Percussion-based quasi real-time void detection for concrete-filled steel tubular structures using dense learned features

Abstract

Concrete-filled steel tubular (CFST) structures have been widely used to provide high load-bearing capacity. Percussions are expected to provide simple and effective means of detecting subsurface voids of CFST structures, caused by poor grouting compactness and harsh operating conditions. Existing percussion data processing methods consist of two steps to deal with signals with high sampling rates: heuristic feature extraction and classification. High-dimensional feature representations tend to be selected to achieve visual interpretability and performance, increasing the complexity and computational cost of the entire process. This research aims at developing a computationally efficient end-to-end framework for the percussion-based void detection of CFST structures suitable for mobile computing devices available in the field, while maintaining the performance and a certain degree of visual interpretability. The proposed approach is based on the dense learned features; First, a classifier that processes raw percussion sound signals is developed using dilated convolutions and global max-pooling. The class activation mapping (CAM) technique is then applied to convert the classifier into the dense feature extractor. Feature visualization for the experimental data shows that (i) the classifier listens to the “echo” of the percussion sound carefully, rather than the impact sound itself, and (ii) the classification results depend on both the vibration frequency and its damping ratio. Finally, the feature extractor is extended to quasi real-time void detection and classification, where the processing time duration for a single 0.1-second (50,000-point) percussion signal is 8.28 ms without leveraging advanced computing capabilities of graphics processing units, and the average testing accuracy for the fivefold cross validation is 99.81%. The results demonstrate the potential of the proposed approach for improving the efficiency of inspections.