Concrete highway bridges are important elements of our country's transportation infrastructure; however, only few studies that address their seismic behavior using data collected from instrumented structures are available in the literature. This gap of knowledge impairs full exploitation of structural health monitoring techniques for seismic damage assessment, and improvement of design recommendations. This research is particularly concerned with curved concrete box-girder highway bridges, whose seismic behavior is still widely unexplored due to lack of field monitoring data. By taking advantage of vibration records collected during six earthquake events at the West Street on Ramp, a curved concrete box-girder highway bridge located in Anaheim, California, this research aims at advancing knowledge about the seismic behavior of these bridges. Modal identification of the bridge during the earthquakes is conducted, and sensitivity analysis is carried out to reconcile the observed dynamic characteristics of the bridge with the behavior of its structural elements. Data collected from an instrumented large-scale bridge specimen during shaking table tests are also analyzed to gain insight about the response of the bridge bents during the earthquakes, and propose a strategy to model their seismic behavior. Information from modal identification and the shaking table tests analyses are instrumental in developing a nonlinear finite element model of the bridge, calibrated employing a multistage finite element model updating strategy. In order to evaluate the significance of using the structural-health-monitoring-informed structural model obtained, seismic performance assessment through incremental dynamic analysis is conducted, and results are compared with the predicted performance estimated with a conventional finite element model of the bridge. By advancing knowledge about the seismic behavior of concrete highway bridges, this research may ultimately contribute to improve structural health monitoring practices and design guidelines for this type of structures.

This master thesis is the result of a collaborative work between EMBRAER and the Escola Politécnica da USP for the study of structural health monitoring (SHM) techniques using sensors applied to aircraft structures. The goal was to develop classification techniques to discriminate between different events arising in the aircraft structure during tests; in the short term, improving the current SHM system used by EMBRAER, based on acoustic emission and, in the long term, fostering the development of a fully distributed system. As a result of studying classification methods for immediate use, we developed two techniques: the Spectral Similarity and a Support Vector Machines (SVM) classifier. Both are unsupervised solutions, due to the unlabeled nature of the data provided. The two solutions were delivered as a final product to EMBRAER for prompt use in the existing SHM system. By studying distributed solutions for future implementations, we developed a detection algorithm based on adaptive techniques. The main result was a special initialization for a maximum likelihood (ML) detector that yields an exponential decay rate in the error probability to a nonzero steady state, using adaptive diffusion estimation in a distributed sensor network. The nodes that compose the network must decide, locally, between two concurrent hypotheses concerning the environment state where they are inserted, using local measurements and shared estimates coming from their neighbors. The exponential performance does not depend on the adaptation step size value, provided it is sufficiently small. The results concerning this distributed detector were published in the journal IEEE Signal Processing Letters. / Esta dissertação de mestrado é o resultado de um trabalho colaborativo entre a EMBRAER e a Escola Politécnica da USP no estudo de técnicas de monitoramento do estado de saúde de estruturas (Structural Health Monitoring - SHM) utilizando sensores em estruturas aeronáuticas. O objetivo foi desenvolver técnicas de classificação para discriminar entre diferentes eventos que surgem em estruturas aeronáuticas durante testes; para o curto prazo, aperfeiçoando o atual sistema de SHM utilizado pela EMBRAER, baseado em emissão acústica e, no longo prazo, fomentando o desenvolvimento de um sistema completamente distribuído. Como resultado do estudo de métodos de classificação para uso imediato, desenvolvemos duas técnicas: a Similaridade Espectral e um classificador que utiliza Support Vector Machines (SMV). Ambas as técnicas são soluções não-supervisionadas, devido a natureza não rotulada dos dados fornecidos. As duas soluções foram entregues como um produto final para a EMBRAER para pronta utilização em seu atual sistema de SHM. Ao estudar soluções completamente distribuídas para futuras implementações, desenvolvemos um algoritmo de detecção baseado em técnicas adaptativas. O principal resultado foi uma inicialização especial para um detector de máxima verossimilhança (maximum likelihood - ML) que possui uma taxa de decaimento exponencial na probabilidade de erro até um valor não nulo em regime estacionário, utilizando estimação adaptativa em uma rede distribuída. Os nós que compõem a rede devem decidir, localmente, entre duas hipóteses concorrentes com relação ao estado do ambiente onde eles estão inseridos, utilizando medidas locais e estimativas compartilhadas vindas de nós vizinhos. O desempenho exponencial não depende do valor do passo de adaptação, se este for suficientemente pequeno. Os resultas referentes a este detector distribuído foram publicados na revista internacional IEEE Signal Processing Letters.

