Buried pipelines are vulnerable to the threat of corrosion. Hence, they are normally coated with a protective coating to isolate the metal substrate from the surrounding environment with the addition of CP current being applied to the pipeline surface to halt any corrosion activity that might be taking place. With time, this barrier will deteriorate which could potentially lead to corrosion of the pipe. The External Corrosion Direct Assessment (ECDA) methodology was developed with the intention of upholding the structural integrity of pipelines. Above ground indirect inspection techniques such as the DCVG which is an essential part of an ECDA, is commonly used to determine coating defect locations and measure the defect's severity. This is followed by excavation of the identified location for further examination on the extent of pipeline damage. Any coating or corrosion defect found at this stage is repaired and remediated. The location of such excavations is determined by the measurements obtained from the DCVG examination in the form of %IR and subjective inputs from experts which bases their justification on the environment and the physical characteristics of the pipeline. Whilst this seems to be a straight forward process, the factors that comes into play which gave rise to the initial %IR is not fully understood. The lack of understanding with the additional subjective inputs from the assessors has led to unnecessary excavations being conducted which has put tremendous financial strain on pipeline operators. Additionally, the threat of undiscovered defects due to the erroneous nature of the current method has the potential to severely compromise the pipeline's safe continual operation. Accurately predicting the coating defect size (TCDA) and interpretation of the indication signal (%IR) from an ECDA is important for pipeline operators to promote safety while keeping operating cost at a minimum. Furthermore, with better estimates, the uncertainty from the DCVG indication is reduced and the decisions made on the locations of excavation is better informed. However, ensuring the accuracy of these estimates does not come without challenges. These challenges include (1) the need of proper methods for large data analysis from indirect assessment and (2) uncertainty about the probability distribution of quantities. Standard mean regression models e.g. the OLS, were used but fail to take the skewness of the distributions involved into account. The aim of this thesis is thus, to come up with statistical models to better predict TCDA and to interpret the %IR from the indirect assessment of an ECDA more precisely. The pipeline data used for the analyses is based on a recent ECDA project conducted by TWI Ltd. for the Middle Eastern Oil Company (MEOC). To address the challenges highlighted above, Quantile Regression (QR) was used to comprehensively characterise the underlying distribution of the dependent variable. This can be effective for example, when determining the different effect of contributing variables towards different sizes of TCDA (different quantiles). Another useful advantage is that the technique is robust to outliers due to its reliance on absolute errors. With the traditional mean regression, the effect of contributing variables towards other quantiles of the dependent variable is ignored. Furthermore, the OLS involves the squaring of errors which makes it less robust to outliers. Other forms of QR such as the Bayesian Quantile Regression (BQR) which has the advantage of supplementing future inspection projects with prior data and the Logistic Quantile Regression (LQR) which ensures the prediction of the dependent variable is within its specified bounds was applied to the MEOC dataset. The novelty of research lies in the approaches (methods) taken by the author in producing the models highlighted above. The summary of such novelty includes: * The use of non-linear Quantile Regression (QR) with interacting variables for TCDA prediction. * The application of a regularisation procedure (LASSO) for the generalisation of the TCDA prediction model.* The usage of the Bayesian Quantile Regression (BQR) technique to estimate the %IR and TCDA. * The use of Logistic Regression as a guideline towards the probability of excavation * And finally, the use of Logistic Quantile Regression (LQR) in ensuring the predicted values are within bounds for the prediction of the %IR and POPD. Novel findings from this thesis includes: * Some degree of relationship between the DCVG technique (%IR readings) and corrosion dimension. The results of the relationship between TCDA and POPD highlights a negative trend which further supports the idea that %IR has some relation to corrosion. * Based on the findings from Chapter 4, 5 and 6 suggests that corrosion activity rate is more prominent than the growth of TCDA at its median depth. It is therefore suggested that for this set of pipelines (those belonging to MEOC) repair of coating defects should be done before the coating defect has reached its median size. To the best of the Author's knowledge, the process of employing such approaches has never been applied before towards any ECDA data. The findings from this thesis also shed some light into the stochastic nature of the evolution of corrosion pits. This was not known before and is only made possible by the usage of the approaches highlighted above. The resulting models are also of novelty since no previous model has ever been developed based on the said methods. The contribution to knowledge from this research is therefore the greater understanding of relationship between variables stated above (TCDA, %IR and POPD). With this new knowledge, one has the potential to better prioritise location of excavation and better interpret DCVG indications. With the availability of ECDA data, it is also possible to predict the magnitude of corrosion activity by using the models developed in this thesis. Furthermore, the knowledge gained here has the potential to translate into cost saving measures for pipeline operators while ensuring safety is properly addressed.

