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Kirikera, Goutham RaghavendraA Structural Neural System for Health Monitoring of Structures
PhD, University of Cincinnati, 2006, Engineering : Mechanical Engineering

A method for structural health monitoring of large structures based on detecting acoustic emissions produced by damage was developed for this dissertation. The advantage of sensing acoustic emissions is that small damage can be detected in structures built with complex geometry and anisotropic materials. A longstanding limitation of the acoustic emission method is that a large number of bulky sensors are required to monitor cracks that can form at any location on a complex structure. The sensors and data acquisition system are also required to work at a high sampling rate because the frequencies of acoustic waves propagating in the structure due to damage are on the order of hundreds of kHz. To overcome the difficulties with using the acoustic emission method, a very elegant and powerful technique that many researchers have either missed or avoided is presented in this dissertation. The new sensing technique is called a structural neural system. The technique was difficult to develop, and required using electronic circuits to mimic the architecture of the biological neural system. In developing the technique, it was also necessary to recognize the strong linkage between fracture mechanics and fatigue damage detection.

The structural neural system developed uses continuous (multi-node) sensors to mimic dendrites, receptors, and the axon which perform sensing in the biological neural system. Analog electronics were then developed to mimic the thresholding and firing functions of the soma (cell body) in the neural system. The end result is a structural neural system that tremendously reduces the complexity and number of data acquisition channels needed to monitor acoustic emissions and detect damage in structures that have high feature density. Simulation and laboratory testing of a prototype of the structural neural system showed that the structural neural system is sensitive to small damage and practical to use on large structures. A field test was also performed in which a simple two-channel four neuron prototype structural neural system was installed on a 9 meter long wind turbine blade at the National Renewable Energy Laboratory in Golden, Colorado. The blade was loaded to failure in a quasi-static proof test. The structural neural system, using only two channels of data acquisition, identified where damage started during the testing, and monitored the growth of damage at five locations on the blade. The structural neural system detected damage well before final failure of the blade, whereas strain gages on the blade did not indicate damage until just before final failure. A post-failure sectioning and examination of the blade verified the damage locations predicted by the structural neural system, and showed that the structural neural system is a practical technique for health monitoring of large structures. Beyond health monitoring, the structural neural system can tell where damage initiates and how damage propagates in a structure. This information might be useful to improve the design and manufacturing of structures.

Committee:

Mark Schulz (Advisor)

Subjects:

Engineering, Mechanical

Keywords:

Acoustic Emission; Structural Health Monitoring; Structural Neural System; Continuous Health Monitoring.

THIEN, ANDREW B.PIPELINE STRUCTURAL HEALTH MONITORING USING MACRO-FIBER COMPOSITE ACTIVE SENSORS
MS, University of Cincinnati, 2006, Engineering : Mechanical Engineering
The United States economy is heavily dependent upon a vast network of pipeline systems to transport and distribute the nation’s energy resources. As this network of pipelines continues to age, monitoring and maintaining its structural integrity remains essential to the nation’s energy interests. Numerous pipeline accidents over the past several years have resulted in hundreds of fatalities and billions of dollars in property damages. These accidents show that the current monitoring methods are not sufficient and leave a considerable margin for improvement. To avoid such catastrophes, more thorough methods are needed. As a solution, the research of this thesis proposes a structural health monitoring (SHM) system for pipeline networks. By implementing a SHM system with pipelines, their structural integrity can be continuously monitored, reducing the overall risks and costs associated with current methods. The proposed SHM system relies upon the deployment of macro fiber composite (MFC) patches for the sensor array. Because MFC patches are flexible and resilient, they can be permanently mounted to the curved surface of a pipeline’s main body. From this location, the MFC patches are used to monitor the structural integrity of the entire pipeline. Two damage detection techniques, guided wave and impedance methods, were implemented as part of the proposed SHM system. However, both techniques utilize the same MFC patches. This dual use of the MFC patches enables the proposed SHM system to require only a single sensor array. The presented Lamb wave methods demonstrated the ability to correctly identify and locate the presence of damage in the main body of the pipeline system, including simulated cracks and actual corrosion damage. The presented impedance methods demonstrated the ability to correctly identify and locate the presence of damage in the flanged joints of the pipeline system, including the loosening of bolts on the flanges. In addition to damage to the actual pipeline itself, the proposed methods were used to demonstrate the capability of detecting deposits inside of pipelines. Monitoring these deposits can prevent clogging and other hazardous situations. Finally, suggestions are made regarding future research issues which are needed to advance this research. Because the research of this thesis has only demonstrated the feasibility of the techniques for such a SHM system, these issues require attention before any commercial applications can be realized.

Committee:

Dr. Randall Allemang (Advisor)

Subjects:

Engineering, Mechanical

Keywords:

structural health monitoring; damage detection; active sensing

Shiryayev, Oleg V.Improved Structural Health Monitoring Using Random Decrement Signatures
Doctor of Philosophy (PhD), Wright State University, 2008, Engineering PhD

Detection of structural damage phenomena such as cracks, delaminations, or loose fasteners is important because it increases safety and may allow significant reduction in operational costs. Many structural health monitoring techniques are based on detecting changes in vibration features obtained from the measured response. The technique presented in this work is based on the random decrement signatures calculated from the random response. No knowledge of input signal is required. Random decrement signatures have been used for damage detection purposes in the past. The significance of this work is that it explores the possibility of identifying the type of damage and focuses on detection of opening and closing cracks. Statistical features of the signatures are used to detect the presence of nonlinearity that often occurs due to onset of damage.

A numerical study was performed where simulated measurements were obtained from a finite element model of a frame containing a damaged member. The results of this numerical study showed that the new technique is able to detect crack type damage in a complex structure. It was able to highlight the damaged spar, but it was not able to precisely locate the damaged member in the spar. The results suggest that reliability of damage detection depends on the amount of noise in the measurements.

Experimental validation was performed using a cantilever beam experiment. The damaged beam used in the experiment contained a real fatigue crack instead of saw cuts that are often used to simulate damage. The technique was shown to be able to detect damage when excitation level was greater than 1.0g RMS. The exact location of damage is not always detected reliably, as sometimes a segment of the structure adjacent to the actual damaged segment is indicated. The advantage of the new technique is that it is model-free and could be used on structures excited by ambient forces that are difficult or sometimes impossible to measure.

