Radioactive waste systems and structures (RWSS) are safety-critical facilities in need of monitoring over prolonged periods of time. Structural health monitoring (SHM) is an emerging technology that aims at monitoring the state of a structure through the use of networks of permanently mounted sensors. SHM technologies have been developed primarily within the aerospace and civil engineering communities. This paper addresses the issue of transitioning the SHM concept to the monitoring of RWSS and evaluates the opportunities and challenges associated with this process. Guided wave SHM technologies utilizing structurally-mounted piezoelectric wafer active sensors (PWAS) have a wide range of applications based on both propagating-wave and standing-wave methodologies. Hence, opportunities exist for transitioning these SHM technologies into RWSS monitoring. However, there exist certain special operational conditions specific to RWSS such as: radiation field, caustic environments, marine environments, and chemical, mechanical and thermal stressors. In order to address the high discharge of used nuclear fuel (UNF) and the limited space in the storage pools the U.S. the Department of Energy (DOE) has adopted a “Strategy for the Management and Disposal of Used Nuclear Fuel and High-Level Radioactive Waste” (January 2013). This strategy endorses the key principles that underpin the Blue Ribbon Commission’s on America’s Nuclear Future recommendations to develop a sustainable program for deploying an integrated system capable of transporting, storing, and disposing of UNF and high-level radioactive waste from civilian nuclear power generation, defense, national security, and other activities. This will require research to develop monitoring, diagnosis, and prognosis tools that can aid to establish a strong technical basis for extended storage and transportation of UNF. Monitoring of such structures is critical for assuring the safety and security of the nation’s spent nuclear fuel until a national policy for closure of the nuclear fuel cycle is defined and implemented. In addition, such tools can provide invaluable and timely information for verification of the predicted mechanical performance of RWSS (e.g. concrete or steel barriers) during off-normal occurrence and accident events such as the tsunami and earthquake event that affected Fukushima Daiichi nuclear power plant. The ability to verify the conditions, health, and degradation behavior of RWSS over time by applying nondestructive testing (NDT) as well as development of nondestructive evaluation (NDE) tools for new degradation processes will become challenging. The paper discusses some of the challenges associated to verification and diagnosis for RWSS and identifies SHM technologies which are more readily available for transitioning into RWSS applications. Fundamental research objectives that should be considered for the transition of SHM technologies (e.g., radiation hardened piezoelectric materials) for RWSS applications are discussed. The paper ends with summary, conclusions, and suggestions for further work.
A computational framework for modeling complex sensor network data using graph signal processing and graph neural networks in structural health monitoring
