A Machine Learning Scheme for Estimating the Diameter of Reinforcing Bars Using Ground Penetrating Radar

Ground penetrating radar (GPR) is a well-established tool for detecting and locating reinforcing bars (rebars) in concrete structures. However, using GPR to quantify the diameter of rebars is a challenging problem that current processing approaches fail to tackle. To that extent, we have developed a novel machine learning framework that can estimate the diameter of the investigated rebar within the resolution range of the employed antenna. The suggested approach combines neural networks and a random forest regression and has been trained entirely using synthetic data. Although the training process relied only on numerical training sets, nonetheless, the suggested scheme is successfully evaluated with real data indicating the generalization capabilities of the resulting regression. The only required input of the proposed technique is a single A-scan, avoiding laborious measurement configurations and multisensor approaches. In addition, the results are provided in real time and making this method practical and commercially appealing.

Automated summary

The proposed approach combines neural networks and random forest regression, trained using synthetic data, to successfully estimate the diameter of investigated rebars with good generalization capabilities. Advantages of this technique include requiring only a single A-scan as input, providing results in real time, making it practical and commercially appealing.