Fiber reinforced polymers (FRP) are an efficient and inexpensive method of repairing deteriorating infrastructure. FRP sheets can be applied to spalling bridge sections and columns to prevent further deterioration and increase stiffness. However, the effect of the environment on the long-term durability of FRP and how the various damage mechanisms initiate and develop are not known. Systems for structural health monitoring are being sought as a means of managing important components in transportation systems as assets in light of modern life cycle cost concepts. This study characterizes a fiber optic sensor for use in detecting acoustic emissions (AE) in FRP. The results of AE analysis (signal amplitude, frequency spectra, MARSE, and in-plane displacement) caused by simulated fiber fracture experiments and other types of mechanical loading in FRP test coupons are reported. The applications to the development of FRP structural health monitoring systems are also discussed. / Master of Science

With increasing applications of composite materials, their health monitoring is of growing importance in engineering practice. Damage development in composite materials is more complex than for metallic materials, because in composite materials (a) multiple damage modes are simultaneously in play, and (b) individual 'damage events' that occur throughout a component's service life may neither noticeably affect its performance, nor suggest future failure. Therefore, informed health monitoring of composite components must include monitoring and analysis of their health state throughout their service life. A crucial aspect of the health monitoring process of composites is the development of tools to help with this goal of understanding the health state of composites throughout their life. This knowledge can lead to timely anticipation of future failure in composite components, and advance the state of current technology. One, timely maintenance can be planned in advance. Two, each component's service life can be determined based on its individual health information, rather than empirical statistics of previously failed components. This dissertation develops such tools and methods. Composite specimens of multiple ply-layups are subjected to tensile loading schemes until failure. Pencil Lead Breaks (PLBs) are used to simulate Acoustic Emission sources and generate acoustic waves that are acquired by installed piezoelectric sensors. A numerical method to estimate the arrival of wave modes from ultrasonic signals is presented. Methods are also presented that utilize PLB signals to indicate approaching failure of specimens under monotonic as well as cyclic loading. These processes have been developed prioritizing simplicity and ease-of-execution, to be adapted for practical deployment. / Ph. D.

V této diplomové práci jsou popsané SHM metody používané v letectví a dále jsou udělány MKP simulace šíření ultrazvoukových vln v celokompozitním tenkostěnném materiálu používaném u leteckých konstrukcí. Pro simulaci byla zvolena SHM metoda nazývaná pitch-catch. Simulace byla provedena na dvou různých kompozitových materiálech a každý z nich byl testován třemí různými konfiguracemi s trhlinou a jednou konfigurací bez trhliny. Jako prepocessor byl použit MSC.Patran a jako post processory byli použity MSC.Nastran a MSC.Dytran, jejichž výsledky byli na závěr porovnány. Simulace prokázali, že rychlost šíření a amplituda vln šířících se v simulovaném panelu je trhlinami ovlivněna. Při konfiguracích s trhlinami rychlost šíření i amplituda vln byli menší, než v případě bez trhliny. Jako vhodnější post processor při MKP simulacích se ukázal MSC.Nastran, jehož výsledky byli přesnější a zárověň bylo i snažší správně odečítat hodnoty dat z grafů pro podrobnější pozorování šíření vln.