Committee:

Joseph Slater, PhD (Advisor); Kuldip Rattan, PhD (Committee Member); Junghsen Lieh, PhD (Committee Member); Mitch Wolff, PhD (Committee Member); Nathan Klingbeil, PhD (Committee Member); Richard Cobb, PhD (Committee Member)

Subjects:

Mechanical Engineering

Keywords:

structural health monitoring; vibration; nonlinear dynamics

Zhang, FanTwo new approaches in anomaly detection with field data from bridges both in construction and service stages
MS, University of Cincinnati, 2015, Engineering and Applied Science: Electrical Engineering
The University of Cincinnati Infrastructure Institute has been dedicated to Structural Health Monitoring for about 20 years. UCII establishes a whole set of monitoring system including sensors, data acquisition equipment and a customer website for each bridge that is to be monitored. The Ironton-Russell Bridge Replacement is the first bridge that UCII has monitored since the bridge’s construction stage. At the heart of UCII’s monitoring system is the ability to detect any anomalies; among these anomalies might be damages caused by structural changes due to creep, shrinkage, crack and so forth. The existing anomaly detection algorithm assumes a linear relationship between strain and temperature. To complement the anomaly detection, an Autoregressive Model based algorithm is proposed which doesn’t rely on the relationship between strain and temperature. Also proposed is a probabilistic approach which employs t-distribution to identify anomalies, moreover, this approach is promising in discerning anomalies that are caused by temperature change from those not related to temperature. These two approaches are proved to be applicable for both in-construction and in-service bridges.

Committee:

Arthur Helmicki, Ph.D. (Committee Chair); H. Howard Fan, Ph.D. (Committee Member); Victor Hunt, Ph.D. (Committee Member)

Subjects:

Engineering

Keywords:

Structural health monitoring;anomaly detection;bridge in construction

Norouzi, MehdiTracking Long-Term Changes in Bridges using Multivariate Correlational Data Analysis
PhD, University of Cincinnati, 2014, Engineering and Applied Science: Electrical Engineering
In this dissertation, long-term measurement data that is being collected from the Jeremiah Morrow Bridge will be used to quantify annual variation in data and establish boundaries for detecting abnormal behaviors including anomalies from univariate trends or multivariate correlational trajectories. Long-term measurement data from the US Grant Bridge will also be used for calibrating an autoregressive integrated moving average model and distinguishing maintenance events. First, the monitoring system that has been used for the two bridges under evaluation will be overviewed. Second, sensory data will be analyzed as a univariate time series and transformed to a simple regression model using temperature data as exogenous inputs. Third, correlation between temperature and sensory data will be analyzed and abnormal changes or outliers within the bivariate time series will be identified. We will try to identify how temperature trends change over time and use the dynamic trends to probabilistically classify temperature-caused events. Fourth, load responses of a bridge will be used to define load signatures; whenever a lane load exists on a bridge (e.g., halted or slowed traffic), the sensory network responds in a certain way that can be quantified by correlation of measured values. Using the identified signature, we should be able to distinguish lane loads from thermal responses. Finally, combining univariate time series outlier detection, variable correlational coefficients (Principle components), extreme thermal response signature, and the load response signature, an integrated monitoring system will be proposed and the results will be compared with previously implemented systems by UCII for these structures.

Committee:

Victor Hunt, Ph.D. (Committee Chair); Douglas Nims, Ph.D. (Committee Member); Arthur Helmicki, Ph.D. (Committee Member); Ali Minai, Ph.D. (Committee Member); William Wee, Ph.D. (Committee Member)

Subjects:

Electrical Engineering

Keywords:

Structural Health Monitoring;PCA;Time Series Analysis;ARIMAX;mPCA;Integrated System

Kolli, Phaneendra K.Wireless Sensor Network for Structural Health Monitoring
Master of Science in Engineering, Youngstown State University, 2010, Department of Electrical and Computer Engineering
A wireless sensor mesh network for health monitoring of structures is presented. It is a low cost, easy to deploy, fast and reliable wireless sensor network. Wireless nodes are all identical to each other with on board sensors for measuring acceleration and temperature. The acceleration data from the nodes used to detect the strain of the structure was calibrated using a Vishay P3 strain gauge instrument. These sensor nodes can collect data as well as relay the data of the neighboring nodes. Data from all the nodes reaches the base station through multiple hop relays. The nodes were tested for their performance by using different frequency channels and radio output power levels. This network implements an energy efficient routing protocol which can also handle a node failure in route without losing data. Different power conservation techniques were discussed which can keep the network unattended for a week after being deployed on the structure.

Committee:

Frank Li, PhD (Advisor); Philip Munro, PhD (Committee Member); Faramarz Mossayebi, PhD (Committee Member)

Subjects:

Civil Engineering; Computer Science; Electrical Engineering; Engineering

Keywords:

Wireless sensor network; Structural health monitoring; Routing protocol; Power management; Sun SPOT.

Boehle, Matthew C.Synthesis and Characterization of a Carbon Nanotube Based Composite Strain Sensor
Master of Science (M.S.), University of Dayton, 2016, Mechanical Engineering
In order to more effectively monitor the health of composite structures, a fuzzy fiber strain sensor was created. The fuzzy fiber is a bundle of glass fibers with carbon nanotubes or nanofibers grown on the surface using a novel chemical vapor deposition process. The nanotube coating makes the fiber bundle conductive while the small conductive path increases sensitivity. The fuzzy fiber sensor can replace conventional metal foil strain gauges in composite applications. The sensor was first characterized by use of a micro-tension test to generate load vs. resistance plots to demonstrate the feasibility of the sensor. The fibers were then cast into epoxy dogbone specimens to enable testing with an extensometer to quantify its strain sensitivity. Sensors were then embedded in carbon fiber prepreg panels. Specimens were prepared to demonstrate their performance in a composite laminate typical of aerospace structures. A multi-axial specimen was constructed to test sensor response to longitudinal, transverse and off-axis loading cases. Cyclic tests were performed to check for hysteresis or non-reversible changes to the sensor. A finite element model was created to compare the experimental results to the expected behavior based on the Poisson effect.

