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To provide better means for a safe and effective monitoring of cemented waste packages including prediction tools to assess the future integrity development during pre-disposal activities, several digital tools are evaluated and improved in the frame of the EC funded project PREDIS. Safety enhancement (e. g. less exposure of testing personnel) and cost effectiveness are part of the intended impact. The work includes but is not limited to inspection methods such as muon imaging, wireless sensors integrated into waste packages as well as external package and facility monitoring such as remote fiber optical sensors. The sensors applied will go beyond radiation monitoring and include proxy parameters important for long term integrity assessment (e. g. internal pressure). Sensors will also be made cost effective to allow the installation of much more sensors compared to current practice. The measured data will be used in digital twins of the packages for specific simulations (geochemical, integrity) providing a prediction of future behavior. Machine Learning techniques trained by the characterization of older packages will help to connect the models to the actual data. All data (measured and simulated) will be collected in a joint data base and connected to a decision framework to be used at actual facilities. The paper includes detailed information about the various tools under consideration, their connection and first results of our research.
Previous work has shown that ultrasonic monitoring using externally applied or embedded transducers and imaging methods based on coda wave interferometry are able to detect subtle changes in concrete elements. In this study, a limited number of embedded transducers has been used to monitor changes in several 12 m long two-span concrete beams subjected to point or linear loads until failure.
The ultrasonic results showed the high sensitivity to stress changes and the nonlinear character of the associated effects. However, the ultrasonic features showed a very good correlation to several conventional monitoring parameters. For higher loads (significant amount of cracking), the technique had to be modified to cope with large wave velocity variations and high decorrelation compared to the reference signal.
Using a very simple imaging procedure, the 2D stress field inside the beam has been visualized including inhomogeneities and artifact at places where cracking occurred at higher loads. The technique has the potential to be included in real time monitoring systems.
Multifaceted developments for pre-disposal management of low and intermediate level radioactive waste are undertaken in the EC funded project PREDIS. In work package 7, innovations in cemented waste handling and pre-disposal storage are advanced by testing and evaluating. To provide better means for safe and effective monitoring of cemented waste packages including prediction tools to assess the future integrity development during pre-disposal activities, several monitoring and digital tools are evaluated and improved. Both safety enhancement (e. g. less exposure of testing personnel) and cost effectiveness are part of the intended impact. Current methods to pack, store, and monitor cemented wastes are identified, analysed and improved. Innovative integrity testing and monitoring techniques applied to evaluate and demonstrate package and storage quality assurance are further developed. The work includes but is not limited to inspection methods such as muon imaging, wireless sensors integrated into waste packages as well as external package and facility monitoring such as remote fiber optical sensors. The sensors applied will go beyond radiation monitoring and include proxy parameters important for long term integrity assessment (e. g. internal pressure). The measured data will be used in digital twins of the packages for specific simulations (geochemical, integrity) providing a prediction of future behaviour. Machine Learning techniques trained by the characterization of older packages will help to connect the models to the actual data. As data handling, processing and fusion are crucial for both the monitoring and the digital twin model, all data (measured and simulated) will be collected in a joint data base and connected to a decision framework. Finally, the implementation of the improved techniques will be tested at actual facilities. An overview about various relevant tools, their interconnections, and first research results will be shown.