# Assesment of material layers in building walls using GeoRadar

arXiv: https://arxiv.org/abs/2208.12064 · HTML: https://mlsanity.com/paper/2208.12064/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 20 references that are in the map.
- First posted on arXiv 25 Aug 2022.

## Builds on (most cited first, 2 of 2)

- [Deep Learning Inversion of Electrical Resistivity Data](https://mlsanity.com/paper/1904.05265/) (2019)
- [GPRInvNet: Deep Learning-Based Ground Penetrating Radar Data Inversion for Tunnel Lining](https://mlsanity.com/paper/1912.05759/) (2019)

## Abstract (arXiv)

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.