Committee:

Khalid Lafdi (Committee Chair); Thomas Whitney (Committee Member); Vinod Jain (Committee Member)

Subjects:

Mechanical Engineering

Keywords:

carbon nanotube; structural health monitoring; composites; strain sensing; self-sensing composites; CNT strain gage

KIRIKERA, GOUTHAM RAGHAVENDRAAN ARTIFICIAL NEURAL SYSTEM WITH DISTRIBUTED PARALLEL PROCESSING FOR STRUCTURAL HEALTH MONITORING
MS, University of Cincinnati, 2003, Engineering : Mechanical Engineering
There is a growing need for the development of in-situ continuous monitoring systems to allow the health monitoring of large structures and the rapid introduction of advanced high performance and heterogeneous materials and combinations of these materials into service. This thesis makes a contribution in the development of artificial neural systems for health monitoring of large and complex structures, and for impact location on targets. The artificial neural system is a passive monitoring system that can minimize the on board instrumentation needed for real-time health monitoring. The system uses highly-distributed interconnected sensor nodes and parallel processing that mimics the hierarchy of the biological neural system to collect dynamic strain signals caused by damage events. The dynamic strains can be in the form of high frequency waves called acoustic emissions caused by damage growth or lower frequency waves and vibration caused by impact to the structure. The artificial neural system processes these dynamic signals and provides an indication of the location and severity of the damage or impact. To verify the approach, an artificial neural system and wave propagation in the panel were modeled. Simulations of damage and impact in a glass fiber composite plate were performed in which the elastic response was computed in closed form at small time steps and the coupled piezoceramic constitutive equations and conductivity equations were also solved. Experimentation was then performed using a glass fiber composite panel and the simulation and experimental results were compared. These studies showed that the artificial neural system is a simultaneously sensitive to low frequency dynamic strains caused by structural vibrations and impact, as well as high frequency acoustic emission signals that accompany damage growth. An important advantage of this new approach is the application of inhibition and firing of the neurons that receive the damage signals. This allows a large reduction in the number of channels of data acquisition needed for health monitoring. Composite materials in particular can benefit from this new type of health monitoring system. Large structures like aircraft and submarines can be monitored simultaneously with a small number of data acquisition channels.

Committee:

Dr. Mark J. Schulz (Advisor)

Subjects:

Engineering, Mechanical

Keywords:

structural health monitoring; artificial neural system; lamb waves; PZT

Kelly, Brendan T.A Newly Proposed Method for Detection, Location, and Identification of Damage in Prestressed Adjacent Box Beam Bridges
Master of Science (MS), Ohio University, 2012, Civil Engineering (Engineering and Technology)
In recent years Structural Health Monitoring (SHM) has gained considerable attention in the engineering world. For bridge infrastructure, previous methods have focused on the detection of localized damage through manipulation of modal parameters extracted longitudinally along the structure. This paper improves upon previous methods by applying modal parameter extraction techniques transversely. Through application of modal curve fitting to transverse frequency response functions (FRFs), not only is local damage identified, but global beam damage is also identified. To present the effectiveness of the approach, modal parameters were extracted from acceleration data obtained from a finite element model (FEM). Analysis of the modal parameters showed that the proposed approach could not only detect both local and global beam damage, but could also differentiate between damage mechanisms using only one transverse mode shape. The proposed method was compared to a previously developed SHM method, the Uniform Load Surface-Curvature (ULS-Curvature) Method.

Committee:

Kenneth K. Walsh, PhD (Advisor); Eric P. Steinberg, PhD (Committee Member); Byung-Cheol Kim, PhD (Committee Member); Tatiana Savin, PhD (Committee Member)

Subjects:

Civil Engineering

Keywords:

Structural Health Monitoring; Bridge Monitoring; Change in First Transverse Mode Method

Tobe, Randy JosephStructural Health Monitoring of a Thermal Protection System for Fastener Failure with a Validated Model
Doctor of Philosophy (PhD), Wright State University, 2010, Engineering PhD

Air vehicles flying at hypersonic speeds encounter extreme thermal, aerodynamic and acoustic loads. To maintain the structural integrity of the flight vehicle, a thermal protection system shields the main structure from these loads. Therefore, maintaining the health of the thermal protection system is critical for a successful mission and vehicle safety. One of the more common types of failure in a mechanically attached thermal protection system is fastener failure. Since reducing vehicle turnaround between flights is desired, creating an automated system to perform structural health monitoring on the fastener health of the thermal protection system is needed. This can be completed by analyzing changes in the dynamic characteristics of the system due to fastener failure.

While much of the recent experimental research focuses on using sensors to detect high-frequency dynamic changes in the system to detect damage, this research focuses on investigating fastener failure damage where only low-frequency dynamics are available. This involves validating a finite element model with low-frequency experimental dynamic tests to ensure the geometry, boundary conditions, material properties, and finite element mesh properly capture the physical characteristics of the structure. The damage states are then simulated with the finite element model to obtain a better understanding of how the damages cause low-frequency dynamic changes without requiring a vast amount of experimental data. The damage detection metrics include previously developed modal parameters-the MAC, PMAC, and COMAC-in addition to two newly developed damage metrics-the normalized coordinate modal assurance criterion and the normalized coordinate modal assurance criterion summation. The new damage metrics investigate how mode shape normalization can provide a distributed prediction for where damage is located.

Committee:

Ramana Grandhi, PhD (Advisor); Ravi Penmetsa, PhD (Committee Member); Joseph Slater, PhD (Committee Member); Richard Cobb, PhD (Committee Member); Steven Olson, PhD (Committee Member)

Subjects:

Mechanical Engineering

Keywords:

Modal-based damage metrics; MAC; Structural health monitoring; Thermal protection system; Fastener failure

Essegbey, John W.Piece-wise Linear Approximation for Improved Detection in Structural Health Monitoring
MS, University of Cincinnati, 2012, Engineering and Applied Science: Electrical Engineering
Structural health monitoring mostly refers to damage identification based on some variations in a system’s behavior with most techniques assuming linearity. Over years of practice within a field, expert knowledge of such variations is acquired resulting in the development of some non-linear mental models of the systems. The traditional quantitative statistical model has been integrated with a new qualitative mental model of day and night in a dynamic bridge system’s strain response. In some divide and conquer fashion, the more linear component of the strain response is analyzed separately with more prediction accuracy and threshold sensitivity. The introduction of such intelligence into the unsupervised learning phase of the linear regression based data analysis algorithm for structural health monitoring has been demonstrated to increase the overall efficiency of the monitor. The increase in sensitivity over the night and attendant detection addresses in part the issue of most structural health monitors being blinded by environmental variations. Using field data from the three-span Ironton-Russell truss bridge, the system behavior is better characterized as having some piecewise linearity, a new paradigm that could contribute towards improving the adequacy of data driven baseline models for monitoring of civil infrastructure. The use of the data itself has also been demonstrated to aid in selecting parameters of concern, threshold setting and system analysis for decision making.

