A Demonstration of Concrete Structural Health Monitoring Framework for Degradation due to Alkali-Silica Reaction [electronic resource].
- Washington, D.C. : United States. Office of the Assistant Secretary for Nuclear Energy, 2016. and Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
- Physical Description:
- 48 pages : digital, PDF file
- Additional Creators:
- Idaho National Laboratory, United States. Office of the Assistant Secretary for Nuclear Energy, and United States. Department of Energy. Office of Scientific and Technical Information
- Restrictions on Access:
- Free-to-read Unrestricted online access
- 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 health diagnosis and prognosis of aging concrete structures in a nuclear power plant that is subjected to physical, chemical, environment, and mechanical degradation. The proposed framework consists of four elements: monitoring, data analytics, uncertainty quantification and prognosis. This report focuses on degradation caused by ASR (alkali-silica reaction). Controlled specimens were prepared to develop accelerated ASR degradation. Different monitoring techniques – thermography, digital image correlation (DIC), mechanical deformation measurements, nonlinear impact resonance acoustic spectroscopy (NIRAS), and vibro-acoustic modulation (VAM) -- were used to detect the damage caused by ASR. Heterogeneous data from the multiple techniques was used for damage diagnosis and prognosis, and quantification of the associated uncertainty using a Bayesian network approach. Additionally, MapReduce technique has been demonstrated with synthetic data. This technique can be used in future to handle large amounts of observation data obtained from the online monitoring of realistic structures.
- Published through SciTech Connect., 04/01/2016., "inl/ext--16-38602", and Sankaran Mahadevan; Vivek Agarwal; Kyle Neal; Paromita Nath; Yanqing Bao; Guowei Cai; Peter Orme; Douglas Adams; David Kosson.
- Funding Information:
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