Assessment and management of aging concrete structures in nuclear power plants require a more systematic approach than simple reliance on existing code margins of safety. Structural health monitoring of concrete structures aims to understand the current health condition of a structure based on heterogeneous measurements to produce high-confidence actionable information regarding structural integrity that supports operational and maintenance decisions. This ongoing research project is seeking to develop a probabilistic framework for a health diagnosis and prognosis of aging concrete structures in a nuclear power plant that is subjected to physical, chemical, environmental, and mechanical degradation. The proposed framework consists of four elements: monitoring, data analytics, uncertainty quantification, and prognosis. This report focuses on degradation caused by alkali-silica reaction (ASR). Controlled specimens were prepared to develop accelerated ASR degradation. Different monitoring techniques (i.e., thermography, digital image correlation, mechanical deformation measurements, nonlinear impact resonance acoustic spectroscopy, and vibro-acoustic modulation) were used to detect the damage caused by ASR. Heterogeneous data from multiple techniques were used for damage diagnosis and prognosis and quantification of the associated uncertainty using a Bayesian network approach. Additionally, the MapReduce technique has been demonstrated with synthetic data. This technique can be used in the future to handle large amounts of observation data obtained from online monitoring of realistic structures. vi vii EXECUTIVE SUMMARYOne challenge for the current fleet of light water reactors in the United States is age-related degradation of their passive assets that include concrete, cables, piping, and the reactor pressure vessel. As the current fleet of nuclear power plants (NPPs) continues to operate up to 60 years or beyond, it is important to understand the current and the future health condition of passive assets under different operating conditions that would support operational and maintenance decisions. To ensure long-term safe and reliable operation of the current fleet, the U.S. Department of Energy's Office of Nuclear Energy funds the Light Water Reactor Sustainability Program to develop the scientific basis for extending operation of commercial light water reactors beyond the current license extension period.Among the different passive assets of interest in NPPs, concrete structures are investigated in this research project. Reinforced concrete structures found in NPPs can be grouped into four categories: (1) primary containment, (2) containment internal structures, (3) secondary containments/reactor buildings, and (4) spent fuel pool and cooling towers. These concrete structures are affected by a variety of degradation mechanisms that are related to chemical, physical, and mechanical causes and to irradiation. Age-related degradation of concrete results in gradual microstructural changes (e.g., slow hydration, crys...

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This research is seeking to develop a probabilistic framework for health diagnosis and prognosis of aging concrete structures in nuclear power plants that are subjected to physical, chemical, environment, and mechanical degradation. The proposed framework consists of four elements: monitoring, data analytics, uncertainty quantification, and prognosis. The current work focuses on degradation caused by ASR (alkali-silica reaction). Controlled concrete specimens with reactive aggregate are prepared to develop accelerated ASR degradation. Different monitoring techniques -infrared thermography [1], digital image correlation (DIC), mechanical deformation measurements, nonlinear impact resonance acoustic spectroscopy [2] (NIRAS), and vibro-acoustic modulation [3] (VAM) -are studied for ASR diagnosis of the specimens. Both DIC and mechanical measurements record the specimen deformation caused by ASR gel expansion. Thermography is used to compare the thermal response of pristine and damaged concrete specimens and generate a 2-D map of the damage (i.e., ASR gel and cracked area), thus facilitating localization and quantification of damage. NIRAS and VAM are two separate vibration-based techniques that detect nonlinear changes in dynamic properties caused by the damage. The diagnosis results from multiple techniques are then fused using a Bayesian network, which also helps to quantify the uncertainty in the diagnosis. Prognosis of ASR degradation is then performed based on the current state of degradation obtained from diagnosis, by using a coupled thermo-hydro-mechanical-chemical (THMC) model [4] for ASR degradation. This comprehensive approach of monitoring, data analytics, and uncertainty-quantified diagnosis and prognosis will facilitate the development of a quantitative, risk-informed framework that will support continuous assessment and risk management of structural health and performance. Acknowledgement:

Current structural health monitoring techniques face significant difficulties in damage diagnosis for heterogeneous materials such as concrete. In this article, we propose a damage diagnosis methodology that overcomes such difficulties and is capable of detecting and localizing the damage, using linear swept and harmonic vibration tests with swept and sinusoidal waveforms. The proposed damage detection procedure uses novel features based on singular value decomposition of linearly swept waveform test data. The latter singular vectors of the intact basis are found to be sensitive to the presence of damage. The damage localization uses a K-factor metric calculated using sinusoidal waveform test data. The K-factor is used to measure the deviation of a signal away from a sinusoid. The damage diagnosis methodology is demonstrated and validated using experiments on thin concrete slabs with drilled holes and thicker concrete blocks with alkali–silica reaction damage.