Ultrasonic based concrete defects identification via wavelet packet transform and GA-BP neural network.

ABSTRACT: Concrete is the main material in building. Since its poor structural integrity may cause accidents, it is significant to detect defects in concrete. However, it is a challenging topic as the unevenness of concrete would lead to the complex dynamics with uncertainties in the ultrasonic diagnosis of defects. Note that the detection results mainly depend on the direct parameters, e.g., the time of travel through the concrete. The current diagnosis accuracy and intelligence level are difficult to meet the design requirement for automatic and increasingly high-performance demands. To solve the mentioned problems, our contribution of this paper can be summarized as establishing a diagnosis model based on the GA-BPNN method and ultrasonic information extracted that helps engineers identify

SUBMITTER: Hu T 

PROVIDER: S-EPMC8444079 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature