Structural Health Monitoring (SHM) is employed to track and evaluate damage and deterioration during regular operation as well as after extreme events for aerospace, mechanical and civil structures. A complete SHM system incorporates performance metrics, sensing, signal processing, data analysis, transmission and management for decision-making purposes. Damage detection in the context of SHM can be successful by employing a collection of robust and practical damage detection methodologies that can be used to identify, locate and quantify damage or, in general terms, changes in observable behavior. In this study, different damage detection methods are investigated for global condition assessment of structures. First, different parametric and non-parametric approaches are re-visited and further improved for damage detection using vibration data. Modal flexibility, modal curvature and un-scaled flexibility based on the dynamic properties that are obtained using Complex Mode Indicator Function (CMIF) are used as parametric damage features. Second, statistical pattern recognition approaches using time series modeling in conjunction with outlier detection are investigated as a non-parametric damage detection technique. Third, a novel methodology using ARX models (Auto-Regressive models with eXogenous output) is proposed for damage identification. By using this new methodology, it is shown that damage can be detected, located and quantified without the need of external loading information. Next, laboratory studies are conducted on different test structures with a number of different damage scenarios for the evaluation of the techniques in a comparative fashion. Finally, application of the methodologies to real life data is also presented along with the capabilities and limitations of each approach in light of analysis results of the laboratory and real life data.

Master of Science / Department of Civil Engineering / Hayder A. Rasheed / Damage detection and structural health monitoring are topics that have been receiving increased attention from researchers around the world. A structure can accumulate damage during its service life, which in turn can impair the structure’s safety. Currently, visual inspection is performed by experienced personnel in order to evaluate damage in structures. This approach is affected by the constraints of time and availability of qualified personnel. This study aims to facilitate damage evaluation and detection in concrete bridge girders without the need for visual inspection while minimizing field measurements. Simply-supported beams with different geometric, material and cracking parameters (cracks’ depth, width and location) were modeled in three phases using Abaqus finite element analysis software in order to obtain stiffness values at specified nodes. In the first two phases, beams were modeled using beam elements. Phase I included beams with a single crack, while phase II included beams with up to two cracks. For phase III, beams with a single crack were modeled using plane stress elements. The resulting damage databases from the three phases were then used to train two types of Artificial Neural Networks (ANNs). The first network type (ANNf) solves the forward problem of providing a health index parameter based on the predicted stiffness values. The second network type (ANNi) solves the inverse problem of predicting the most probable cracking pattern, where a unique analytical solution is not attainable. In phase I, beams with 3, 5, 7 and 9 stiffness nodes and a single crack were modeled. For the forward problem, ANNIf had the geometric, material and cracking parameters as inputs and stiffness values as outputs. This network provided excellent prediction accuracy measures (R2 > 99%). For the inverse problem, ANNIi had the geometric and material parameters as well as stiffness values as inputs and the cracking parameters as outputs. Better prediction accuracy measures were achieved when more stiffness nodes were utilized in the ANN modeling process. It was also observed that decreasing the number of required outputs immensely improved the quality of predictions provided by the ANN. This network provided less accurate predictions (R2 = 68%) compared to ANNIf, however, ANNIi still provided reasonable results, considering the non-uniqueness of this problem’s solution. In phase II, beams with 9 stiffness nodes and two cracks were modeled following the same procedure. ANNIIf provided excellent results (R2 > 99%) while ANNIIi had less accurate (R2 = 65%) but still reasonable predictions. Finally, in phase III, simple span beams with 3, 5, 7 and 9 stiffness nodes and a single crack were modeled using plane stress elements. ANNIIIf (R2 > 99%) provided excellent results while ANNIIIi had less accurate (R2 = 65%) but still reasonable predictions. Predictions in this phase were very accurate for the crack depth and location parameters (R2 = 97% and 99%, respectively). Further inspection showed that ANNIIIi provided more accurate predictions when compared with ANNIi. Overall, the obtained results were reasonable and showed good agreement with the actual values. This indicates that using ANNs is an excellent approach to damage evaluation, and a viable approach to obtain the, analytically unattainable, solution of the inverse damage detection problem.

