanical sensors on the structure. A potential solution to this challenge is the deployment of large-area electronics in the form of a sensing skin to provide complete coverage of a monitored area while being simple to apply and maintain. Along this line of effort, networks constituted of soft elastomeric capacitors have been deployed to monitor strain on steel and composite structures. However, using soft elastomeric capacitors on concrete surfaces has been challenging due to the electrical coupling between the sensors and concrete, which amplifies transduced strain signals obtained from the soft elastomeric capacitors. In this work, the authors investigate the isolation of the soft elastomeric capacitors from the concrete by extending the styrene-block-ethylene-co-butylene-block-styrene matrix of the soft elastomeric capacitors to include a decoupling layer between the electrode and the concrete. Experimental investigations are carried out on concrete specimens for which the soft elastomeric capacitor is adhered to with a thin layer of off-the-shelf epoxy and then loaded on the dynamic testing system to monitor strain provoked on the concrete samples. The results presented here demonstrate the viability of the electrically isolated soft elastomeric capacitors for monitoring strain on concrete structures. Initial comparisons between un-isolated and electrically isolated soft elastomeric capacitors showed that the nominal capacitance of the soft elastomeric capacitor is significantly lowered by adding an isolation layer of SEBS. Furthermore, strain results for the soft elastomeric capacitors are compared to ones from a resistive strain gauge and digital image correlation. The data obtained is significant for modifying soft elastomeric capacitors with the anticipation for future use on concrete structures.
The effect of low energy impacts can seriously impair the operational life span of composites in the field. These low-energy impacts can induce a permanent loss in the toughness of the composite without any visible indication of the material’s compromise. The detection of this damage utilizing nondestructive inspection requires dense measurements over much of the surface and has been traditionally achieved by removing the part from service for advanced imaging techniques. While these methods can accurately diagnose the damage inflicted internally by the impacts, they accrue non-trivial opportunity costs while the structure is inspected. To enable the capabilities of in-service monitoring of the composite, the novel soft elastomeric capacitor was investigated as a sensing solution. The sensor is made of three layers comprised of a styrene-ethylene-butylene-styrene (SEBS) matrix, a commercially available elastomer. These layers consist of a titania filled center layer that forms the dielectric of the capacitor and two highly conductive outer layers doped with carbon black. This simple formation allows for a capacitor that has extremely robust mechanical properties. The soft elastomeric capacitor functions by taking up deformations on the surface of the composite that is transduced into a measurable change in capacitance. This study provides an electro-mechanical model for impact damage and experimentally investigates the efficacy of these sensors for use in damage detection given their promising characteristics; that being that the sensor geometry can be arbitrarily large allowing for much fewer sensors than traditional sensor networks employed for this task at a much lower cost than installing traditional in-situ sensing solutions. To investigate these properties a set of impact trials were undertaken on a drop tower using small samples of glass fiber reinforced plate, of random orient and short fiber, with a soft elastomeric capacitor mounted directly opposite the impact site. The impactor head was only allowed one contact with the sample before being intercepted. The testing range for the samples ranged from well below the yield strength of the glass fiber reinforced plate to the ultimate strength of the plate. Experimental results reported a square root relation between the impact energy given to the plate when inducing plastic deformations and the sensor’s measured change in capacitance.
Rolling element bearings perform an essential role in most rotating machinery. Bearing fault diagnosis and prognosis can detect degradation to bearing performance, preventing the costs of unexpeceted system failure. Acoustic Emission (AE) introduces high sensitivity, early and rapid detection of cracking, and real time monitoring that can provide an alarm once cracking is noticed. This paper discusses the nondestructive monitoring of crack growth in rolling element bearings in a marine environment and the determination of acoustic emission parameters which indicate crack initiation and propagation. The paper’s intellectual merit lies in the signal alarm developed from an AE data pattern recognition method, and the specially made rotating machinery test bed that simulates a bearing used on board a ship. Four rolling element bearings were tested in the test bed at various loads and rotation cycles. All AE data was clustered using k-means unsupervised method, and the lowest correlated features were selected for pattern recognition. Useful AE parameters for classifying crack initiation and propagation were determined. Acoustic emission proved to be suitable for remote monitoring of bearing degradation. With the use of signal alarms based upon the clustering method and parameters discussed, one can be notified when a crack has been initiated and is propagating. This will allow the user to avoid a costly unexpected system failure and plan to perform a less costly bearing replacement.
