The acoustic emission (AE) technique is suitable for monitoring and evaluating hydraulic concrete damage due to its good response to material damage. While continuously advancing conventional AE analysis methods, various advanced digital processing technologies and intelligent algorithms have been applied to deeply explore the damage information and evaluate hydraulic concrete damage. An intelligent framework for evaluating hydraulic concrete damage based on AE has been established, according to the working principle of the AE monitoring system for hydraulic concrete damage. Based on the content involved in this framework, a review is conducted on the current research status of hot topics such as conventional analysis methods, signal processing methods, acoustic source localization (ASL) methods, AE source recognition methods, and deep learning technique applications. The complex characteristics of AE signals of hydraulic concrete damage and the research needs of how to overcome the adverse effects have been summarized, aiming to continuously improve the framework and achieve the construction of an intelligent platform for evaluating hydraulic concrete damage based on AE.

Degradation of cement-based materials with the infrastructure engineering constructed in 20 th century for the end of service–life is increasingly concerned. Acoustic emission (AE) as an intrinsic physical phenomenon of energy emission happening inside material or structure can effectively support a precise sensation of health of material and structure based on its spatiotemporal information. This review briefly introduced the sensing principle and analysis method of AE and summarized the supports from AE to description of degradation process, influence factors, and cement–based material degradation such as alkali aggregate reaction, corrosion induced cracking, fatigue damage, and freeze–thaw spalling. In addition, the potential aspects to enrich AE research on the degradation of cement–based materials were also discussed via considering the time–dependent and large-scale feature of hydraulic structure.