No description available.

No description available.

No description available.

Marine renewable energy promises to provide a significant contribution to the future electricity supply. It is estimated that 17% of today's UK electricity demand could be generated from wave and tidal sources. The ambition to harvest this resource is in the public interest, as it eases the pressures on energy security, holds the potential to reduce carbon emissions and has the prospect to create a new UK industry sector worth £15 billion. From an engineering perspective, marine energy is one of the least developed renewable energy technologies and has to be regarded as unproven. The reliability of components and devices in the harsh marine environment is one of the main engineering challenges. Reliability assessments and the assurance of acceptable reliability levels are dependant on the adequacy of failure information, which is scantily available for marine energy. This thesis shows that large failure rate uncertainties impede the reliability assessment for wave energy converters and how a suite of experimental, numerical and statistical methods can be applied to improve scarcely available reliability information. The analysis of component load conditions identifies fatigue as failure mode of concern and the fatigue life of mooring lines and marine power cables is quantified in a floating wave energy application. A Bayesian statistical approach and dedicated service-simulation component testing is proposed, and implemented to improve the quality of reliability estimates and to provide relevant data and assurance. The methods presented, along with the results, will assist reliability assessment and design during early development stages, and will inform the prediction of maintenance requirements during operation. Reliable marine energy systems will be the technical enabler for the successful transition of prototype devices to a commercially viable marine energy industry.

Um ensaio de carga dinâmica pode ser efetuado em tubulões, estacas hélice continua, estacas moldadas in situ ou estacas cravadas. Tal ensaio se baseia na Teoria da Propagação de Onda em estacas e visa avaliar a capacidade de carga estática mobilizada a partir do evento dinâmico. A análise do ensaio é feita mediante um programa (p.e. CAPWAP ou DLTWAVE), computacional que leva em conta tanto os sinais de força e velocidade obtidas em campo e o modelo do sistema estaca-solo Este trabalho apresenta uma metodologia para atualização bayesiana dos parâmetros de Smith da teoria de propagação de onda, levando em conta não só os sinais registrados, mas também a informação geotécnica-probabilista prévia sobre estes parâmetros. Uma aproximação semelhante já foi aplicada pelo orientador da pesquisa à estimativa de parâmetros geotécnicos de rochas a partir de medidas de deslocamento em túneis, e de campos de permeabilidade a partir de observações piezométricas em barragens. A técnica proposta está baseada firmemente em conhecimento empírico geotécnico que fornece o ensaio e erro do procedimento usual de comparação de sinais: a atualização bayesiana permite aos parâmetros afetados por incerteza maior ter um ajuste proporcionalmente maior de acordo com observações. O máximo deslocamento elástico (quake), o fator de amortecimento (damping) e a resistência estática unitária, de cada trecho da estaca, são considerados variáveis aleatórias com distribuições prévias estimadas dependendo do local de cravação da estaca. Discute-se e calcula-se a correlação cruzada entre parâmetros diferentes e autocorrelação ao longo de profundidade da estaca. As observações são escolhidas do sinal calculado por um software de análise de sinais de cravação, em espaços regulares ao longo do tempo. Postula-se um modelo linear de observação, pesquisa-se tal linearidade, também, deriva-se uma matriz de sensibilidade (ligando cada parâmetro a cada observação), e estimam-se também as incertezas nas observações (sinais). O resultado final é uma distribuição de parâmetros de Smith ao longo da profundidade de estacas, atualizada de acordo com as observações. A aproximação proposta é aplicada em sinais de um dos momentos da cravação, no final da cravação (\'end of driving\', EOD). / An dynamic loading test can be performed in concrete piles, bored piles, drilled shafts, auger cast-in-place (continuous flight auger) piles. Such essay bases on the Theory of the Propagation of Wave in piles with the aim of evaluate the capacity of static load mobilized from the dynamic event. The analysis of the test is made by a PC program (p.e. CAPWAP or DLTWAVE), that takes into account the signals of force and velocity obtained in field and the pile-soil model. This work presents a methodology for bayesian updating of Smith\'s parameters, taking into account not just the recorded signals, but also the prior geotechnical-probabilistic information about these parameters. A similar approach has already been applied by the senior author to the estimation of rock geotechnical parameters from displacement measurements in tunnels, and of permeability fields in embankment dams from piezometric observations. The proposed technique is deemed more firmly based on sound geotechnical knowledge than the usual trial and error signal matching procedure: bayesian updating allows for parameters affected by larger uncertainty to undergo proportionately larger adjustment according to observations. Quake, damping and static resistance are considered random variables, with prior distributions assessed from information about the geotechnical characteristics of the soils the pile is driven into. Cross-correlation between different parameters and auto-correlation along pile depth are discussed and estimated. Signals measured at some properly selected times are the chosen observations. A linear observation model, linking observations to parameters, is postulated, linearity is investigated, a sensitivity matrix (linking each parameter to each observation) is derived, and uncertainty in the observations (signals) is estimated. The final result is a distribution of Smith\'s parameters along pile depth, updated in accordance with the observations. The proposed approach is tested in end of driving (EOD) signal.