Due to the heterogeneous nature of the cement-based materials, the ultrasonic waves in concrete exhibit highly scattering and attenuation, leading to the difficulty of concrete damaged detection. This paper presents a dual mode ultrasonic array imaging methodology that can map damage using Rayleigh surface waves and permanently installed piezoelectric sensors. The dual mode sensing integrates passive acoustic emission and active ultrasonic wave inspection. When a crack is developing, acoustic emission (AE) occurs and the disturbance can propagate outwards along the structure surface. A novel AE source imaging algorithm has been developed to detect and locate the AE source. Once the AE source is located, the sensor array switches to its active mode. For active sensing, one sensor in the array is used to generate Rayleigh wave for interrogation, while all the others are used as the wave receivers. All the sensory data are processed by the active ultrasonic array imaging algorithm. The proof-of-concept testing was performed on a grout specimen with representative dimensions. The passive array imaging algorithm was able to locate the AE source simulated by pencil lead break while active sensing imaging was able to detect the damage simulated by a hole. The duel mode imaging method is promising and economically beneficial for solving a key source localization problem in damage detection on large concrete structures.
Acoustic emission (AE) has been recognized for its unique capabilities as an NDT method. However, there is untapped
potential for the practical application of AE to structural health monitoring and prognosis. As part of the development of a wireless sensor network for structural bridge health monitoring, this study aims to provide a framework for the estimation of fatigue damage and remaining life of steel bridge components through AE monitoring. Fourteen compact tension (CT) specimens and nine cruciform fillet welded joints were used in AE monitored fatigue tests to investigate the correlation of AE features with crack growth in base materials and weldments. The material (structural steel A572 Grade 50) and the welding procedures are representative of those used in actual bridge construction. Based on the balance between AE signal energy and the energy release due to crack growth, deterministic models are presented to predict crack extension and remaining fatigue life for stable and unstable crack stages. The effect of weld length and fatigue load ratio on the AE activity is evaluated. The presence of noise is inevitable in the application of AE monitoring. The efficiency of data filtering and reduction algorithms is key to minimize the power and data storage demand of the wireless sensing system. AE data filtering protocols based on load pattern, source location, waveform feature analysis, and pattern recognition are proposed to minimize noise-induced AE and false indications due to wave reflections.
The depassivation and corrosion of bonded prestressing steel strands in concrete bridge members may lead to major
damage or collapse before visual inspections uncover evident signs of damage, and well before the end of the design life. Recognizing corrosion in its early stage is desirable to plan and prioritize remediation strategies. The Acoustic Emission (AE) technique is a rational means to develop structural health monitoring and prognosis systems for the early detection and location of corrosion in concrete. Compelling features are the sensitivity to events related to micro- and macrodamage, non-intrusiveness, and suitability for remote and wireless applications. There is little understanding of the correlation between AE and the morphology and extent of early damage on the steel surface. In this paper, the evidence collected from prestressed concrete (PC) specimens that are exposed to salt water is discussed vis-à-vis AE data from continuous monitoring. The specimens consist of PC strips that are subjected to wet/dry salt water cycles, representing portions of bridge piles that are exposed to tidal action. Evidence collected from the specimens includes: (a) values of half-cell potential and linear polarization resistance to recognize active corrosion in its early stage; and (b) scanning electron microscopy micrographs of steel areas from two specimens that were decommissioned once the electrochemical measurements indicated a high probability of active corrosion. These results are used to evaluate the AE activity resulting from early corrosion.
KEYWORDS: Sensors, Bridges, Active remote sensing, Transducers, Inspection, Wave propagation, Signal detection, Signal to noise ratio, Acoustic emission, Aluminum
Current routine inspection practices for bridge health monitoring are not sufficient for timely identification of areas of concern or are incapable of providing enough information to bridge owners to make valuable informed decisions for maintenance prioritization. Continuous monitoring is needed for long-term evaluation from an integrated sensing system that would act as a monitoring and early warning alarm system that is able to effectively communicate the information from the bridge directly to the bridge owners for potential and immediate response. Due to the variety of deterioration sources and locations, currently there is no single NDE method that can detect and address the potential sources globally. To address the need of this urgent highway bridge health monitoring, a joint venture research has been initiated under the NIST Technology Innovation Program by incorporating a novel and promising sensing approach based on piezoelectricity together with energy harvesting to reduce the dramatic uncertainty inherent into any inspection and maintenance plan. One approach to damage detection and classification has been focused on the use of piezoelectric sensors (PES) at both active (ultrasonic NDE) mode and passive (acoustic emission) mode on steel bridge. The acoustic emission (AE) method has been shown the best potential for global bridge health monitoring while active sensing will provide additional quantification process. Two types of the PES have been studied to provide fundamental principles for their applications to steel bridges. Extensive laboratory investigation was performed supported by a theoretical modeling analysis.