Committee:

Arthur Helmicki, PhD (Committee Chair); Victor Hunt, PhD (Committee Member); Ali Minai, PhD (Committee Member)

Subjects:

Electrical Engineering

Keywords:

structural health monitoring;piece-wise linear approximation;mental model;intelligent parameter varying;improved detection;exploratory data analysis;

Jiang, XiaomoDynamic fuzzy wavelet neural network for system identification, damage detection and active control of highrise buildings
Doctor of Philosophy, The Ohio State University, 2005, Civil Engineering
A multi-paradigm nonparametric model, dynamic fuzzy wavelet neural network (WNN) model, is developed for structural system identification of three dimensional highrise buildings. The model integrates chaos theory (nonlinear dynamics theory), a signal processing method (wavelets), and two complementary soft computing methods (fuzzy logic and neural network). An adaptive Levenberg-Marquardt-least-squares learning algorithm is developed for adjusting parameters of the dynamic fuzzy WNN model. The methodology is applied to one five-story test frame and two highrise moment-resisting building structures. Results demonstrate that the methodology incorporates the imprecision existing in the sensor data effectively and balances the global and local influences of the training data. It therefore provides more accurate system identifications and nonlinear approximation with a fast training convergence. A nonparametric system identification-based model is developed for damage detection of highrise building structures subjected to seismic excitations using the dynamic fuzzy WNN model. The model does not require complete measurements of the dynamic responses of the whole structure. A damage evaluation method is proposed based on a power density spectrum method. The multiple signal classification method is employed to compute the pseudospectrum from the structural response time series. The methodology is validated using experimental data obtained for a 38-story concrete test model. It is demonstrated that the WNN model together with the pseudospectrum method is effective for damage detection of highrise buildings based on a small amount of sensed data. A nonlinear control model is developed for active control of highrise three dimensional building structures including geometrical and material nonlinearities, coupling action between lateral and torsional motions, and actuator dynamics. A dynamic fuzzy wavelet neuroemulator is developed for predicting the structural response in future time steps. A neuro-genetic algorithm is developed for finding the optimal control forces without the pre-training required in a neural network-based controller. Both neuroemulator and neuro-genetic algorithm are validated using two irregular three-dimensional steel building structures, a twelve-story structure with vertical setbacks and an eight-story structure with plan irregularity. Numerical validations demonstrate that the control methodology can significantly reduce the structural displacements of three-dimensional buildings subjected to various seismic excitations.

Committee:

Hojjat Adeli (Advisor)

Subjects:

Engineering, Civil

Keywords:

Structural System Identification; Damage Detection; Structural Health Monitoring; Active Control; Dynamic Neural Network; Wavelets; Fuzzy Logic; Genetic Algorithm; Nonlinear Optimization

Zanjanizadeh, VahidUse of Finite Element Modeling for Condition Assessment of reinforced Concrete Bridge Colums in Structural Health Monitoring
Master of Science in Engineering, University of Akron, 2009, Civil Engineering
Bridges are very important in public transportation because vast revenue resources are consumed in building bridges, and the need to maintain these structures to be continuously operational. In addition, bridges deteriorate with time like any other structure. The causes could be chemical attack, overloading, environmental effects, corrosion of steel reinforcement and quality of maintenance. Hence, they require health monitoring and structural evaluation periodically. Structural health monitoring (SHM) holds a great deal of potential to reduce the inspection and maintenance costs of existing bridges by identifying the structural deficiencies at an early stage, as well as verifying the efficacy of repair or rehabilitation procedures. Most of the SHM research focused is on the bridge deck and girders, and there appears to be no focused study on condition assessment of bridge columns despite bridge columns being more vulnerable against corrosion, and susceptible to vehicle collisions. This research consists of investigation of methodologies for full scale finite element modeling of bridges subjected to moving truck loads using a commercial package called ABAQUS/standard. Moving load induced by two standard AASHTO trucks was developed through load-time history that was applied on 35 nodes on the bridge deck. Modal analysis followed by an implicit dynamic analysis was carried out to study the dynamic behavior of bridges under moving load. The selected bridge was West bound Ronald Reagan cross country highway (SR126) bridge, HAM-126-0881, over Hamilton Avenue (Route 4) in Cincinnati, Ohio. It is a typical three-span steel-girder reinforced concrete bridge. The results of finite element analysis are validated with data collected in field SHM tests conducted on this bridge through wired sensor network by another research group. Additionally, the influence of several parameters such as variations in truck loads and the corresponding speeds, damping ratios of the bridge, and the possible variations in material properties of concrete on the dynamic response of bridges was studied using finite element modeling. Good agreement was found between the field measurement and the response predicted by the finite element simulations. Most concerned dynamic response was strains at different locations in bridge girders and columns, because it is a significant item that can be measured with confidence during structural health monitoring field tests. Particularly, the strain response of the columns due to moving loads was evaluated in this study. The study revealed that the columns have considerable influence on dynamic behavior of the bridges. Also, the range of the strains in the column was found to be very small (20 to 100-micro). This strain range may be used in sensor designs for SHM field tests. By increasing truck speed and weight, the response of the bridge in terms of strains and accelerations increased. However, the effect of the damping was quite the opposite, i.e., by increasing the damping ratio, the response of the bridge reduced. The effect was in the form of smoothing of the response curves than decreasing the global response.

