Assessment of Material Layers in Building Walls Using GeoRadar

Assessing the structure of a building with non-invasive methods is an important problem. One of the possible approaches is to use GeoRadar to examine wall structures by analyzing the data obtained from the scans. However, so far, the obtained data have to be assessed manually, relying on the experience of the user in interpreting GPR radargrams. We propose a data-driven approach to evaluate the material composition of a wall from its GPR radargrams. In order to generate training data, we use gprMax to model the scanning process. Using simulation data, we use a convolutional neural network to predict the thicknesses and dielectric properties of walls per layer. We evaluate the generalization abilities of the trained model on the data collected from real buildings.

Automated summary

Assessing the structure of a building using non-invasive methods is important. This study proposes a data-driven approach to evaluate the material composition of a wall using GeoRadar scans. A convolutional neural network is trained using simulated data to predict the thicknesses and dielectric properties of each layer in the wall, and the model's generalization abilities are evaluated using real building data.