Depth detection of subsurface voids in concrete-filled steel tubular (CFST) structure using percussion and decision tree. (15th October 2020)

• Record Type: Journal Article
• Authors: Chen, Dongdong; Montano, Victor; Huo, Linsheng; Song, Gangbing
• Is Part Of: Measurement. Volume 163(2020)
• Publication Date: 2020-10-15
• Subjects: Void detection -- Concrete-filled steel tubular structures (CFST) -- Decision tree -- Machine learning -- Percussion; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.107869
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A novel percussion method for the depth detection of subsurface voids is proposed. The decision tree algorithm is used in the percussion method. Power spectrum density is used for extracting the attributes of decision trees. The average correction rate of 100 repeated predictions is up to 96.33%. 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.