This thesis investigates the behaviour and failure of simple aerospace type carbon fibre composite structures. The work focused on Acoustic Emission (AE) wave propagation in composite materials, the use of advanced AE techniques to detect, characterise and locate damage and their application to the monitoring of buckling and impact failure in large scale structures. The novelty in the work is highlighted below:

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This thesis examines the role of Acoustic Emission (AE) as a non-destructive testing technique for concrete structures. The work focuses on the development of experimental techniques and data analysis methods for the detection, location and assessment of AE from the failure of plain and reinforced concrete specimens. Four key topics are investigated:

Orientador: Jozué Vieira Filho / Resumo: As técnicas de detecção de danos baseadas na Impedância Eletromecânica (EMI) baseiam-se na capacidade dos materiais piezoeléctricos em atuar como sensores e atuadores e contribuem para o desenvolvimento de sistemas de Structural Health Monitoring (SHM). As técnicas clássicas baseadas na EMI utilizam um transdutor Pb-Lead Zirconate Titanite (PZT ) ligado à estrutura monitorada e medem a assinatura de impedância do PZT. No entanto, as técnicas baseadas na EMI dependem de diferentes fatores, como faixa de frequência, número de PZT, temperatura ambiente, tipo de estrutura, entre outros. Assim, para demonstrar a eficácia dos métodos baseados na EMI, faz-se necessário realizar experimentos práticos, o que não é uma tarefa trivial, considerando tais fatores. Portanto, neste trabalho são estudados e propostos procedimentos para criar modelos numéricos, usando elementos finitos (FE), de técnicas baseadas na EMI usando o software PZFlex®. Além disso, os modelos desenvolvidos são usados para propor uma técnica inovadora de compensação de temperatura em sistemas baseados na EMI. Foram modeladas, simuladas e analisadas algumas estruturas clássicas como placa-de-alumínio/PZT e tubo-de-aço/PZT. Os resultados das simulações foram comparados com os equivalentes obtidos com modelos experimentais reais e mostraram-se fortemente correlacionados, indicando que o modelo proposto pode ser uma ferramenta poderosa para o desenvolvimento de técnicas de SHM baseadas na EMI. Foram realizadas simulações ... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The damage detection techniques based on the Electromechanical Impedance (EMI) rely on the ability of piezoelectric materials in acting as sensors and actuators and contribute to the development of Structural Health Monitoring (SHM) systems. The classical EMI-based techniques use a Pb-Lead Zirconate Titanite (PZT) transducer bonded to the monitored structure and measure the impedance signature of the PZT. However, the techniques based on EMI depend on different factors, such as frequency range, number of PZTs, environmental temperature, type of structure, among others. Thus, to demonstrate the effectiveness of EMI-based methods, it is necessary to carry out practical experiments, which is not a trivial task considering such factors. Therefore, in this work, it is studied and proposed procedures to create numerical models of techniques based on the EMI, using finite elements (FE) and the PZFlex® software. In addition, the developed models are used to propose an innovative temperature compensation technique for EMI-based systems. Some classical structures were modelled, simulated and analyzed, such as aluminum plate/PZT and steel pipe/PZT. The results of the simulations were compared with the equivalents obtained by experimental models and showed to be strongly correlated, indicating that the proposed model can be a powerful tool for the development of EMI-based SHM techniques. Simulations were performed to analyze the behavior of the signatures under the effect of temperature,... (Complete abstract click electronic access below) / Doutor

