A damage zone detection method in concrete hydraulic structures based on multi-frequency ultrasonic characteristics.

To avoid severe threats to the safety of people's lives and property caused by the ultimate collapsing of damaged concrete hydraulic structures and to overcome the technical bottleneck related to the low precision level of conventional acoustic non-destructive testing methods in distinguishing between different structural characteristics of damaged areas, this article focuses on the shortcomings in accuracy and detection capability of current acoustic technologies for damage detection in concrete structures. Different damaged areas of concrete structures exhibit distinct characteristics of frequency acoustic signals, and these multi-frequency ultrasonic characteristics were studied in this research to improve the detection method of damaged areas in concrete hydraulic structures. First, a damage area detection model that can synchronously reflect the non-smooth surface and multi-layer structural characteristics of the damaged area was established based on the real-state characteristics of concrete damage areas, providing a theoretical basis for the fine detection of the internal characteristics of concrete structures. Subsequently, an acoustic response feature function for the damaged area was constructed based on the multi-frequency information of acoustic signals, constituting an acoustic response feature extraction method that can effectively distinguish among multiple reflected echo signals. At the same time, by introducing the concept of the damage area recognition feature quantity, a structural damage area recognition method was formed to effectively distinguish between non-damaged and damaged areas in concrete structures, altogether encircling a complete set of the hydraulic concrete structure damage area detection technology system. Finally, the feasibility and superiority of the proposed method were verified through local and global testing experiments. The results indicated that the method proposed in this paper can improve the accuracy and efficiency of detecting damaged areas in concrete structures. The theoretical error was below 10%. The proposed method exhibited stronger adaptability, providing more effective and accurate diagnostic methods for assessing the current state of damaged areas in concrete structures in practice.