This paper discusses the development status of a self-powered wireless sensor node for steel and concrete bridges
monitoring and prognosis. By the end of the third year in this four-year cross-disciplinary project, the 4-channel acoustic
emission wireless node, developed by Mistras Group Inc, has already been deployed in concrete structures by the
University of Miami. Also, extensive testing is underway with the node powered by structural vibration and wind energy
harvesting modules developed by Virginia Tech. The development of diagnosis tools and models for bridge prognosis,
which will be discussed in the paper, continues and the diagnosis tools are expected to be programmed in the node's
AVR during the 4th year of the project. The impact of this development extends beyond the area of bridge health
monitoring into several fields, such as offshore oil platforms, composite components on military ships and race boats,
combat deployable bridges and wind turbine blades. Some of these applications will also be discussed. This project was
awarded to a joint venture formed by Mistras Group Inc, Virginia Tech, University of South Carolina and University of
Miami by the National Institute of Standards and Technology through its Technology Innovation Program Grant
#70NANB9H007.
The corrosion of reinforced concrete structures is a major issue from both a structural safety and maintenance
management point of view. Early detection of the internal degradation process provides the owner with sufficient options
to develop a plan of action. An accelerated corrosion test was conducted in a small scale concrete specimen reinforced
with a 0.5 inch (13 mm) diameter prestressing strand to investigate the correlation between corrosion rate and acoustic
emission (AE). Corrosion was accelerated in the laboratory by supplying anodic current via a rectifier while
continuously monitoring acoustic emission activity. Results were correlated with traditional electrochemical techniques
such as half-cell potential and linear polarization. The location of the active corrosion activity was found through a
location algorithm based on time of flight of the stress waves. Intensity analysis was used to plot the relative significance
of the damage states present in the specimen and a preliminary grading chart is presented. Results indicate that AE may
be a useful non-intrusive technique for the detection and quantification of corrosion damage.
KEYWORDS: Transducers, Sensors, Bridges, Active remote sensing, Wave propagation, Passive remote sensing, Semiconducting wafers, Active sensors, Signal to noise ratio, Signal detection
Monitoring of fatigue cracking in bridges using a combined passive and active scheme has
been approached by the authors. Passive Acoustic Emission (AE) monitoring has shown to be able to
detect crack growth behavior by picking up the stress waves resulting from the breathing of cracks while
active ultrasonic pulsing can quantitatively assess structural integrity by sensing out an interrogating
pulse and receive the structural reflections from the discontinuity. In this paper, we present a
comparative study of active and passive sensing with two types of transducers: (a) AE transducers, and
(b) embeddable piezoelectric wafer active sensors (PWAS). The study was performed experimentally on
steel plates. Both pristine and damaged (notched) conditions were considered. For active sensing, pitchcatch
configuration was examined in which one transducer was the transmitter and another transducer
acted as the receiver. The ping signal was generated by the AE hardware/software package AEwin. For
passive sensing, 0.5-mm lead breaks were executed both on top and on the edge of the plate. The
comparative nature of the study was achieved by having the AE and PWAS transducers placed on the
same location but on the opposite sides of the plate. The paper presents the main findings of this study
in terms of (a) signal strength; (b) signal-to-noise (S/N) ratio; (c) waveform clarity; (d) waveform
Fourier spectrum contents and bandwidth; (e) capability to detect and localize AE source; (f) capability
to detect and localize damage. The paper performs a critical discussion of the two sensing
methodologies, conventional AE transducers vs. PWAS transducers.
Signal identification including noise filtering and reduction of acquired signals is needed to achieve efficient and
accurate data interpretation for remote acoustic emission (AE) monitoring of in-service steel bridges. Noise filtering may
ensure that genuine hits from crack growth are involved in the estimation of fatigue damage and remaining fatigue life.
Reduction of the data quantity is desirable for the sensing system to conserve energy in the data transmission and
processing procedures. Identification and categorization of acquired signals is a promising approach to effectively filter
and reduce AE data in the application of bridge monitoring. In this study an investigation on waveform features (time
domain and frequency domain) and relevant filters is carried out using the results from AE monitored fatigue tests. It is
verified that duration-amplitude (D-A) filters are effective to discriminate against noise for results of steel fatigue tests.
