Publication

Abstract : A significant increase in existing inspection efforts and the creation of more resilient structures using built-in sensors and systems is essential for accurate and reliable assessment and monitoring of structural conditions in order to minimize the risk of catastrophic failure. The acoustic emission (AE) technique could immensely benefit the damage assessment, real-time checking of structural integrity under operating conditions, and identification of the operational risk factors along with the causes of initiation and development of flaws. Therefore, this article presents a comprehensive overview of AE as a potential damage assessment and localization technique to determine the performance and behavior of reinforced concrete (RC) members along with an overview of the latest research in computational AE damage modeling and machine learning tools to optimize the accurate interpretation of voluminous AE data. The primary aim of this review is to identify specific gaps in the literature pertaining to concrete structures that need immediate attention of the researchers working in this domain. The identification of damage mechanisms based on AE events and parameters covered in this review would enable researchers to identify damage sources nondestructively in situ and produce large data sets for detailed statistical analyses of the effects of small-scale local damages in large-scale concrete structures. This review will also be helpful for practicing engineers to choose between different available AE parameters based on the limitations and advantages of these parameters for specific inspection and maintenance requirements of various types of RC structural elements, loading conditions, and failure.