Um ensaio de carga dinâmica pode ser efetuado em tubulões, estacas hélice continua, estacas moldadas in situ ou estacas cravadas. Tal ensaio se baseia na Teoria da Propagação de Onda em estacas e visa avaliar a capacidade de carga estática mobilizada a partir do evento dinâmico. A análise do ensaio é feita mediante um programa (p.e. CAPWAP ou DLTWAVE), computacional que leva em conta tanto os sinais de força e velocidade obtidas em campo e o modelo do sistema estaca-solo Este trabalho apresenta uma metodologia para atualização bayesiana dos parâmetros de Smith da teoria de propagação de onda, levando em conta não só os sinais registrados, mas também a informação geotécnica-probabilista prévia sobre estes parâmetros. Uma aproximação semelhante já foi aplicada pelo orientador da pesquisa à estimativa de parâmetros geotécnicos de rochas a partir de medidas de deslocamento em túneis, e de campos de permeabilidade a partir de observações piezométricas em barragens. A técnica proposta está baseada firmemente em conhecimento empírico geotécnico que fornece o ensaio e erro do procedimento usual de comparação de sinais: a atualização bayesiana permite aos parâmetros afetados por incerteza maior ter um ajuste proporcionalmente maior de acordo com observações. O máximo deslocamento elástico (quake), o fator de amortecimento (damping) e a resistência estática unitária, de cada trecho da estaca, são considerados variáveis aleatórias com distribuições prévias estimadas dependendo do local de cravação da estaca. Discute-se e calcula-se a correlação cruzada entre parâmetros diferentes e autocorrelação ao longo de profundidade da estaca. As observações são escolhidas do sinal calculado por um software de análise de sinais de cravação, em espaços regulares ao longo do tempo. Postula-se um modelo linear de observação, pesquisa-se tal linearidade, também, deriva-se uma matriz de sensibilidade (ligando cada parâmetro a cada observação), e estimam-se também as incertezas nas observações (sinais). O resultado final é uma distribuição de parâmetros de Smith ao longo da profundidade de estacas, atualizada de acordo com as observações. A aproximação proposta é aplicada em sinais de um dos momentos da cravação, no final da cravação (\'end of driving\', EOD). / An dynamic loading test can be performed in concrete piles, bored piles, drilled shafts, auger cast-in-place (continuous flight auger) piles. Such essay bases on the Theory of the Propagation of Wave in piles with the aim of evaluate the capacity of static load mobilized from the dynamic event. The analysis of the test is made by a PC program (p.e. CAPWAP or DLTWAVE), that takes into account the signals of force and velocity obtained in field and the pile-soil model. This work presents a methodology for bayesian updating of Smith\'s parameters, taking into account not just the recorded signals, but also the prior geotechnical-probabilistic information about these parameters. A similar approach has already been applied by the senior author to the estimation of rock geotechnical parameters from displacement measurements in tunnels, and of permeability fields in embankment dams from piezometric observations. The proposed technique is deemed more firmly based on sound geotechnical knowledge than the usual trial and error signal matching procedure: bayesian updating allows for parameters affected by larger uncertainty to undergo proportionately larger adjustment according to observations. Quake, damping and static resistance are considered random variables, with prior distributions assessed from information about the geotechnical characteristics of the soils the pile is driven into. Cross-correlation between different parameters and auto-correlation along pile depth are discussed and estimated. Signals measured at some properly selected times are the chosen observations. A linear observation model, linking observations to parameters, is postulated, linearity is investigated, a sensitivity matrix (linking each parameter to each observation) is derived, and uncertainty in the observations (signals) is estimated. The final result is a distribution of Smith\'s parameters along pile depth, updated in accordance with the observations. The proposed approach is tested in end of driving (EOD) signal.