Ce travail a pour objectif d'étudier la tenue en fatigue de l'alliage de fonderie A356-T6 sous chargement multiaxial. Des essais en fatigue à 106 cycles ont été effectués pour deux rapports de chargements différents à Rσ = 0 et Rσ = -1. La première partie expérimentale est menée sur des éprouvettes issues d'une coulée en ‘V’ avec des défauts naturels de fonderie et des défauts artificiels avec une variation de la microstructure. Suite aux résultats expérimentaux, nous avons montré que la taille des défauts ainsi que la microstructure caractérisée par la SDAS, sont les principaux paramètres qui influencent la limite de fatigue de cet alliage. Par comparaison entre les résultats obtenus à Rσ = 0 et Rσ = -1, il s'avère que la contrainte moyenne joue un rôle primordial sur la sensibilité du matériau à la taille du défaut et à la SDAS. Dans la suite, des modifications ont été menées sur le critère de DSG qui consistent à introduire l’effet de la SDAS au niveau du critère. L’application de ce critère modifié dans un diagramme de Kitagawa pour les différents cas de chargement a montré que l’abattement de la limite de fatigue en fonction de la taille de défaut et de la SDAS est bien décrit. Dans la dernière partie un outil numérique a été développé permettant de simuler la limite de fatigue en partant du procédé de fonderie. Cette démarche est sous forme d’une chaîne de calcul numérique qui permet de simuler la taille de défaut et de la SDAS à partir du procédé de fonderie. Suite à cette simulation, le modèle est capable de prévoir la limite de fatigue en utilisant le critère de DSG modifié. La combinaison entre la loi de Weibull et le critère de DSG permet à la suite de la chaîne de simulation de prévoir ainsi la probabilité de rupture à chaque point de la structure. Nous avons proposé dans cette partie un moule qui permet d’élaborer des éprouvettes avec deux microstructures différentes. Dans cette étude, une deuxième campagne d’essais a été réalisée sur ces éprouvettes afin de valider la simulation numérique sur le moule proposé. Le modèle numérique prévoit raisonnablement bien les résultats expérimentaux obtenus. / This study aims to investigate the fatigue behaviour of A356-T6 aluminum alloy. Experimental fatigue tests at 106 cycles have been performed for two loading ratios: Rσ = 0 and Rσ = -1. The first experimental investigation was conducted on specimens from a ‘V’ wedge casting with natural and artificial defects which provides a variation of the microstructure. Following the experimental results, we have shown that defects characterized by their size and the microstructure characterized by SDAS, are the main parameters that control the fatigue limit. By comparing the results obtained for both loading ratios, it appears that the mean stress has an effect on the sensitivity to the defect size effect and microstructure.The DSG criterion was modified to introduce the effect of SDAS. This improved DSG criterion has been employed to predict the Kitagawa diagram for multiaxial loading for different loading cases. The simulation of the modified DSG criterion showed that the reduction of the fatigue limit with the defect size and SDAS is well described. In the last part a numerical model was developed to perform a simulation of the fatigue limit starting from the casting process. Using this numerical model, we simulated the defect size and SDAS depending on the solidification time, eventually the fatigue limit issimulated using the improved DSG criterion. With combining between Weibull law and modified DSG,we predict the probability of failure at each point of the structure. We proposed in this part a mold which let to obtain samples with two different microstructures. In this study, a second fatigue tests was carried out on these samples to validate the numerical simulation on the proposed mold. It turns out that the numerical model provides reasonably well the obtained experimental results.