Structural health monitoring (SHM) is a quickly advancing field of study in civil engineering and recent advances in the field are in stark contrast to where the field started. For example modern technology of wireless sensing systems allowed for easier monitoring of structures, but the challenge of limiting the number of instrumented locations has not been overcome with traditional methods. The potential of alternative methods has only been realized in recent years with the increase of model based approaches. In particular, the use of limited measurements to estimate structural response at all locations is appealing. To accomplish this goal, this work approaches SHM by using a numerical model combined with a linear recursive state estimation algorithm, known as the Kalman Filter, to update the model-based prediction with a limited number of real time measurements taken on the structure. A thorough overview of the contents is given here. The first section introduces the topic of SHM and the goal of SHM. Then the challenges and limitation that face SHM are discussed along with the recent advances that can be used to overcome them. In Section 2, the proposed framework, a Kalman filter approach, is established. First, a finite element model is formulated for plate structures using the Mindlin-Reissner plate theory and then this finite element code is verified by a comparison with a commercial FEA software. Then the state space model of the system is defined for use with the Augmented Kalman Filter (AKF); the AKF approach overcomes the intrinsic challenge of unknown excitations for civil structures. The AKF is then formulated and discussed. For Section 3, using the AKF in numerical simulations are conducted for 5 different cases. The first three cases study the advantages of multi-metric measurements, i.e. strain and acceleration measurements combined, versus single metric measurement, i.e. strain measurement only or acceleration measurement only. Following that, the next two cases explore the question of whether multi-metric measurements will always provide the best results. Based on the conclusions from the previous section, Section 4 investigates the application of a genetic algorithm, a search algorithm based of Darwinian principles, to find the optimal sensor placement to use as the input to the AKF. Here the developed search algorithm is used in two cases, the first is to find the optimal placement for the strain measurement only case. Next, the improvements in accuracy that are gained by placing taking more measurements is investigated to determine if the gain in accuracy per added measurement decreases for large numbers of measurements. Section 5 contains the final conclusions about the use of the AKF for SHM of plate structures then the potential opportunities of future work regarding plate structures are discussed.

This thesis presents a development of Structural Health Monitoring (SHM) and Fault Diagnosis based on Artificial Immune System (AIS), a biology-inspired method motivated from the Biological Immune System (BIS). Using the antigen to model structural health or damage condition of specific characteristics and the antibody to represent an information system or a database that can identify the specific damage pattern, the AIS can detect structural damage and then take action to ensure the structural integrity. In this study the antibodies for SHM were first trained and then tested. The feature space in training includes the natural frequencies and the modal shapes extracted from the simulated structural response data including both free-vibration and seismic response data. The concepts were illustrated for a 2-DOF linear mass-spring-damper system and promising results were obtained. It has shown that the methodology can be effectively used to detect, locate, and assess damage if it occurred. Consistently good results were obtained for both feature spaces of the natural frequencies and the modal shapes extracted from both response data sets. As the only exception, some significant errors were observed in the result for the seismic response data when the second modal shape was used as the feature space. The study has shown great promises of the methodology for structural health monitoring, especially in the case when the measurement data are not sufficient. The work lays a solid foundation for future investigations on the AIS application for large-scale complex structures.

An innovative artificial immune system (AIS) is proposed herein for structural health monitoring (SHM) to ensure the structural integrity and functionality. While satisfactory results were obtained by previous AIS schemes, their performance is strongly structural-parameter-value (SPV) dependent and deviations of SPVs in testing from training due to modeling errors and measurement noises significantly deteriorates the AIS' performance. This thesis presents a less SPV-dependent AIS with a three-phase architecture, including damage-existence-detection, damage-location-determination, and damage-severity-estimation, using specially designed feature vectors (FVs) based on structural modal parameters. The maximum-relative-modal-parameter-change is used to detect the damage's existence and estimate its severity, and the pattern in normalized-modal-parameter-change is used to determinate the damage's location. Comparisons between the proposed FVs and their existing counterparts were conducted for 2/3/4-degree-of-freedom structures to illustrate the superior performance and less SPV-dependence of the proposed method, particularly in determining damage location. The proposed AIS was tested on a 4-degree-of-freedom model using 440 randomly generated damage conditions with a different SPV set per condition. A success rate of 95.23% in the determination of damage's existence and its location was obtained. The trained AIS for the 4-degree-of-freedom model was further evaluated by a four-story and two-bay by two-bay prototype structure used in the benchmark problem proposed by the IASC-ASCE Structural Health Monitoring Task Group. Results have shown great potentials of the proposed approach in its real-world applications.

