A Simple Demonstration of Concrete Structural Health Monitoring Framework

Mahadevan, Sankaran¹  Agarwal, Vivek¹  Cai, Guowei¹  Nath, Paromita¹  Bao, Yanqing¹  Bru Brea, Jose Maria¹  Koester, David¹  Adams, Douglas¹  Kosson, David¹ 

[1] Idaho National Lab. (INL), Idaho Falls, ID (United States)

关键词: concrete structures;    damage modeling;    Data analysis;    structural health monitoring;    Uncertainty quantification;   

DOI  :  10.2172/1235197
RP-ID  :  INL/EXT--15-34729
PID  :  OSTI ID: 1235197

学科分类：材料科学（综合）

美国|英语

来源: SciTech Connect

【 摘 要 】

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 subjected to physical, chemical, environment, and mechanical degradation. The proposed framework consists of four elements???damage modeling, monitoring, data analytics, and uncertainty quantification. This report describes a proof-of-concept example on a small concrete slab subjected to a freeze-thaw experiment that explores techniques in each of the four elements of the framework and their integration. An experimental set-up at Vanderbilt University???s Laboratory for Systems Integrity and Reliability is used to research effective combination of full-field techniques that include infrared thermography, digital image correlation, and ultrasonic measurement. The measured data are linked to the probabilistic framework: the thermography, digital image correlation data, and ultrasonic measurement data are used for Bayesian calibration of model parameters, for diagnosis of damage, and for prognosis of future damage. The proof-of-concept demonstration presented in this report highlights the significance of each element of the framework and their integration.