The study is helpful to find an appropriate AE data filtering protocol for field implementations.
Early detection of corrosion can help reduce the cost of maintenance and extend the service life of structures.
Acoustic emission (AE) sensing has proven to be a promising method for early detection of corrosion in reinforced
concrete members. A test program is presented composed of four medium-scale prestressed concrete T-beams.
Three of the beams have a length of 16 ft. 4 in. (4.98 m), and one is 9 ft. 8 in. (2.95 m). In order to corrode the
specimens a 3% NaCl solution was prepared, which is representative of sea salt concentration. The beams were
subjected to wet-dry cycles to accelerate the corrosion process. Two of the specimens were pre-cracked prior to
conditioning in order to examine the effect of crack presence. AE data was recorded continuously while half-cell
potential measurements and corrosion rate by Linear Polarization Resistance (LPR) were measured daily. Corrosion
current was also being acquired constantly to monitor any change in the concrete resistivity. Results indicate that the
onset of corrosion may be identified using AE features, and were corroborated with measurements obtained from
electrochemical techniques. Corroded areas were located using source triangulation. The results indicate that
cracked specimens showed corrosion activity prior to un-cracked specimens and experienced higher corrosion rates.
The level of corrosion was determined using corrosion rate results. Intensity analysis was used to link the corrosion
rate and level to AE data.
KEYWORDS: Acoustic emission, Electronic filtering, Mechanics, Sensors, Data modeling, Transducers, Data acquisition, Linear filtering, Monte Carlo methods, Digital filtering
This paper compares six different filtering protocols used in Acoustic Emission (AE) monitoring of fatigue crack
growth. The filtering protocols are combination of three different filtering techniques which are based on Swansong-like
filters and load filters. The filters are compared deterministically and probabilistically. The deterministic
comparison is based on the coefficient of determination of the resulting AE data, while the probabilistic comparison
is based on the quantification of the uncertainty of the different filtering protocols. The uncertainty of the filtering
protocols is quantified by calculating the entropy of the probability distribution of some AE and fracture mechanics
parameters for the given filtering protocol. The methodology is useful in cases where several filtering protocols are
available and there is no reason to choose one over the others. Acoustic Emission data from a compact tension
specimen tested under cyclic load is used for the comparison.
This paper presents the most recent advances in the development of a self powered wireless sensor network for steel and
concrete bridges monitoring and prognosis. This five-year cross-disciplinary project includes development and
deployment of a 4-channel acoustic emission wireless node powered by structural vibration and wind energy harvesting
modules. In order to accomplish this ambitious goal, the project includes a series of tasks that encompassed a variety of
developments such as ultra low power AE systems, energy harvester hardware and especial sensors for passive and
active acoustic wave detection. Key studies on acoustic emission produced by corrosion on reinforced concrete and by
crack propagation on steel components to develop diagnosis tools and models for bridge prognosis are also a part of the
project activities. It is important to mention that the impact of this project extends beyond the area of bridge health
monitoring. Several wireless prototype nodes have been already requested for applications on offshore oil platforms,
composite ships, combat deployable bridges and wind turbines. This project was awarded to a joint venture formed by
Mistras Group Inc, Virginia Tech, University of South Carolina and University of Miami and is sponsored through the
NIST-TIP Grant #70NANB9H007.
Acoustic emission (AE) monitoring is desirable to nondestructively detect fatigue damage in steel bridges. Investigations
of the relationship between AE signals and crack growth behavior are of paramount importance prior to the widespread
application of passive piezoelectric sensing for monitoring of fatigue crack propagation in steel bridges. Tests have been
performed to detect AE from fatigue cracks in A572G50 steel. Noise induced AE signals were filtered based on friction
emission tests, loading pattern, and a combined approach involving Swansong II filters and investigation of waveforms.
The filtering methods based on friction emission tests and load pattern are of interest to the field evaluation using sparse
datasets. The combined approach is suitable for data filtering and interpretation of actual field tests. The pattern
recognition program NOESIS (Envirocoustics) was utilized for the evaluation of AE data quality. AE parameters are
associated with crack length, crack growth rate, maximum stress intensity and stress intensity range. It is shown that AE
hits, counts, absolute energy, and signal strength are able to provide warnings at the critical cracking level where
cracking progresses from stage II (stable propagation) to stage III (unstable propagation which may result in failure).
Absolute energy rate and signal strength rate may be better than count rate to assess the remaining fatigue life of inservice
steel bridges.