Committee:

Anil Patnaik, PhD (Advisor)

Subjects:

Civil Engineering

Keywords:

Bridge; Structural health monitoring; damage; Bridge columns;Moving load; dynamic

Niroula, KushalAcoustic Monitoring of the Main Suspension Cables of the Anthony Wayne Bridge
Master of Science, University of Toledo, 2014, Civil Engineering
The 82 year old Anthony Wayne Bridge (AWB) in Toledo, Ohio is undergoing an extensive rehabilitation in two phases starting in construction season 2014. The plan is to first replace the approaches and rehabilitate the superstructure. Upon completion of the superstructure rehabilitation, steps to preserve the main suspension cables will be taken. Prior to taking action to preserve the cables, however, it is necessary to evaluate the condition of the cables. Therefore, as part of cable condition evaluation, an acoustic monitoring system was installed on July 2011 and has been continuously monitoring the main cables since then. Acoustic emission (AE) is a non-destructive technique which is practical for monitoring elements of bridges where invasive inspection is either difficult or costly. The AE system can be accessed remotely in real time and it does not cause any interruption to traffic. In the case of a suspension bridge, main cables are of primary concern as their condition cannot be assessed externally unlike other bridge components and they are fracture critical. This paper presents a case study on the application of the acoustic emission technique to the main cables of the AWB. Several laboratory experiments were planned and executed to develop understanding of the potential AE sources. Wire breaks were the primary AE sources under concern. Rain and frictional activities induced by traffic and wind events would create secondary and/or noise sources. The rain, friction and wire break were all simulated in the laboratory and it was verified that, by using a combination of parameters along with signal signatures, a wire break signal can be discriminated against other secondary or noise sources. The AE monitoring system on the AWB uses a series of 7 algorithms that analyze the parameters of each detected AE event. For each feature that meets or exceeds the value of the classification, it is assigned a source type ranging from 0 to 7. Thus for a wire break, the AE signal would meet or exceed all 7 criteria and would receive a `source type’ classification of `7’. The analysis of the data collected on the AWB during January 2013 to June 2013 (excluding May 2013) showed very high acoustic activity near sensors 1, 2, 14 and 15. After further examination it was found that those activities were of frictional nature caused by weather events and traffic induced movements. Many AE events were classified as high as `source type 6’ that occurred during extreme weather events and there was not any `source type 7’ event. This suggests that no wire breaks have been recorded so far. No wire breaks were discovered during the 2012 invasive inspection too, which supports the results from AE monitoring. Meanwhile, the system was unable to capture the signal produced by cutting of wire samples during the invasive inspection and this challenges the reliability of the monitoring system. An `Auto Sensor Test’ performed in March 2014 indicates that there has been degradation in the system’s performance. Many of the sensors do not seem to have proper coupling, thereby causing difficulty in effective signal-source interpretation. Very few AE events were observed in a review of data from the bridge closure period that started on March 17, 2014.

Committee:

Douglas Nims (Advisor); Douglas Nims (Committee Chair); Brian Randolph (Committee Member); Ahalapitiya Jayatissa (Committee Member)

Subjects:

Civil Engineering; Electrical Engineering

Keywords:

Acoustic Monitoring, Main Suspension Cables, Structural Health Monitoring, Non-Destructive Evaluation, Anthony Wayne Bridge, Acoustic Emission

Adediji, Adekunle CPCA Eigen Residuals: An Analytical Solution to System Modeling and Multivariate Structural Health Monitoring
MS, University of Cincinnati, 2013, Engineering and Applied Science: Electrical Engineering
Typically, researchers underestimate the effect of temperature, amongst other inputs such as load, creep and shrinkage, in a structure. Even when this is not the case, it is treated as completely independent and worthy of generating error-free predictions about the current state of a structure. There have been various methods used, under the broader context of Structural Health Monitoring in literature, and most have been feature-extraction-based solutions. This thesis introduces Eigen Residuals, which is mathematically derived from PCA. I shall show that knowledge from this, and Eigen Slope, better models a truss bridge member's response to changes in strain as a result of driving inputs. There are instances where existing system models for a structure's members are inaccurate, and PCA System Modeling solves this challenge. Next, I establish a monitoring threshold using equi-probability contours, and incorporate knowledge of expected structural relationships between members. I proceed to develop a system-wide multi-member bridge monitoring algorithm, using Eigen Residuals in a multivariate approach. Members validate each other (via a voting system for events of significance), and provide an extra layer of intelligence. Our final results show an improved system model for individual members of a truss bridge. In addition, the multi-member algorithm eliminated false alarms that were generated, and provided extra information to the Ohio Department of Transportation, for bridge maintenance purposes.

Committee:

Arthur Helmicki, Ph.D. (Committee Chair); Victor Hunt, Ph.D. (Committee Member); Ali Minai, Ph.D. (Committee Member)

Subjects:

Engineering

Keywords:

Analytical;Eigenvector Residuals;System Modeling;Multivariate;PCA;Structural Health Monitoring

DATTA, SAURABHACTIVE FIBER COMPOSITE CONTINUOUS SENSORS FOR STRUCTURAL HEALTH MONITORING
MS, University of Cincinnati, 2003, Engineering : Mechanical Engineering
Detecting damage in structures that are in service and operating is difficult using conventional non-destructive evaluation techniques. This thesis examines the use of acoustic emission and resulting waves in the structure to determine damage in the structure. In order to detect and measure the waves generated continuous sensors are used. Continuous sensors contain multiple interconnected sensor nodes that form an array of sensors covering the whole structure. A new concept of active fiber composite sensor is added to the continuous sensor. The use of active fiber sensor brings the possibility of unidirectional sensing in continuous sensor. The advantage of this passive health monitoring approach is that the sensors are highly distributed and uses parallel processing allowing large structures to be monitored for damage using a small number of channels of data acquisition. In the thesis, the continuous sensor is modeled and simulated by solving the elastic response of a plate and the coupled piezoelectric constitutive equations. The model and simulation allow the sensor to be optimized for a particular material and plate size. The simulation predicts that acoustic waves representative of damage growth can be detected using continuous sensors. The simulation results show the possibility of unidirectional sensing and give some insight into the sensor response. Based on the simulation results the unidirectional sensor are constructed and tested. To improve the sensitivity of the continuous sensor, unidirectional active fiber composite sensors were built from piezoceramic ribbon preforms. Different designs and sensor configurations are examined and advantages are discussed. The sensor design proposed is manufactured in Smart Structures and Bio-Nanotechnology Laboratory. Step by step manufacturing of the active fiber composite sensors is also discussed in the thesis. The continuous sensors constructed in the lab are evaluated in a realistic test to show their ability to detect acoustic emissions caused by damage to a composite material. The sensors were mounted on narrow glass fiber plates and tested to failure in a mechanical test machine. The sensors were able to detect the propagation of crack when the structure containing the damage was still intact and was in service. Results from the experiments are presented and explained in relevant section of this thesis. Use of active fiber sensor as continuous sensors can potentially reduce the cost, complexity, and number of channels of data acquisition so that this technique becomes practical to perform structural health monitoring.