Orientador: Michael John Brennan / Banca: Fabricio Cesar Lobato de Almeida / Banca: Max de Castro Magalhães / Resumo: Este trabalho apresenta o estudo da propagação de ondas em estruturas unidimensionais, como barras e vigas, bem como a metodologia utilizada para a análise de resposta das ondas quando submetidas a descontinuidades estruturais. A motivação deste projeto é o Monitoramento da Integridade Estrutural, SHM, técnica utilizada em engenharia para detectar a presença de falhas em estruturas mecânicas em vários tipos de indústrias como: civis, automobilísticas, aeronáuticas, evitando, assim, problemas futuros e gastos monetários. Existem diversas técnicas para a aplicação de SHM, uma delas utiliza a propagação de ondas. A utilização de ondas é uma ferramenta bastante procurada por empresas atualmente por ser uma técnica não destrutiva e por caracterizar descontinuidades geométricas. Ondas elásticas dispersam sua energia quando encontram uma descontinuidade, portanto, é possível observar o que acontece nesta divisão através dos coeficientes de reflexão e transmissão. Neste contexto, estes coeficientes são modelados e estudados em duas situações: com ondas longitudinais guiadas por barras e com ondas de flexão guiadas por vigas. Neste trabalho, são modelados diferentes tipos de falhas com arranjos de elementos básicos da mecânica: massa, mola e amortecedor. Os dois tipos de ondas submetidas a esses elementos possuem características específicas observadas inclusive no modelamento matemático. Adicionalmente, elaboram-se estruturas com descontinuidade geométrica para aplicação e correlação dos modelos previamente desenvolvidos visando uma relação de frequências de excitação necessárias para qualificação de diferentes formas de descontinuidades localizada para estrutura de material definido / Abstract: This work presents a study on wave propagation in one-dimensional structures, such as rods and beams, and analyses the effects of structural discontinuities on wave motion. The motivation of this project is the Structural Health Monitoring (SHM), technique used in engineering to detect the presence of damage in mechanical structures in several types of industries like: civil, automobile, aeronautical, thus, avoiding future problems and financial costs. There are several techniques for SHM application, and some of them use wave propagation. The use of waves is a tool sought by companies as a non-destructive technique and for being able to characterise geometric discontinuities. Elastic waves scatter their energy when they reach a discontinuity, and this is characterised by the reflection and transmission coefficients of the discontinuity. In this context, these coefficients are studied for two situations: with longitudinal waves guided by rods and with bending waves guided by beams. In this work, two different types of damage are modelled through basic mechanical elements such as mass, spring and damper. Additionally, structures with geometric discontinuity are investigated and compared with the models previously developed in order to gain physical insight into their dynamic behaviour / Mestre

Structural Health Monitoring (SHM) is concerned with the analysis of aerospace, mechanical and civil systems with the objective of identifying damage at its onset. In civil engineering applications, damage may be defined as any change in the structural properties that hinders the current or future performance of that system. This is the premise on which vibration-based techniques are based. Vibration-based methods exploit the response measured directly on the system to solve the SHM assignment. However, also fluctuations in the external conditions may induce changes in the structural properties. For these reasons, the SHM problem is ideally suited to be solved within the context of statistical pattern recognition, which is the discipline concerned with the automatic classification of objects into categories. Within the statistical pattern recognition based SHM framework, the structural response is portrayed by means of a compact representation of its main traits, called damage sensitive features (dsf). In this dissertation, two typologies of dsf are studied: the first type is extracted from the response of the system by means of digital signal processes alone, while the other is obtained by making use of a physical model of the system. In both approaches, the effects of external conditions are accounted for by modeling the damage sensitive features as random variables. While the first method uses outlier analysis tools and delivers a method optimally apt to perform the task of damage detection within the short-term horizon, the second approach, being model-based, allows for a deeper characterization of damage, and it is then more suited for long-term monitoring purposes. In the dissertation, an approach is also proposed that allows the use of the statistical pattern recognition framework when there is limited availability of data to model the damage sensitive features. All proposed methodologies are validated both numerically and experimentally.