The US transportation infrastructure has been receiving intensive public and private attention in recent years. The
Federal Highway Administration estimates that 42 percent of the nearly 600,000 bridges in the Unites States are in need
of structural or functional rehabilitation1. Corrosion of reinforcement steel is the main durability issue for reinforced and
prestressed concrete structures, especially in coastal areas and in regions where de-icing salts are regularly used.
Acoustic Emission (AE) has proved to be a promising method for detecting corrosion in steel reinforced and prestressed
concrete members. This type of non-destructive test method primarily measures the magnitude of energy released within
a material when physically strained. The expansive ferrous byproducts resulting from corrosion induce pressure at the
steel-concrete interface, producing longitudinal and radial microcracks that can be detected by AE sensors. In the
experimental study presented herein, concrete block specimens with embedded steel reinforcing bars and strands were
tested under accelerated corrosion to relate the AE activity with the onset and propagation stages of corrosion. AE data
along with half cell potential measurements and galvanic current were recorded to examine the deterioration process.
Finally, the steel strands and bars were removed from the specimens, cleaned and weighed. The results were compared
vis-à-vis Faraday's law to correlate AE measurements with degree of corrosion in each block.
KEYWORDS: Data modeling, Acoustic emission, Nondestructive evaluation, Composites, Aerospace engineering, Homeland security, Current controlled current source, Bayesian inference, Monte Carlo methods
Acoustic emission (AE) is generated when cracks develop and it is used as an indicator of the current state of
damage in structural elements. Algorithms that use AE data to predict the state of a structural element are still in
their research stages because the relationship between crack length and AE activity is not well understood. The
process of trying to predict the future stage of a crack based on AE data is usually performed by an expert, and
requires significant experience. This paper proposes a new strategy for the use of AE data for structural prognosis.
A probabilistic model is used to predict AE data. An expert can analyze this data to draw conclusions about the
health of the structural member. The goal is to aid the analyst by providing an estimation of the AE activity in the
future. The methodology provides the cumulative signal strength at a future number of cycles, assuming the loading
and boundary conditions hold. The methodology uses a relationship between the rate of change of the cumulative
absolute energy of the AE with respect to the number of cycles and the stress intensity range. A third order
polynomial equation that describes the stress intensity range as function of the AE data is proposed. The variables
to be updated are treated as random and their joint probability distribution is computed using Bayesian inference.
Markov Chain Monte Carlo (MCMC) is used to forecast the cumulative signal strength at some number of cycles in
the future. The methodology is tested using a compact test specimen tested in structures lab at the University of
South Carolina.
South Carolina is one of the most seismically active states in the eastern U.S. Due to this high level of seismic activity,
structural health monitoring is important to ensure a high level of confidence in the state's infrastructure. The University
of South Carolina (U.SC) is currently studying the behavior of prestressed pile to bent-cap connections that are typical of
construction used in the state. Bent caps are generally constructed with multiple piles. In these tests single pile specimens
were created for both interior and exterior piles. Interior specimens were subjected to a constant compressive load while
exterior specimens experienced both compressive and tensile loads. Acoustic Emission (AE) sensing was utilized on fullscale
test specimens to investigate the feasibility of detecting and characterizing damage in these connections during a
seismic event. Seven full-scale prestressed concrete piles have been embedded into cast-in-place (CIP) reinforced
concrete bent caps and tested under reverse cyclic loading. AE data has been gathered with eight strategically placed AE
sensors. Preliminary analysis of the data indicates that AE is promising method with respect to the detection of damage
prior to detection by visual observation. AE activity is used to detect both the onset and location of cracking and to
characterize the extent of damage at later stages of degradation. One focus of the work is to minimize the amount of AE
data recorded for the development of wireless systems having low power consumption.
Acoustic Emission (AE) sensing was employed to assess the rate of corrosion of steel strands in small scale concrete
block specimens. The corrosion process was accelerated in a laboratory environment using a potentiostat to supply a
constant potential difference with a 3% NaCl solution as the electrolyte. The embedded prestressing steel strand served
as the anode, and a copper plate served as the cathode. Corrosion rate, half-cell potential measurements, and AE activity
were recorded continuously throughout each test and examined to assess the development of corrosion and its rate. At
the end of each test the steel strands were cleaned and re-weighed to determine the mass loss and evaluate it vis-á-vis the
AE data. The initiation and propagation phases of corrosion were correlated with the percentage mass loss of steel and
the acquired AE signals. Results indicate that AE monitoring may be a useful aid in the detection and differentiation of
the steel deterioration phases, and estimation of the locations of corroded areas.