Submitted by ANA PAULA ALVES GUIMARÃES null (annapaulasun@gmail.com) on 2017-02-04T20:28:04Z No. of bitstreams: 1 dissertação-final.pdf: 4630588 bytes, checksum: 8c2806b890a1b7889d8d26b4a11e97bf (MD5) / Approved for entry into archive by LUIZA DE MENEZES ROMANETTO (luizamenezes@reitoria.unesp.br) on 2017-02-07T13:18:18Z (GMT) No. of bitstreams: 1 guimaraes_apa_me_ilha.pdf: 4630588 bytes, checksum: 8c2806b890a1b7889d8d26b4a11e97bf (MD5) / Made available in DSpace on 2017-02-07T13:18:18Z (GMT). No. of bitstreams: 1 guimaraes_apa_me_ilha.pdf: 4630588 bytes, checksum: 8c2806b890a1b7889d8d26b4a11e97bf (MD5) Previous issue date: 2016-12-20 / O monitoramento da condição estrutural é uma área que vem sendo bastante estudada por permitir a construção de sistemas que possuem a capacidade de identificar um determinado dano em seu estágio inicial, podendo assim evitar sérios prejuízos futuros. O ideal seria que estes sistemas tivessem o mínimo de interferência humana. Sistemas que abordam o conceito de aprendizagem têm a capacidade de serem autômatos. Acredita-se que por possuírem estas propriedades, os algoritmos de aprendizagem de máquina sejam uma excelente opção para realizar as etapas de identificação, localização e avaliação de um dano, com capacidade de obter resultados extremamente precisos e com taxas mínimas de erros. Este trabalho tem como foco principal utilizar o algoritmo support vector machine no auxílio do monitoramento da condição de estruturas e, com isto, obter melhor exatidão na identificação da presença ou ausência do dano, diminuindo as taxas de erros através das abordagens da aprendizagem de máquina, possibilitando, assim, um monitoramento inteligente e eficiente. Foi utilizada a biblioteca LibSVM para análise e validação da proposta. Desta forma, foi possível realizar o treinamento e classificação dos dados promovendo a identificação dos danos e posteriormente, empregando as predições efetuadas pelo algoritmo, foi possível determinar a localização dos danos na estrutura. Os resultados de identificação e localização dos danos foram bastante satisfatórios. / Structural health monitoring (SHM) is an area that has been extensively studied for allowing the construction of systems that have the ability to identify damages at an early stage, thus being able to avoid serious future losses. Ideally, these systems have the minimum of human interference. Systems that address the concept of learning have the ability to be autonomous. It is believed that by having these properties, the machine learning algorithms are an excellent choice to perform the steps of identifying, locating and assessing damage with ability to obtain highly accurate results with minimum error rates. This work is mainly focused on using support vector machine algorithm for monitoring structural condition and, thus, get better accuracy in identifying the presence or absence of damage, reducing error rates through the approaches of machine learning. It allows an intelligent and efficient monitoring system. LIBSVM library was used for analysing and validation of the proposed approach. Thus, it was feasible to conduct training and classification of data promoting the identification of damages. It was also possible to locate the damages in the structure. The results of identification and location of the damage was quite satisfactory.

Identifying material parameters in composite plates is a necessary first step in a variety of structural applications. For example, understanding the material parameters of carbon fiber composite is important in investigating sensor and actuator placement on micro-air-vehicle wings for control and wing morphing purposes. Knowing the material parameters can also help examine the health of composite structures and detect wear or defects. Traditional testing methods for finding material parameters such as stiffness and damping require multiple types of experiments such as tensile tests and shaker tests. These tests are not without complications. Methods such as tensile testing can be destructive to the test specimens while use of strain gages and accelerometers can be inappropriate due to the lightweight nature of the structures. The proposed inverse problem testing methods using digital image correlation via high speed cameras can potentially eliminate the disadvantages of traditional methods as well as determine the required material parameters including stiffness and damping by conducting only one type of experiment. These material parameters include stiffness and damping for both isotropic and orthotropic materials, and ply angle layup specifically for carbon fiber materials. A finite element model based on the Kirchoff-Love thin plate theory is used to produce theoretical data for comparison with experimental data collected using digital image correlation. Shaker experiments are also carried out using digital image correlation to investigate the modal frequencies as validation of the results of the inverse problem. We apply these techniques first to an aluminum plate for which material parameters are known to test the performance and efficiency of the method. We then apply the method to a composite plates to determine not only these parameters, but also the layup angle. The inverse problem successfully estimates the Young's modulus and damping for the aluminum material. In addition, the vibration analysis produces consistent resonance frequencies for the first two modes for both theoretical and experimental data. However, carbon fiber plates present challenges due to limitations of the Kirchoff-Love plate theory used as the underlining theoretical model for the finite element approximation used in the inverse problem, resulting in a persistent mismatch of resonance frequencies in experimental data. / Graduation date: 2012

