Challenges in concrete structures health monitoring

Abstract

Structural health monitoring needs to produce actionable information regarding structural integrity that supports operational and maintenance decision making that is individualized for a given structure and its performance objectives. An effective Prognostics and Health Management (PHM) framework for aging structures (subjected to physical, chemical, environmental, and mechanical degradation) needs to integrate four elements - damage modeling, monitoring, data analytics, and uncertainty quantification. This paper briefly discusses available techniques and ongoing challenges in each of these four elements of PHM, in the context of concrete structures. A Bayesian network approach is discussed for integrating heterogeneous information from multi-physics computational models of degradation processes, full-field measurement techniques, big data analytics, and various data and model uncertainty sources. Such a comprehensive framework can quantitatively support decisions regarding appropriate risk management actions.