NDT Data Fusion for the Enhancement of Defect Visualization in Concrete

Abstract

Ground penetrating radar (GPR) and active thermography are well known non-destructive testing (NDT) methods for structural visualization and defect detection in concrete. However, for both methods, the probability of detection is strongly depth-dependent and each method suffers from an almost blind region at a specific depth. In this study we propose the use of unsupervised clustering techniques for the fusion of GPR and thermographic phase contrast data to enhance defect visualization in concrete. The evaluation was carried out on the basis of experimental data acquired on laboratory concrete test specimens, which contain inbuilt anomalies varying in shape, material and position. To achieve an optimal fusion of radar depth slices and thermographic phase contrast images along the depth axis, we derive sensitivity curves for both NDT methods and use the probability mass information to further improve the fusion results. Results show that the fuzzy c-means algorithm may contribute to an enhanced detection probability of defects below high density reinforcement. For defects with a concrete cover from 1.5 to 2 cm, the use of weighted clustering is particularly suggested. In general, complex defect types and shapes could be better resolved by using the Gustafson-Kessel algorithm or noise clustering. In addition, we demonstrate the application of the Dempster-Shafer theory to quantitatively evaluate the effectiveness of fused data on the basis of joint mass probability.