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Non Destructive Testing of Concrete Railway Tracks by Impulse Response and Impact Echo Techniques
(2003)
Acoustic emission and ultrasonic monitoring of a prestressed concrete bridge in its final years
(2021)
Many European bridges built in the 1950s, 60s and 70s must be re-placed in the next decade due to structural deficiencies, insufficient load capacity or other issues. However, the existing bridges must be used for another couple of years even if flaws and damages already have been detected.
In Germany, several prestressed concrete bridges have been instrumented with acoustic emission detection systems to detect wire breaks and to provide early warning signs before failure. To evaluate and interpret the consequences of wire breaks additional instrumentation and accompanying measures as finite element modeling are required.
At a bridge in southern Germany we have complemented such a system with active ultrasonic monitoring. Repeated ultrasonic measurements are evaluated with a very sensible algorithm called coda wave interferometry. This method, inspired by seismology, has been shown to deliver early warning signs in lab experiments. Large volumes of concrete can be monitored with a limited network of ultrasonic transducers. We will report on the installation, capabilities, and lim-itations as well as first results.
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 presentation includes detailed information about the various tools under consideration, their connection and first results of our research.