L'étude probabiliste de sûreté (EPS) parasismique est l'une des méthodologies les plus utiliséespour évaluer et assurer la performance des infrastructures critiques, telles que les centrales nucléaires,sous excitations sismiques. La thèse discute sur les aspects suivants: (i) Construction de méta-modèlesavec les réseaux de neurones pour construire les relations entre les intensités sismiques et les paramètresde demande des structures, afin d'accélérer l'analyse de fragilité. L'incertitude liée à la substitution desmodèles des éléments finis par les réseaux de neurones est étudiée. (ii) Proposition d'une méthodologiebayésienne avec réseaux de neurones adaptatifs, afin de prendre en compte les différentes sourcesd'information, y compris les résultats des simulations numériques, les valeurs de référence fournies dansla littérature et les évaluations post-sismiques, dans le calcul de courbes de fragilité. (iii) Calcul des loisd'atténuation avec les réseaux de neurones. Les incertitudes épistémiques des paramètres d'entrée de loisd'atténuation, tels que la magnitude et la vitesse moyenne des ondes de cisaillement de trente mètres, sontprises en compte dans la méthodologie développée. (iv) Calcul du taux de défaillance annuel en combinantles résultats des analyses de fragilité et de l'aléa sismique. Les courbes de fragilité sont déterminées parle réseau de neurones adaptatif, tandis que les courbes d'aléa sont obtenues à partir des lois d'atténuationconstruites avec les réseaux de neurones. Les méthodologies proposées sont appliquées à plusieurs casindustriels, tels que le benchmark KARISMA et le modèle SMART. / Seismic probabilistic risk assessment (SPRA) is one of the most widely used methodologiesto assess and to ensure the performance of critical infrastructures, such as nuclear power plants (NPPs),faced with earthquake events. SPRA adopts a probabilistic approach to estimate the frequency ofoccurrence of severe consequences of NPPs under seismic conditions. The thesis provides discussionson the following aspects: (i) Construction of meta-models with ANNs to build the relations betweenseismic IMs and engineering demand parameters of the structures, for the purpose of accelerating thefragility analysis. The uncertainty related to the substitution of FEMs models by ANNs is investigated.(ii) Proposal of a Bayesian-based framework with adaptive ANNs, to take into account different sourcesof information, including numerical simulation results, reference values provided in the literature anddamage data obtained from post-earthquake observations, in the fragility analysis. (iii) Computation ofGMPEs with ANNs. The epistemic uncertainties of the GMPE input parameters, such as the magnitudeand the averaged thirty-meter shear wave velocity, are taken into account in the developed methodology.(iv) Calculation of the annual failure rate by combining results from the fragility and hazard analyses.The fragility curves are determined by the adaptive ANN, whereas the hazard curves are obtained fromthe GMPEs calibrated with ANNs. The proposed methodologies are applied to various industrial casestudies, such as the KARISMA benchmark and the SMART model.

There has been increasing interest in evaluating the performance of existing reinforced concrete (RC) bridges just after natural disasters or man-made events especially when the defects are invisible, or in quantifying the improvement after rehabilitations. In order to obtain an accurate assessment of the reliability of a RC bridge, it is critical to incorporate information about its current structural properties, which reflects the possible aging and deterioration. This dissertation proposes to develop an adaptive reliability analysis of RC bridges incorporating the damage detection information obtained from nondestructive testing (NDT). In this study, seismic fragility is used to describe the reliability of a structure withstanding future seismic demand. It is defined as the conditional probability that a seismic demand quantity attains or exceeds a specified capacity level for given values of earthquake intensity. The dissertation first develops a probabilistic capacity model for RC columns and the capacity model can be used when the flexural stiffness decays nonuniformly over a column height. Then, a general methodology to construct probabilistic seismic demand models for RC highway bridges with one single-column bent is presented. Next, a combination of global and local NDT methods is proposed to identify in-place structural properties. The global NDT uses the dynamic responses of a structure to assess its global/equivalent structural properties and detect potential damage locations. The local NDT uses local measurements to identify the local characteristics of the structure. Measurement and modeling errors are considered in the application of the NDT methods and the analysis of the NDT data. Then, the information obtained from NDT is used in the probabilistic capacity and demand models to estimate the seismic fragility of the bridge. As an illustration, the proposed probabilistic framework is applied to a reinforced concrete bridge with a one-column bent. The result of the illustration shows that the proposed framework can successfully provide the up-to-date structural properties and accurate fragility estimates.

No description available.

The goal of the present dissertation is to develop and apply novel and systematic data-driven optimization approaches that can efficiently optimize Additive Manufacturing (AM) systems with respect to targeted properties of final parts. The proposed approaches are capable of achieving sets of process parameters that result in the satisfactory level of part quality in an accelerated manner. First, an Accelerated Process Optimization (APO) methodology is developed to optimize an individual scalar property of parts. The APO leverages data from similar—but non-identical—prior studies to accelerate sequential experimentation for optimizing the AM system in the current study. Using Bayesian updating, the APO characterizes and updates the difference between prior and current experimental studies. The APO accounts for the differences in experimental conditions and utilizes prior data to facilitate the optimization procedure in the current study. The efficiency and robustness of the APO is tested against an extensive simulation studies and a real-world case study for optimizing relative density of stainless steel parts fabricated by a Selective Laser Melting (SLM) system. Then, we extend the idea behind the APO in order to handle multi-objective process optimization problems in which some of the characteristics of the AMabricated parts are uncorrelated. The proposed Multi-objective Process Optimization (m-APO) breaks down the master multi-objective optimization problem into a series of convex combinations of single-objective sub-problems. The m-APO maps and scales experimental data from previous sub-problems to guide remaining sub-problems that improve the solutions while reducing the number of experiments required. The robustness and efficiency of the m-APO is verified by conducting a series of challenging simulation studies and a real-world case study to minimize geometric inaccuracy of parts fabricated by a Fused Filament Fabrication () system. At the end, we apply the proposed m-APO to maximize the mechanical properties of AMabricated parts that show conflicting behavior in the optimal window, namely relative density and elongation-toailure. Numerical studies show that the m-APO can achieve the best trade-off among conflicting mechanical properties while significantly reducing the number of experimental runs compared with existing methods.