An automated method in damage state assessment of reinforced concrete columns for the purpose of establishing a rapid and quantitative post-earthquake safety and structural evaluation procedure is proposed. Several techniques from the fields of computer vision and image processing are employed in order to develop a set of methods capable of automatically detecting spalled regions on the surface of reinforced concrete columns as well as the properties of cracks and spalled regions on these surfaces. The resulting properties of the observed visible damage on the reinforced concrete column surfaces are then utilized to automatically estimate the existing condition and safety of the column. The damage state is quantified according to the maximum drift capacity of the column. The methods proposed in this research were implemented in a Microsoft Visual Studio .NET environment, and tested on real images of damaged columns. The test results indicated that the methods could automatically detect spalled regions and retrieve the properties of spalling and cracks on reinforced concrete column surfaces in images or video frames, and further, that this retrieved information could be accurately translate to a meaningful assessment of the column's existing damage state in the form of the maximum drift capacity.

There is shift in the Architectural / Engineering / Construction and Facility Management (AEC&FM) industry toward performance-driven projects. Assuring good performance requires efficient and reliable performance control processes. However, the current state of the AEC&FM industry is that control processes are inefficient because they generally rely on manually intensive, inefficient, and often inaccurate data collection techniques. Critical performance control processes include progress tracking and dimensional quality control. These particularly rely on the accurate and efficient collection of the as-built three-dimensional (3D) status of project objects. However, currently available techniques for as-built 3D data collection are extremely inefficient, and provide partial and often inaccurate information. These limitations have a negative impact on the quality of decisions made by project managers and consequently on project success. This thesis presents an innovative approach for Automated 3D Data Collection (A3dDC). This approach takes advantage of Laser Detection and Ranging (LADAR), 3D Computer-Aided-Design (CAD) modeling and registration technologies. The performance of this approach is investigated with a first set of experimental results obtained with real-life data. A second set of experiments then analyzes the feasibility of implementing, based on the developed approach, automated project performance control (APPC) applications such as automated project progress tracking and automated dimensional quality control. Finally, other applications are identified including planning for scanning and strategic scanning.

This study focuses on developing structural health monitoring techniques to detect damage in deepwater risers subjected to vortex-induced vibration (VIV), and studying vibration control strategies to extend the service life of offshore structures. Vibration-based damage detection needs both responses from the undamaged and damaged deepwater risers. Because no experimental data for damaged deepwater risers is available, a model to predict the VIV responses of deepwater risers with given conditions is needed, which is the forward problem. In this study, a new three dimensional (3D) analytical model is proposed considering coupled VIV (in-line and cross-flow) for top-tensioned riser (TTR) with wake oscillators. The model is verified by direct numerical simulations and experimental data. The inverse problem is to detect damage using VIV responses from the analytical models with/without damage, where the change between dynamic properties obtained from riser responses represents damage. The inverse problem is performed in two steps: blind identification and damage detection. For blind identification, a wavelet modified second order blind identification (WMSOBI) method and a complex WMSOBI (CWMSOBI) method are proposed to extract modal properties from output only responses for standing and traveling wave vibration, respectively. Numerical simulations and experiments validate the effectiveness of proposed methods. For damage detection, a novel weighted distribution force change (WDFC) index (for standing wave) and a phase angle change (PAC) index (for traveling wave) are proposed and proven numerically. Experiments confirm that WDFC can accurately locate damage and estimate damage severity. Furthermore, a new fatigue damage estimation method involving WMSOBI, S-N curve and Miner's rule is proposed and proven to be effective using field test data. Vibration control is essential to extend the service life and enhance the safety of offshore structures. Literature review shows that semi-active control devices are potentially a good solution. A novel semi-active control strategy is proposed to tune the damper properties to match the dominant frequency of the structural response in real-time. The effectiveness of proposed strategy in vibration reduction for deepwater risers and offshore floating wind turbines is also validated through numerical studies.