KEYWORDS: Acoustic emission, Data modeling, Active remote sensing, Bridges, Transducers, Sensors, Lead, Signal to noise ratio, Genetic algorithms, Signal detection
Monitoring of fatigue cracks in steel bridges is of interest to bridge owners and agencies. Monitoring of fatigue cracks
has been attempted with acoustic emission using either resonant or broadband sensors. One drawback of passive sensing
is that the data is limited to that caused by growing cracks. In this work, passive emission was complemented with
active sensing (piezoelectric wafer active sensors) for enhanced detection capabilities. Passive and active sensing
methods were described for fatigue crack monitoring on specialized compact tension specimens. The characteristics of
acoustic emission were obtained to understand the correlation of acoustic emission behavior and crack growth. Crack
and noise induced signals were interpreted through Swansong II Filter and waveform-based approaches, which are
appropriate for data interpretation of field tests. Upon detection of crack extension, active sensing was activated to
measure the crack size. Model updating techniques were employed to minimize the difference between the numerical
results and experimental data. The long term objective of this research is to develop an in-service prognostic system to monitor structural health and to assess the remaining fatigue life.
Piezoelectric wafer active sensors (PWAS) are well known for its dual capabilities in structural health
monitoring, acting as either actuators or sensors. Due to the variety of deterioration sources and locations
of bridge defects, there is currently no single method that can detect and address the potential sources
globally. In our research, our use of the PWAS based sensing has the novelty of implementing both
passive (as acoustic emission) and active (as ultrasonic transducers) sensing with a single PWAS network.
The combined schematic is using acoustic emission to detect the presence of fatigue cracks in steel
bridges in their early stage since methods such as ultrasonics are unable to quantify the initial condition of
crack growth since most of the fatigue life for these details is consumed while the fatigue crack is too
small to be detected. Hence, combing acoustic emission with ultrasonic active sensing will strengthen the
damage detection process. The integration of passive acoustic emission detection with active sensing will
be a technological leap forward from the current practice of periodic and subjective visual inspection, and
bridge management based primarily on history of past performance.
In this study, extensive laboratory investigation is performed supported by theoretical modeling
analysis. A demonstration system will be presented to show how piezoelectric wafer active sensor is used
for acoustic emission. Specimens representing complex structures are tested. The results will also be
compared with traditional acoustic emission transducers to identify the application barriers.
rent routine inspection practices for bridge health monitoring are not sufficient for the timely
identification of areas of concern or to provide enough information to bridge owners to make informed
decisions for maintenance prioritization. Continuous monitoring is needed for long term evaluation from
an integrated sensing system that would act as a monitoring and early warning alarm system and be able
to communicate the information from the bridge directly to the bridge owners for potential and immediate
action. To address this urgent highway bridge health monitoring need, a joint venture research has been
initiated by incorporating novel and promising sensing approach based on piezoelectricity together with
energy harvesting to reduce the dramatic uncertainty inherent into any inspection and maintenance plan.
In the system, the damage detection and classification is focused on the use of piezoelectric wafer active
sensors (PWAS) at both active (Lamb wave interrogation) mode and passive (acoustic emission) mode on
steel bridge. For efficient energy usage, the active mode will be triggered when acoustic emission caused
by the structural change is detected. In the active sensing mode, computed array imaging will be used to
detect the presence of crack and to track its growth. To further quantify the crack growth, damage physics
based damage indicator will be defined and used to trace the crack growth as well.
Due to the state of aging civil infrastructure systems structural health monitoring and nondestructive evaluation have
received increased attention recently. Events related to bridge collapses in Pennsylvania (partial) and Minnesota
(catastrophic) combined with the levee failures in Louisiana have justifiably drawn the attention of the policy makers
and the public at large. Therefore it appears likely that both monitoring efforts of existing systems and the development
of more resilient systems will be increased. In the case of civil structures (bridges, dams, levees, and buildings) the most
common type of sensors used are strain gages and accelerometers. While these sensors can be useful if used correctly
they are limited in the types of data that can be gathered and are not well-suited for many applications. In contrast
acoustic emission sensors are very rarely used for civil applications but can in fact provide useful information either as a
stand-alone data type or to supplement the data gathered from other sensors. This paper describes several case studies
where acoustic emission has been successfully used in civil infrastructure applications and summarizes both the
advantages and challenges that are inherent in the method for such applications.
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