Ces travaux de thèse concernent la caractérisation en fatigue à grand nombre de cycles de l’alliage d’aluminium moulé A357-T6 (AS7G06-T6) en présence de défauts de type retassure. Six coulées ont été produites pour étudier deux familles de défauts : les retassures de type cavité et les retassures de type spongieuse avec un grade de nocivité qui varie entre 2 et 4 (selon la norme ASTM E 2422). La limite de fatigue en traction positive (R = 0.1) a été estimée en fonction de chaque type de défaut pour N = 2∙106 cycles. Des essais ont été également menés sur des éprouvettes dégradées en surface avec un défaut artificiel. Le défaut artificiel est caractérisé par une variation très localisée de la morphologie (usinage au FIB et par EDM) représentative de la microporosité interdendritique. La morphologie locale et globale des défauts naturels est étudiée grâce à des simulations numériques aux éléments finis. Les calculs numériques sont conduits sur la géométrie réelle d’un pore reconstruite à partir d’un scan en μ-CT. Deux géométries équivalentes ont été proposées pour approximer la morphologie d’un défaut naturel : une sphère et un ellipsoïde équivalent d’inertie.L’effet de la position du défaut sur l’évolution des champs mécaniques a été également étudié en conduisant des simulations numériques aux éléments finis sur la géométrie réelle du pore. Deux approches ont été proposés pour simuler un diagramme de type Kitagawa-Takahashi: (i) la Mécanique de la Rupture en Élasticité Linéaire (LEFM) (ii) un critère de fatigue detype Defect Stress Gradient (DSG), les deux modèles ont été testés dans le cas d’un amorçage sur un défaut de surface et interne.Une campagne d’essais de fatigue (R = 0.1) a été conduite à l’échelle d’un composant industriel. Une pièce réalisée par fonderie en A357-T6 a été testée dans une configuration saine et dégradée avec des défauts artificiels de surface. Un critère de type DSG, qui prend en compte l’effet du gradient de contrainte à l’échelle macroscopique de la structure, a été proposé.Le modèle a été validé par un calcul aux éléments finis du composant en utilisant une routine de post traitement des données, qui affiche en sortie la cartographie de la taille admissible du défaut de surface en chaque point du modèle simulé. / The purpose of this work is to characterize the high cycle fatigue strength of the A357-T6 (AS7G06-T6) cast aluminum alloy affected by natural casting defects (shrinkages). Six different castings have been manufactured in order to study two shrinkage types: the cavity shrinkages and the sponge shrinkages with a defect grade ranging from 2 to 4 (as defined by the ASTM E2422 standard). High cycle fatigue tests have been carried out in order to estimate the fatigue limit under positive tensile loading (R = 0.1) for each defect type at N = 2∙106 cycles. The effect of the local defect morphology has been investigated performing several fatigue tests on specimens with artificial surface defects characterized by a local morphology modification (FIB and EDM machining). The artificial defect size and morphology is representative of the natural interdendritic porosity. The local and global defect morphology of the natural defects have been studied by means of finite element simulations conducted on the real shrinkage morphology reconstructed from a μ-CT scan. Two equivalent geometries have been proposed to simplify the natural defect morphology: a sphere and an inertia - equivalent ellipsoid.The effect of the defect position on the mechanical filed evolution has also been studied by means of finite element simulations performed on the real defect geometry. The analysis of the Kitagawa-Takahashi diagram has been performedby using two different approaches for both internal and surface defects: (i) the Linear Elastic Fracture Mechanics (LEFM)(ii) the Defect Stress Gradient (DSG) fatigue criterion. An experimental campaign has been performed at a component scale. A casted A357-T6structural component has been tested with two different configurations: as received and degraded with artificial surface defects. A DSG fatigue criterion,taking into account the effect of the stress gradient at the macroscopic scale of the component, has been proposed. The model has been validated by means of finite element simulations using a data post-processing sub-routine that gives as output the critical defect size map at each point of the simulated model.

No description available.