Application of clustering algorithms to void recognition by 3D ground penetrating radar

• Published in: Frontiers in materials 2023-09, Vol.10
• Main Authors: Zhou, Nanjie, Tang, Jiaming, Weixiong, Li, Huang, Zhiyong, Xiaoning, Zhang
• Format: Article
• Language: English
• Subjects: 3D ground-penetrating radar; automated interpretation; clustering algorithms; multi-channel joint judgment; void detection
• ISSN: 2296-8016; 2296-8016
• DOI: 10.3389/fmats.2023.1239263

The manual interpretation of ground-penetrating radar images is characterised by long interpretation cycles and high staff requirements. The automated interpretation schemes based on support vector machines, digital images, convolutional neural networks and other techniques proposed in recent years mainly detect features from B-scan slices of 3D ground-penetrating radar data, without taking full advantage of the multi-channel acquisition of data from 3D ground-penetrating radar and joint discrimination. This paper proposes a void recognition algorithm based on cluster analysis algorithm, using VRADI algorithm to process 3D ground-penetrating radar B-Scan, using DBSCAN clustering algorithm to divide clusters and remove noise; proposes correlation weighting coefficient W i , j to quantitatively evaluate the degree of correlation of different survey channels, proposes prime relative position coefficient P d indicator to quantitatively evaluate the position similarity, and proposes weighted homocentric overlap coefficient P r indicator to quantify signal similarity. This paper applies the algorithm to carry out physical engineering experiments and uses binary logistic regression analysis to develop a correlation model. The experimental results show that significance of P d and P r are less than 0.05, both of which are important influencing indicators for the determination of the presence or absence of void. With an optimal critical probability of 0.4, the recognition accuracy of VRADI algorithm increases from 71.7% to 92.2%. The VRADI algorithm combined with the cluster analysis algorithm outperforms manual recognition in terms of accuracy (92.2% > 83.9%) and recall (90.5% > 86.9%), and the algorithm has good engineering application value.