Depth detection of subsurface voids in concrete-filled steel tubular (CFST) structure using percussion and decision tree

Abstract

Abstract The void defects significantly threaten the integrity of concrete-filled steel tubular (CFST) structure. Along with detecting the presence of subsurface voids, the knowledge of the geometry of voids can provide meaningful information to determine the overall structural health. This study develops a method to determine the depths of subsurface voids by integrating the percussion technique with a machine learning algorithm. A decision tree model is trained to detect different depths of subsurface voids in a CFST specimen. The percussed sounds from areas with and without subsurface voids were analyzed by using the power spectrum density (PSD). The process was repeated 100 times, and the average correction ratio is up to 96.33%. The results showed that the proposed approach has great potential in subsurface void inspection and evaluation for CFST structures.