Committee:

Dr. Mark J. Schulz (Advisor)

Subjects:

Engineering, Mechanical

Keywords:

active fiber composite continuous sensors; structural health monitoring; acoustic emissions; artificial neural system; damage detection

Rahman, A.B.M. MostafizurAssessment of Bridge Service Life Using Wireless Sensor Network
Master of Science in Engineering, Youngstown State University, 2012, Department of Civil/Environmental and Chemical Engineering

This paper describes a method for estimating remaining service life of a bridge based on real-time responses of the bridge. Real-time responses were recorded using wireless sensor network. With a significant percentage of nation's bridges being structurally deficient or functionally obsolete and with no quantitative method of health monitoring being used in general practice, it has become the necessity to develop a SHM method, which will provide a quantitative assessment of overall bridge health. This research focuses on estimating overall condition of the bridge analyzing dynamic response rather than focusing on individual damage types, their severity and locations.

SHM process in this research uses dynamic responses of a bridge subjected to service loads, collects the response through a system of wireless sensor network, simulates an ideal and practical bridge using finite element model, and then estimates the remaining service life of the bridge based on the modal correlation between the existing and an ideal bridge condition. Results indicate that the bridge under this study has lost approximately 47% of its approximately 50 years of service life in 30 years of service. It was also observed that only higher order modes are more sensitive to damage compared to lower ones.

With limited budget available for bridge maintenance and repair, this research can help bridge owners, policy makers, transportation planners or any related professionals or organizations in prioritizing and allocating budgets based on actual bridge condition.

Committee:

AKM Anwarul Islam, PhD (Advisor); Javed Alam, PhD (Committee Member); Frank Li, PhD (Committee Member)

Subjects:

Civil Engineering; Engineering

Keywords:

Bridge Service Life Assessment; Bridge Health; Wireless Sensor Network; SHM; Structural Health Monitoring

SHINDE, VISHALDEVELOPMENT OF A DATA ACQUISITION SYSTEM AND PIEZOELECTRIC SENSORS FOR AN EXPERIMENTAL STRUCTURAL NEURAL SYSTEM
MS, University of Cincinnati, 2006, Engineering : Mechanical Engineering
This thesis develops a data acquisition system and long piezoelectric sensors for a technique of structural health monitoring based on a structural neural system and continuous sensors. The structural neural system uses distributed sensing and parallel signal processing in real time to monitor large structures like an aircraft for damage. The structural neural system consists of piezoceramic nerves and electronic logic circuits and was tested on an aluminum plate that was fatigue loaded using a mechanical testing machine. The testing indicated that the SNS analog processor using conventional monolithic piezoceramic sensors was able to detect the acoustic emissions using continuous fiber sensors. The acoustic emission level in aluminum was small but detectable. A higher sensitivity of the neural system was needed. Therefore, further sensor development was undertaken including fabricating piezoelectric active fiber continuous sensors. The testing in this thesis indicates that the continuous sensor can becloser to the damage site, and is more sensitive than conventional discrete sensors. In thisthesis a data acquisition system was developed using LabVIEW and single fiber continuous sensors were developed for the structural neural system. The testing indicates that the structural neural system will be able to continuously monitor a structure and provide a long-term history of the health of the structure.

Committee:

Dr. Mark Schulz (Advisor)

Subjects:

Engineering, Mechanical

Keywords:

Structural Health Monitoring; Acoustic Emission; AE; NDT; Piezo; Piezoelectric sensors; AFC; Composite Sensors; Smart Materials; Data Acquisition; DAQ; LabVIEW.

Schomer, John JEmbedding fiber Bragg grating sensors through ultrasonic additive manufacturing
Master of Science, The Ohio State University, 2017, Mechanical Engineering
Fiber Bragg Grating (FBG) sensorsare optical fibers that detect in-situ strain through deviation of a reflected wavelength of light to detect in-situ strain. These sensors are immune to electromagnetic interference, and the inclusion of multiple FBGs on the same fiber allows for a seamlessly integrated sensing network. FBGs are attractive for embedded sensing in aerospace applications due to their small noninvasive size and prospect of constant, real-time nondestructive evaluation. FBGs are typically used in composite laminate type applications due to difficulties in building them into metallic structures. Additive manufacturing, also referred to as 3D printing, can allow for the inclusion of sensors inside of structural entities by the building of material around the sensor to be embedded. In this study, FBG sensors are embedded into aluminum 6061 via ultrasonic additive manufacturing (UAM), a rapid prototyping process that uses high power ultrasonic vibrations to weld similar and dissimilar metal foils together. UAM was chosen due to the desire to embed FBG sensors at low temperatures, a requirement that excludes other additive processes such as selective laser sintering or fusion deposition modeling. This study demonstrated the feasibility of embedding FBGs in aluminum 6061 via UAM. Further, the sensors were characterized in terms of birefringence losses, post embedding strain shifts, consolidation quality, and strain sensing performance. Sensors embedded into an ASTM test piece were compared against an exterior surface mounted foil strain gage at both room and elevated temperatures using cyclic tensile tests. The effects of metal embedment at temperatures above the melting point of the protective coating (160 degrees Celsius) of the FBG sensors were explored, and the hermetic sealing of the fiber within the metal matrix was used to eplain the coating survival. In-situ FBG sensors were also used to monitor the UAM process itself. Lastly, an example application was both modeled using finite element analysis to identify areas where FBG sensors could be placed, and then built with an embedded FBG sensor.

Committee:

Marcelo Dapino (Advisor); Mo-How Shen (Committee Member)

Subjects:

Mechanical Engineering

Keywords:

Fiber Bragg Grating, Ultrasonic Additive Manufacturing, Structural Health Monitoring, 3D Printing, Fiber Optic

Ranade, Ashutosh MLoad Rating for the Critical Components of Ironton-Russell Bridge
MS, University of Cincinnati, 2017, Engineering and Applied Science: Civil Engineering
The behavior of a structure can be documented by implementation of a Structural Health Monitoring (SHM) system to collect long-term measurement data. SHM makes it possible to detect changes of structural responses and, in some cases, correlate the changes of structural responses with changes in material and geometric properties. The ability of the SHM system to predict the responses is directly correlated to the reliability and effectiveness of the selected network of sensors. Therefore, an effective SHM system is developed based upon the selection of the critical points of a structure that have to be inspected and monitored. This thesis concentrates on the methodology for determining and validating the critical points of the Ironton-Russell Bridge, a cable-stayed bridge in Ironton, OH and Russell, KY. A set of finite element models (i.e., both SAP2000 and MIDAS) were employed to determine load responses used in the design and construction of the bridge. Member capacities were calculated using RESPONSE 2000. Capacity and load responses were used to calculate the rating factors for towers, edge girders, and piers. Based on this information, the critical locations in the bridge were determined and used as the basis for SHM sensor locations, which had been selected for installation during construction of the bridge. The differences in the design drawings and the as-built structure were studied especially for their effects on the rating factors and, thus, the locations of the sensors. Future scope of the project is to conduct truck load tests at predetermined locations based on the influence line diagrams, and to compare the measured data to the simulation data.

