[V-379] RISK ASSESSMENT ON DEFECTED CONCRETE STRUCTURE BY USING HAMMERING SOUND TEST
Keywords:Risk Assessment, Hammering Sound Test, Convolutional Neural Network
RISK ASSESSMENT ON DEFECTED CONCRETE STRUCTURE BY USING HAMMERING SOUND TEST
Hammering sound test using a rotary hammer is one of the most popular concrete soundness inspection methods, known for its feasibility and the reasonable cost. However, it is highly dependent on inspectors’ experiences of differentiating the reflected sound. In order to improve the efficiency of evaluation, a method utilizing a convolutional neuron network will be discussed in this study. An optimized convolutional neuron network (CNN) model based on data from hammering sound tests using rotary hammer was constructed to evaluate the defect level of concrete structures.
Hammering sound test using a rotary hammer is one of the most popular concrete soundness inspection methods, known for its feasibility and the reasonable cost. However, it is highly dependent on inspectors’ experiences of differentiating the reflected sound. In order to improve the efficiency of evaluation, a method utilizing a convolutional neuron network will be discussed in this study. An optimized convolutional neuron network (CNN) model based on data from hammering sound tests using rotary hammer was constructed to evaluate the defect level of concrete structures.
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