Cette recherche s’inscrit dans le cadre de la surveillance structurale des enceintes de confinement des centrales nucléaires qu’EDF exploite. L’objectif est de déterminer comment la connaissance de la teneur en eau du béton de ces ouvrages pourrait améliorer les prédictions de leur comportement mécanique, qui sont régulièrement réalisées dans le cadre de la démonstration de sûreté des installations.Dans un premier temps, nous avons étudié comment les incertitudes de mesure d’auscultation se propageaient dans les modèles pour quantifier l’information apportée par la mesure de teneur en eau. Nous avons également considéré les effets de ces incertitudes sur les extrapolations en fin de vie de l’ouvrage. Dans un second temps, nous avons proposé un modèle de surveillance des déformations différées comprenant la teneur en eau comme variable explicative et fondé sur une dépendance linéaire avec les déformations. Cette approche a été validée grâce aux données issues de la maquette VeRCoRs.Nous avons voulu ensuite estimer plus précisément l’exigence métrologique pour qualifier les chaines de mesure in situ de teneur en eau des bétons. Pour cela, nous avons utilisé la méthode de Monte Carlo pour simuler la propagation des incertitudes de mesures, en comparant les performances du modèle que nous proposions avec celles d’une approche d’ingénierie plus classique. / This research takes place within the framework of the structural monitoring of nuclear power plant containments that EDF operates. Our goal is to determine how the knowledge of the concrete water content these structures could improve the predictions of their mechanical behavior, which are periodically undertaken as part of the safety case for the facilities.As a first step, we have studied monitoring data uncertainties propagation through the models, in order to quantify the information brought by the water content measurement. We have also addressed the effects of these uncertainties on the extrapolations to the end of the structure lifetime. In a second phase, we have proposed a delayed deformation monitoring model including water content as an explanatory variable and based on a linear dependence with strains. This approach has been validated with data from the VeRCoRs mock up.Then, we sought to estimate more accurately the metrological requirement to qualify in-situ measurement systems for concrete water content. To do this, we used the Monte Carlo method to simulate the propagation of measurement uncertainties, by comparing the performances of the model that we proposed with those of a more conventional engineering approach.

Depuis plusieurs années l'équipe de recherche S4M se concentre sur une approche technologique de la SHM avec pour objectif la surveillance de systèmes complexes par des capteurs intelligents distribués: le Smart Sensing. L'équipe S4M conduit des travaux d'instrumentation de structures complexes au travers du déploiement de systèmes de surveillance distribués et de recherche de marqueurs de vieillissement par la mesure et l'exploitation de signaux via des capteurs MEMS déployés. Différents domaines ont déjà été adressés avec des travaux conduits conjointement avec des constructeurs aéronautiques. Ce travail de recherche, effectué en partenariat avec le laboratoire LMDC de l'INSA se focalise sur le matériau de type béton renforcé par des plaques composites, comme structure hétérogène nécessitant une surveillance périodique et/ou continue. Un des enjeux est de contrôler la maintenance préventive ou le surdimensionnement par des coefficients de confiance en proposant une méthode de contrôle non destructif. Notre objectif de recherche est de contribuer dans la recherche d'une ou de signature(s) dans des signaux mesurés par des capteurs piezo en réponse à des impulsions générant la propagation d'ondes mécaniques témoignant un vieillissement ou un endommagement de la structure poutre en béton armé. / For several years the research team S4M focuses on a technological approach to SHM with the aim for monitoring of complex systems by intelligent sensors distributed: Smart Sensing. The S4M team led instrumentation complex structures work through the deployment of distributed monitoring systems and search for markers of aging by measuring and operating signals through deployed MEMS sensors. Different areas have already been addressed with the work conducted jointly with aircraft manufacturers. This research, conducted in partnership with the LMDC-INSA laboratory focuses on the concrete like material reinforced composite plates as heterogeneous structure requiring periodic or continuous monitoring. One of the challenges is to control preventive maintenance or oversizing trusted coefficients by providing a non-destructive testing method. Our research goal is to help in the search for a signature in the signals measured by piezo sensors in response to pulses generating propagation of mechanical waves reflecting an aging or damage to the beam structure of reinforced concrete.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第19228号 / 医博第4027号 / 新制||医||1011(附属図書館) / 32227 / 京都大学大学院医学研究科医学専攻 / (主査)教授 武田 俊一, 教授 竹内 理, 教授 髙田 穣 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Wave propagation analysis in 1-D and 2-D composite structures is performed efficiently and accurately through the formulation of a User-Defined Element (UEL) based on the wavelet spectral finite element (WSFE) method. The WSFE method is based on the first order shear deformation theory which yields accurate results for wave motion at high frequencies. The wave equations are reduced to ordinary differential equations using Daubechies compactly supported, orthonormal, wavelet scaling functions for approximations in time and one spatial dimension. The 1-D and 2-D WSFE models are highly efficient computationally and provide a direct relationship between system input and output in the frequency domain. The UEL is formulated and implemented in Abaqus for wave propagation analysis in composite structures with complexities. Frequency domain formulation of WSFE leads to complex valued parameters, which are decoupled into real and imaginary parts and presented to Abaqus as real values. The final solution is obtained by forming a complex value using the real number solutions given by Abaqus. Several numerical examples are presented here for 1-D and 2-D composite waveguides. Wave motions predicted by the developed UEL correlate very well with Abaqus simulations using shear flexible elements. The results also show that the UEL largely retains computational efficiency of the WSFE method and extends its ability to model complex features. An enhanced cross-correlation method (ECCM) is developed in order to accurately predict damage location in plates. Three major modifications are proposed to the widely used cross-correlation method (CCM) to improve damage localization capabilities, namely actuator-sensor configuration, signal pre-processing method, and signal post-processing method. The ECCM is investigated numerically (FEM simulation) and experimentally. Experimental investigations for damage detection employ a PZT transducer as actuator and laser Doppler vibrometer as sensor. Both numerical and experimental results show that the developed method is capable of damage localization with high precision. Further, ECCM is used to detect and localize debonding in a composite material skin-stiffener joint. The UEL is used to represent the healthy case whereas the damaged case is simulated using Abaqus. It is shown that the ECCM successfully detects the location of the debond in the skin-stiffener joint.

The application of piezoelectric sensors such as the ultrasonic transducer has significantly enhanced the fields of nondestructive evaluation (NDE). Their application of piezoelectric materials allows for the sensing of low energy, high frequency acoustic emission (AE) events such as fatigue cracking in metals and delamination in composites. Utilizing the physical characteristics of these AE waves, the location of these structural defects can then be source located by means of time-of-flight trilateration. The real time sensing of such events has led to the field of structural health monitoring (SHM) and has revolutionized NDE. Furthermore, with the application of modern micro-electromechanical system-based (MEMS) technology, the fields of NDE and SHM can be improved greatly, and sensing instrumentation simplified. A novel piezoresistive-based MEMS strain sensor is presented as this improvement to NDE and SHM. The ultrathin silicon membrane-based (USM) strain sensor's ability to capture an AE signal is demonstrated by a Hsu-Nielsen source and shows comparable frequency content to a commercial piezoceramic ultrasonic transducer. To the knowledge of the authors, this makes the USM strain sensor the first known piezoresistive strain sensor capable of recording low energy AE. The novel improvements to NDE and SHM arise from the sensor's low minimum detectable strain and wide frequency bandwidth, enabling a dual-use application of both AE and static strain sensing. The USM sensor's ability to document quasi-static bending is demonstrated and once again compared with an ultrasonic transducer, which provides no significant response. This dual-use application is proposed to effectively combine the uses of both strain and ultrasonic transducer sensor types within one sensor, lending itself novel and useful to NDE and SHM. The potential benefits include enhanced sensitivity, reduced sensor size and cost, and reduced instrumentation complexity. / Master of Science / Visual inspection for cracks and defects has long been staples of assessing structural health throughout human history. These surface imperfections are an obvious hindrance to structural integrity and routine observation and inspection is needed to ensure a structure's safety. With the progression of technology and the discovery of piezoelectric materials, more advanced methods have been devised to detect and source locate not only surface level but sub-surface cracking. This has been accomplished through the use of piezoelectric ultrasonic transducers to monitor the propagation of acoustic emission (AE) vibrations, which are the result of energy redistribution by events such as cracking. The remote monitoring of AE events has led to the growth of the nondestructive evaluation (NDE) field, where these cracks and defects can be located by the detection of their AE source. These transducers, however, are met with limitations in their applications. Operating off the piezoelectric effect allows for a superb response to low energy, high frequency excitation characteristic of AE, but results in no response to quasi-static strain measurements, such as that of a slowly applied bending load on a plate. In the work herein, modern micro-electromechanical system (MEMS) based technology is utilized to devise a sensor capable of both AE and static strain measurements. The dual sensing of both of these measurements can allow for the source location of cracking events along with the monitoring of structure strain, effectively combining the use of two sensors into one. This dual-application use can have a great impact on the evaluation of critical structures like bridges and aircraft and simplify and reduce costs inherent to nondestructive evaluation.

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The focus of this dissertation is aimed at advancing autonomous structural health monitoring. All the research is based on developing the impedance method for monitoring structural health. The impedance technique utilizes piezoelectric patches to interrogate structures of interested with high frequency excitations. These patches are bonded directly to the structure, so information about the health of the structure can be seen in the electrical impedance of the piezoelectric patch. However, traditional impedance techniques require the use of a bulky and expensive impedance analyzer. Research presented here describes efforts to miniaturize the hardware necessary for damage detection. A prototype impedance-based structural health monitoring system, incorporating wireless based communications, is fabricated and validated with experimental testing. The first steps towards a completely autonomous structural health monitoring sensor are also presented. Power harvesting from ambient energy allows a prototype to be operable from a rechargeable power source. Aerospace vehicles are equipped with thermal protection systems to isolate internal components from harsh reentry conditions. While the thermal protection systems are critical to the safety of the vehicle, finding damage in these structures presents a unique challenge. Impedance techniques will be used to detect the standard damage mechanism for one type of thermal protection system. The sensitivity of the impedance method at elevated temperatures is also investigated. Sensors are often affixed to structures as a means of identifying structural defects. However, these sensors are susceptible to damage themselves. Sensor diagnostics is a field of study directed at identifying faulty sensors. The influence of temperature on these techniques is largely unstudied. In this dissertation, a model is generated to identify damaged sensors at any temperature. A sensor diagnostics method is also adapted for use in developed hardware. The prototype used is completely digital, so standard sensor diagnostics techniques are inapplicable. A new method is developed to work with the digital hardware. / Ph. D.