Assesment of material layers in building walls using GeoRadar
This addresses the need for accurate, non-invasive building inspection, but it is incremental as it applies existing simulation and neural network methods to a specific domain.
The paper tackled the problem of non-invasive assessment of building wall structures by using a convolutional neural network to predict material layer thicknesses and dielectric properties from GeoRadar data, achieving evaluation on real building data.
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. 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 data collected from real buildings.