Nesta tese doutoral estudam-se formas de resolver o problema de detecção de dano em estruturas a partir da aplicação de técnicas de inteligência computacional e da resposta dinâmica da estrutura. Duas opções para a formulação do problema são consideradas. Primeiro, um problema de otimização é estabelecido a partir da minimização da diferença entre os parâmetros dinâmicos experimentais da estrutura na condição com dano e aqueles calculados utilizando um modelo de elementos finitos que representa tal condição. Diferentes técnicas metaheurísticas (algoritmos genéticos, particle swarm optimization, evolução diferencial), algumas em versões com adaptação de parâmetros, são empregadas. Estuda-se, ainda, a formulação do problema de otimização como um com múltiplos objetivos. Uma nova forma de avaliar o desempenho de uma metodologia de detecção de dano é proposta, que está baseada na capacidade da metodologia para obter um nível determinado de exatidão no cálculo da extensão do dano e na presença de falso-negativos e falso-positivos nos resultados. Segundo, aplicam-se redes neurais para determinar o mapeamento entre os parâmetros dinâmicos experimentais da condição atual da estrutura e a extensão ou posição do dano nesta. Estruturas do tipo viga e treliça foram submetidas a diferentes cenários de dano com o intuito de determinar o desempenho das metodologias propostas. Resultados mostram a habilidade de técnicas de inteligência computacional para detecção de cenários de dano com uns poucos elementos danificados; porém não é possível garantir que as metodologias terão sucesso para o 100% dos casos. Recomenda-se a utilização de técnicas de busca local para melhorar a solução encontrada pelos algoritmos globais. Finalmente, observou-se que se requer da determinação da quantidade mínima de informação a ser utilizada, uma função objetivo adequada e uma alta qualidade nas medições para garantir uma detecção de dano confiável. / This research aims at studying how to solve the damage detection problem by using computational intelligence techniques and the dynamic response of the structure. Two different ways for formulating the solution to the problem are implemented. In first place, an optimization problem is formulated as the minimization of the difference between the experimental dynamic parameters for the current structure and those from a finite element model that represent the damaged condition. Several metaheuristics (genetic algorithms, particle swarm optimization and differential evolution) are used to solve the optimization problem, where most of them present adaptive configurations. The implication of a multi-objective approach is also studied. A new scheme to determine the algorithm´s performance is proposed, which computes three error indicators concerning differences between the real and computed damage extents and the presence of false-positives and false-negatives. In second place, artificial neural networks are used to determine the mapping between the experimental dynamic parameters and either the damage extension (quantification) or the damage position (localization). Different damage scenarios were simulated in beam and truss structures to verify the performance of the proposed methodologies. Results show the ability of computational intelligence techniques to detect damage scenarios with a few damaged elements; however, it is not possible to guarantee a 100% of success. It is suggested to use local search techniques to improve the solution found by the different proposed algorithms. Three main conclusions are the followings: i) it is necessary to determine the minimum quantity of modal data that permits guarantying a reliable damage detection, ii) objective functions plays a very important role to the success of the algorithms and iii) noise prejudice the damage identification process.

Master of Science / Department of Civil Engineering / Hani G. Melhem / This study reports the results of an analytical, experimental and a numerical study (proof of concept study) on a proposed method for extracting the pseudo-free-vibration response of a structure using ambient vibration, usually of a random nature, as a source of excitation to detect any change in the dynamic properties of a structure that may be caused by damage. The structural response contains not only a random component but also a component reflecting the dynamic properties of the structure, comparable to the free vibration for a given initial condition. Structural response to the arbitrary excitation is recorded by one or several accelerometers with a desired data-collection frequency and resolution. The free-vibration response of the structure is then extracted from this data by removing the random component of the response by the method proposed in this study. The features of the free-vibration response of the structure extracted by a suitable method, namely Fast Fourier Transform (FFT) in this study, can be used for change detection. Possible change of the pattern of these features is dominantly linked to the change in dynamic properties of the system, caused by possible damage. To show the applicability of the concept, besides an analytical verification using Newmark’s linear acceleration method, two steel portal frames with different flexural stiffness were made in the steel workshop of the structural laboratory for an experimental study. These structures were also numerically modeled using a finite element software. A wireless accelerometer with a sampling frequency rate of 2046 Hz was affixed on the top of the physical structure, at the same location where the acceleration was recorded for the corresponding numerical model. The physical structure was excited manually by an arbitrary hit and the response of the structure to this excitation, in terms of the acceleration on the top of the structure, was recorded. The pseudo-free-vibration response was extracted and transferred into frequency domain using FFT. The frequency with the largest magnitude which is the fundamental frequency of the structure was traced. This was repeated for several independent excitations and the fundamental frequencies were observed to be the same, showing that the process can correctly identify the natural frequencies of the structure. Similarly, the numerical model was excited and for several base excitation cases, the fundamental frequencies were found to be the same. Considering the acceptable accuracy of the results from the two numerical models in simulating the response of their corresponding physical models, additional numerical models were analyzed to show the consistency and applicability of the proposed method for a range of flexural stiffness and damping ratio. The results confirm that the proposed method can precisely extract the pseudo-free-vibration response of the structures and detect the structural frequencies regardless of the excitation. The fundamental frequency is tied to the stiffness and a larger stiffness leads to a higher frequency, as expected, regardless of the simulated ambient excitation.