Recent years have seen a concurrent development of new sensor technologies and high-fidelity modeling capabilities. At the junction of these two topics lies an interesting opportunity for real-time system monitoring and damage assessment of structures. During monitoring, measurements from a structure are used to learn the parameters and equations characterizing a physics-based model of the system; thus enabling damage identification. Since monitored quantities are physical, these methods offer precious insight into the damage state of the structure (localization, type of damage and its extent). Furthermore, one obtains a model of the structure in its current condition, an essential element in predicting the future behavior of the structure and enabling adequate decision-making procedures. This dissertation focuses more specifically on solving some of the challenges associated with the use of online Bayesian learning algorithms, also called sequential filtering algorithms, for damage detection and characterization in nonlinear structural systems. A major challenge regarding online Bayesian filtering algorithms lies in achieving good accuracy for large dimensional systems and complex nonlinear non-Gaussian systems, where non-Gaussianity can arise for instance in systems which are not globally identifiable. In the first part of this dissertation, we show that one can derive algorithmic enhancements of filtering techniques, mainly based on innovative ways to reduce the dimensionality of the problem at hand, and thus obtain a good trade-off between accuracy and computational complexity of the learning algorithms. For instance, for particle filtering techniques (sampling-based algorithms) subjected to the so-called curse of dimensionality, the concept of Rao-Blackwellisation can be used to greatly reduce the dimension of the sampling space. On the other hand, one can also build upon nonlinear Kalman filtering techniques, which are very computationally efficient, and expand their capabilities to non-Gaussian distributions. Another challenge associated with structural health monitoring is the amount of uncertainties and variabilities inherently present in the system, measurements and/or inputs. The second part of this dissertation aims at demonstrating that online Bayesian filtering algorithms are very well-suited for SHM applications due to their ability to accurately quantify and take into account these uncertainties in the learning process. First, these algorithms are well-suited to address ill-conditioned problems, where not all parameters can be learnt from the available noisy data, a problem which frequently arises when considering large dimensional nonlinear systems. Then, in the case of unknown stochastic inputs, a method is derived to take into account in this sequential filtering framework unmeasured stationary excitations whose spectral properties are known but uncertain.

Bridge monitoring studies indicate that the quasi-static response of a bridge, while dependent on various input forces, is affected predominantly by variations in temperature. In many structures, the quasi-static response can even be approximated as equal to its thermal response. Consequently, interpretation of measurements from quasi-static monitoring requires accounting for the thermal response in measurements. Developing solutions to this challenge, which is critical to relate measurements to decision-making and thereby realize the full potential of SHM for bridge management, is the main focus of this research. This research proposes a data-driven approach referred to as temperature-based measurement interpretation (TB-MI) approach for structural performance evaluation of bridges based on continuous bridge monitoring. The approach characterizes and predicts thermal response of structures by exploiting the relationship between temperature distributions across a bridge and measured bridge response. The TB-MI approach has two components - (i) a regression-based thermal response prediction (RBTRP) methodology and (ii) an anomaly detection methodology. The RBTRP methodology generates models to predict real-time structural response from distributed temperature measurements. The anomaly detection methodology analyses prediction error signals, which are the differences between predicted and real-time response to detect the onset of anomaly events. In order to generate realistic data-sets for evaluating the proposed TB-MI approach, this research has built a small-scale truss structure in the laboratory as a test-bed. The truss is subject to accelerated diurnal temperature cycles using a system of heating lamps. Various damage scenarios are also simulated on this structure. This research further investigates if the underlying concept of using distributed temperature measurements to predict thermal response can be implemented using physics-based models. The case study of Cleddau Bridge is considered. This research also extends the general concept of predicting bridge response from knowledge of input loads to predict structural response due to traffic loads. Starting from the TB-MI approach, it creates an integrated approach for analyzing measured response due to both thermal and vehicular loads. The proposed approaches are evaluated on measurement time-histories from a number of case studies including numerical models, laboratory-scale truss and full-scale bridges. Results illustrate that the approaches accurately predicts thermal response, and that anomaly events are detectable using signal processing techniques such as signal subtraction method and cointegration. The study demonstrates that the proposed TB-MI approach is applicable for interpreting measurements from full-scale bridges, and can be integrated within a measurement interpretation platform for continuous bridge monitoring.