Committee:

Bahram| Shahrooz (Committee Chair); Arthur Helmicki (Committee Member); Richard Miller (Committee Member)

Subjects:

Civil Engineering

Keywords:

Load Rating;Cable-stayed Bridge;Structural Health Monitoring

Hehr, Adam JEmbedded Carbon Nanotube Thread Strain and Damage Sensor for Composite Materials
MS, University of Cincinnati, 2013, Engineering and Applied Science: Mechanical Engineering
This thesis investigates the use of carbon nanotube (CNT) thread for use in distributed structural health monitoring (SHM) systems, specifically as an embedded damage and strain sensor for laminated polymeric fiber composite materials. CNT thread has shown potential to be integrated into/onto composite materials to provide confident damage detection, localization, and characterization in complex geometries without complicated detection algorithms and minimal sensing channels. This thesis articulates work done with CNT thread performance as a strain sensor, an evaluation of sensor invasiveness, identification of matrix cracking (fatigue damage), a study of damaging and non-damaging impact events, potential SHM design architectures for aircraft, and the multifunctional aspect of damping. Multifunctional here implies improving the composite material besides self-sensing of damage and strain. Besides improving the material in other ways, CNT thread is low in weight, small in size, and the material is modest in cost. As a consequence of these strong sensor and material characteristics, the author believes that this could be a game changing material for high cost composite vehicles. It is envisioned that future military and commercial composite vehicles will utilize technologies such as this sensing thread to provide safety, reliability, durability, condition-based maintenance, and multifunctionality to the structure.

Committee:

Mark Schulz, Ph.D. (Committee Chair); Randall Allemang, Ph.D. (Committee Member); Allyn Phillips, Ph.D. (Committee Member); Vesselin Shanov, Ph.D. (Committee Member)

Subjects:

Mechanics

Keywords:

carbon nanotube;structural health monitoring;embedded sensing;composite materials;carbon nanotube thread;strain and damage sensing;

Vehorn, Keith A.Vibro-Acoustic Modulation as a Baseline-Free Structural Health Monitoring Technique
Master of Science (M.S.), University of Dayton, 2013, Mechanical Engineering
Structural health monitoring (SHM) methods are being explored as techniques to assess the integrity of mechanical, civil, and aerospace structures. Most of these methods detect or quantify damage by comparing current structural state measurements to stored baseline measurements collected from an undamaged structure. These baseline dependent methods assume that measured signals will not change when exposed to varying environmental and usage conditions. To avoid limitations of this assumption, baseline-free techniques such as vibro-acoustic modulation (VAM) are being explored. VAM is a nonlinear vibration technique in which the structure of interest is excited using a combination of specific frequencies and the response recorded. The VAM technique assumes that an undamaged structure can be represented by a linear system while the representation of a damaged structure must include nonlinearity. A nonlinearity is assumed to result in the generation of sideband responses. To demonstrate the use of VAM to detect fatigue cracking, experimental testing has been performed on existing damaged and undamaged specimens, as well as on fatigue specimens where cracks have been initiated and grown. Initial testing of the damaged and undamaged specimens provides validation for using VAM as a baseline-free SHM technique. Subsequent measurements during fatigue testing confirm this result. Two rectangular coupons were fatigue cycled to initiate and grow cracks. The VAM method detected cracks at 6.42 percent and 12.24 percent damaged cross-sectional area. Potential advantages and limitation of the use of VAM for fatigue crack detection are discussed, and recommendations for additional research efforts to improve or refine the technique are given.

Committee:

Steven A Olson, Ph.D (Advisor)

Subjects:

Mechanical Engineering

Keywords:

vibro acoustic modulation; VAM; nonlinear acoustics; structural health monitoring; SHM; crack detection;

Song, YiMultifunctional Composites Using Carbon Nanotube Fiber Materials
PhD, University of Cincinnati, 2012, Engineering and Applied Science: Mechanical Engineering

Composite materials have high in-plane mechanical properties and are lightweight. Thus laminated composites are widely used in engineering applications. However, interlaminar stresses can arise and lead to premature failure at low in-plane stress levels. This constitutes a fundamental weakness in polymeric laminated composite materials. In addition, the full functional capabilities are not utilized in the designs of current composites. In this dissertation, a smart materials strategy is employed to mitigate the weak interlaminar properties of composite materials and to make composites more multi-functional materials.

The first step in our strategy to improve composites is to reinforce the material using a nano-phase material component. A nanoreinforced laminated composite (NRLC) consisting of Multiwalled Carbon Nanotube (MWCNT) arrays that are interspersed between the plies of a fiber reinforced polymeric composite was developed. The MWCNT array and the polymer matrix are arranged within the laminated composite so that the new interfaces yield higher interlaminar properties. The mechanical response of these composite materials under quasi-static loading including interlaminar shear, in-plane tension, and out-of-plane compression was studied and documented. The morphology of the NRLC was characterized using Scanning Electron Microscopy and correlated with its mechanical response. The NRLC material developed achieved substantially stiffer and stronger interlaminar shear properties without significantly compromising the in-plane mechanical properties.

Structural integrity could still be compromised by unpredictable circumstances thus it will be important to continuously monitor composite structures for damage thus providing safety to the users and confidence to the system operator. The approach taken was to develop a built-in damage sensing system for the material. A new integrated and distributed sensing approach based on nanotechnology was developed wherein carbon nanotube arrays or forests were spun into a tough and electrically conductive thread to be used along with conventional fibers in composites. But the nanotube thread has piezoresistive properties and can sense strain and damage. The sensor thread was integrated into composite materials and used for the first time as a sensor to monitor strains and detect damage including delamination. The instrumented composites, named self-sensing composites, were determined to be very sensitive to damage. These materials will help to revolutionize the maintenance of structures, which will now be based on the actual condition not just the length of operation of the structure.