Vibration-based damage identification (VBDI) techniques have been developed in part to address the problems associated with an aging civil infrastructure. To assess the potential of VBDI as it applies to highway bridges in Iowa, three applications of VBDI techniques were considered in this study: numerical simulation, laboratory structures, and field structures. VBDI techniques were found to be highly capable of locating and quantifying damage in numerical simulations. These same techniques were found to be accurate in locating various types of damage in a laboratory setting with actual structures. Although there is the potential for these techniques to quantify damage in a laboratory setting, the ability of the methods to quantify low-level damage in the laboratory is not robust. When applying these techniques to an actual bridge, it was found that some traditional applications of VBDI methods are capable of describing the global behavior of the structure but are most likely not suited for the identification of typical damage scenarios found in civil infrastructure. Measurement noise, boundary conditions, complications due to substructures and multiple material types, and transducer sensitivity make it very difficult for present VBDI techniques to identify, much less quantify, highly localized damage (such as small cracks and minor changes in thickness). However, while investigating VBDI techniques in the field, a novel methodology, operational response and waveform analysis (ORWA), was developed to extend the focus of traditional VBDI techniques by correlating bridge damage to operational structural motion. It was found that if the frequency-domain response of the structure can be generated from operating traffic load, the structural response can be animated and used to develop a holistic view of the bridge's response to various automobile loadings. By animating the response of a field bridge, concrete cracking (in the abutment and deck) was correlated with structural motion and problem frequencies (i.e., those that cause significant torsion or tension-compression at beam ends) were identified. Furthermore, a frequency-domain study of operational traffic was used to identify both common and extreme frequencies for a given structure and loading. Finally, a finite element analysis of a structure similar to the field bridge was carried out to supplement and partially verify experimental results. Further work should (1) perfect the process of collecting high-quality operational frequency response data; (2) expand and simplify the process of correlating frequency response animations with damage; and (3) develop efficient, economical, pre-emptive solutions to common damage types identified by ORWA.

The use of composite structures in engineering applications has proliferated over the past few decades due to its distinct advantages namely: high structural performance, corrosion resistance, and high strength/weight ratio. However, they also come with a set of disadvantages, i.e. they are prone to fibre breakage, matrix cracking and delaminations. These types of damage are often invisible and if undetected, could lead to catastrophic failures of structures. Although there are systems to detect such damage, the criticality assessment and prognosis of the damage is often much more difficult to achieve. The research study conducted here resulted in the development of a Structural Health Monitoring (SHM) system for a 2D polymeric composite T-joint, used in maritime structures. The SHM system was found to be capable of not only detecting the presence of multiple delaminations in a composite structure, but also capable of determining the location and extent of all t he delaminations present in the T-joint structure, regardless of the load (angle and magnitude) acting on the structure. The system developed relies on the examination of the strain distribution of the structure under operational loading. This SHM system necessitated the development of a novel pre-processing algorithm - Damage Relativity Assessment Technique (DRAT) along with a pattern recognition tool, Artificial Neural Network (ANN), to predict and estimate the damage. Another program developed - the Global Neural network Algorithm for Sequential Processing of Internal sub Networks (GNAISPIN) uses multiple ANNs to render the SHM system independent to variations in structural loading and capable of estimating multiple delaminations in composite T-joint structures. Upto 82% improvement in detection accuracy was observed when GNAISPIN was invoked. The Finite Element Analysis (FEA) was also conducted by placing delaminations of different sizes at various locations in two structures, a composite beam and a T-joint. Glass Fibre Reinforced Polymer T-joints were then manufactured and tested, thereby verifying the accuracy of the FEA results experimentally. The resulting strain distribution from the FEA was pre-processed by the DRAT and used to trai n the ANN to predict and estimate damage in the structures. Finally, on testing the SHM system developed with strain signatures of composite T-joint structures, subjected to variable loading, embedded with all possible damage configurations (including multiple damage scenarios), an overall damage (location & extent) prediction accuracy of 94.1% was achieved. These results are presented and discussed in detail in this study.