There is shift in the Architectural / Engineering / Construction and Facility Management (AEC&FM) industry toward performance-driven projects. Assuring good performance requires efficient and reliable performance control processes. However, the current state of the AEC&FM industry is that control processes are inefficient because they generally rely on manually intensive, inefficient, and often inaccurate data collection techniques. Critical performance control processes include progress tracking and dimensional quality control. These particularly rely on the accurate and efficient collection of the as-built three-dimensional (3D) status of project objects. However, currently available techniques for as-built 3D data collection are extremely inefficient, and provide partial and often inaccurate information. These limitations have a negative impact on the quality of decisions made by project managers and consequently on project success. This thesis presents an innovative approach for Automated 3D Data Collection (A3dDC). This approach takes advantage of Laser Detection and Ranging (LADAR), 3D Computer-Aided-Design (CAD) modeling and registration technologies. The performance of this approach is investigated with a first set of experimental results obtained with real-life data. A second set of experiments then analyzes the feasibility of implementing, based on the developed approach, automated project performance control (APPC) applications such as automated project progress tracking and automated dimensional quality control. Finally, other applications are identified including planning for scanning and strategic scanning.

Traditional naval vessels with steel structures have the benefit of large safety factors and a distinct material endurance limit. However, as performance requirements and budget constraints rise, the demand for lighter weight vessels increases. Reducing the mass of vessels is commonly achieved by the use of aluminum or composite structures, which requires closer attention to be paid to crack initiation and propagation. It is rarely feasible to require a lengthy inspection process that removes the vessel from service for an extended amount of time. Structural health monitoring (SHM), involving continuous measurement of the structural response to an energy source, has been proposed as a step towards condition-based maintenance. Furthermore, using a passive monitoring system with an array of sensors has several advantages: monitoring can take place in real-time using only ambient noise vibrations and neither deployment of an active source nor visual access to the inspected areas are required. Passive SHM on a naval vessel is not without challenge. The structures of ships are typically geometrically complex, causing scattering, multiple reflections, and mode conversion of the propagating waves in the vessel. And rather than a distinct and predictable input produced by controlled active sources, the vibration sources are hull impacts, smaller waves, and even onboard machinery and activity. This research summarizes findings from data collected onboard a Navy vessel and presents recommendations data processing techniques. The intent is to present a robust method of passive structural health monitoring for such a vessel using only ambient vibrations recordings.

Condition assessment and safety verification of existing bridges and decisions as to whether bridge posting is required are addressed through analysis, load testing, or a combination of methods. Bridge rating through structural analysis is by far the most common procedure for rating existing bridges. The American Association of State Highway and Transportation Officials (AASHTO) Manual for Bridge Evaluation (MBE), First Edition permits bridge capacity ratings to be determined through allowable stress rating (ASR), load factor rating (LFR) or load and resistance factor rating (LRFR); the latter method is keyed to the AASHTO LRFD Bridge Design Specifications, which is reliability-based and has been required for the design of new bridges built with federal findings since October, 2007. A survey of current bridge rating practices in the United States has revealed that these three methods may lead to different ratings and posting limits for the same bridge, a situation that carries serious implications with regard to the safety of the public and the economic well-being of communities that may be affected by bridge postings or closures. To address this issue, a research program has been conducted with the overall objective of providing recommendations for improving the process by which the condition of existing bridge structures is assessed. This research required a coordinated program of load testing and finite element analysis of selected bridges in the State of Georgia to gain perspectives on the behavior of older bridges under various load conditions. Structural system reliability assessments of these bridges were conducted and bridge fragilities were developed for purposes of comparison with component reliability benchmarks for new bridges. A reliability-based bridge rating framework was developed, along with a series of recommended improvements to the current bridge rating methods, which facilitate the incorporation of various in situ conditions of existing bridges into the bridge rating process at both component and system levels. This framework permits bridge ratings to be conducted at three levels of increasing complexity to achieve the performance objectives, expressed in the terms of reliability, that are embedded in the LRFR option of the AASHTO Manual of Bridge Evaluation. This research was sponsored by the Georgia Department of Transportation, and has led to a set of Recommended Guidelines for Condition Assessment and Evaluation of Existing Bridges in Georgia.