Finally, multi-functionality of composites was investigated in this dissertation. Composite materials should be able to perform multiple functions simultaneously. Overall, the multi-functional composite materials should self-monitor their integrity, respond to their environment in a functional way, and provide multi-functionality. Smart composites can react to their environment by becoming stiffer to react to external loads, changing temperature to prevent icing, or changing their electromagnetic signature. Multi-functionality could also encompass transmitting electrical power or communication through the structural material, providing lightning and static electricity protection, or acting as an antenna. Carbon nanotube materials are ideal to develop smart materials because nanotubes have high strength, toughness, electrical and thermal conductivity, and light weight that cannot be matched by conventional materials. Different smart and multi-functional properties of smart composites based on carbon nanotube ribbon, yarn, and sheet were investigated and shown to be feasible in this dissertation. Overall, this dissertation aims to open up the frontier for smart composites to become the next generation of new material coming onto the aerospace, defense and advanced materials markets.

Committee:

Mark Schulz, Ph.D. (Committee Chair); Dong Qian, Ph.D. (Committee Member); Vesselin Shanov, Ph.D. (Committee Member); David Thompson, Ph.D. (Committee Member)

Subjects:

Engineering

Keywords:

composites;carbon nanotube CNT;structural health monitoring SHM;

Gordon, Neal AMaterial Health Monitoring of SIC/SIC Laminated Ceramic Matrix Composites With Acoustic Emission And Electrical Resistance
Master of Science in Engineering, University of Akron, 2014, Mechanical Engineering
Ceramic matrix composites (CMC) composed of Hi-Nicalon Type S™ fibers, a boron-nitride (BN) interphase, and pre-impregnated (pre-preg) melt-infiltrated silicon / silicon-carbide (SiC) matrix have been studied at room-temperature consisting of unidirectional and cross-ply laminates. Quasi-static, hysteretic and uniaxial tensile tests were done in conjunction with a variety of temporary, laboratory-based material health-monitoring techniques such as electrical resistance (ER) and acoustic emission (AE). The mechanical stress-strain relationship paired with electrical and acoustic measurements were analyzed to expand upon current composite knowledge to develop a more fundamental understanding of the failure of brittle matrix laminates, their constituents, and interactions. In addition, a simple but effective method was developed to allow visual confirmation of post-test crack spacing via microscopy. To enhance fidelity of acquired data, some specimens were heat-treated (i.e. annealing) in order to alter the residual stress state. Differences in location, acoustic frequency, and magnitude of matrix cracking for different lay-ups have been quantified for unidirectional and [0/90] type architectures. Empirical results shows complex hysteretic mechanical and electrical behavior due to fiber debonding and frictional sliding of which no general model exists to capture the essence of this CMC system. The results of this work may be used in material research and development, stress analysis and design verification, manufacturing quality control, and in-situ system and component monitoring.

Committee:

Gregory Morscher, Dr. (Advisor); Wieslaw Binienda, Dr. (Committee Member); Tirumalai Srivatsan, Dr. (Committee Member)

Subjects:

Aerospace Materials; Mechanical Engineering

Keywords:

Ceramic Matrix Composite,CMC;Aerospace;Non-Destructive Evaluation,NDE;Structural Health Monitoring,SHM;Composites;Acoustic Emission;Electrical Resistance

Lee, Soon GieHybrid Damage Identification Based on Wavelet Transform and Finite Element Model Updating
Doctor of Philosophy, University of Akron, 2012, Civil Engineering

Structural health monitoring (SHM) has gained more attentions recently since nearly 140,000 of a total 600,000 highway bridges in the US are nearing 50 years of age and are approaching the end of their design life. Most in-service highway bridge structures are suspected to be undergoing deterioration processes induced by the physical and harsh environmental changes. Therefore, timely maintenance with a robust SHM system having capability of early detection of impending damage is required to prevent catastrophic events for the public safety with reduced expenses.

Vibrational modal properties may not be sufficient for detecting early damage in local regions of complex civil infrastructure. Moreover, most of current damage detection methods require reference data which are not always available. There have also been pressing needs for real-time monitoring to prevent sudden catastrophic disasters. This dissertation addresses current challenges and needs identified in existing vibration-based damage detection methods, focusing on wavelet-based reference-free real-time damage identification and subsequent finite element model updating for quantifying damage severity.

First, a damage detection method based on a wavelet entropy analysis has been embedded in wireless smart sensor nodes (Imote2) and tested with three-story shear building and a laboratory truss bridge structure. To realize the reference-free damage detection, a continuous relative wavelet entropy (CRWE)-based damage detectionmethod is also proposed and demonstrated with a laboratory truss bridge structure. Although the reference-free CRWE method can detect damage locations without reference data,computational times put limitations in its applications to a real-time SHM system. To make real-time monitoring feasible in SHM systems, a statistical referencefree real-time damage detection method has been developed based on the wavelet packet transformation and log likelihood ratios.

Second, finite element model updating has been conducted to quantify the level of damage at the identified damage locations. For an identification model, fracturemechanics based cracked beam element with local flexibility coefficients and rotational spring stiffness coefficients have been used. After experimental modal testing of the laboratory truss structure, modal properties are extracted by the output-only frequency domain decomposition method. Because of limited number of sensors, mode shapes of each panel of the structure are separately extracted and combined by the interface DOF-by-DOF decentralized modal identification method. Modal properties (i.e. mode shapes and natural frequencies) are used to quantify physical damage level in term of crack depth.

In summary, this dissertation proposed wavelet-based robust and viable real-time reference-free damage localization methods and conducted damage quantification by finite element model updating. The proposed method has been experimentally verified and evaluated using test-beds that include a three-story shear building structure and a laboratory truss bridge structure.

Committee:

GunJin Yun, Dr. (Advisor); Ernian Pan, Dr. (Committee Member); Joan Carletta, Dr. (Committee Member); Kevin Kreider, Dr. (Committee Member); David Roke, Dr. (Committee Member)

Subjects:

Civil Engineering

Keywords:

Structural Health Monitoring; Damage Identification; Wavelet Transform; Damage Quantification; Finite Element Model Updating

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