Information

  • Publication Type: Miscellaneous Publication
  • Workgroup(s)/Project(s): not specified
  • Date: 2022
  • Keywords: ddep learning, georadar

Abstract

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.

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BibTeX

@misc{gilmutdinov-2022-aomlbwug,
  title =      "Assesment of material layers in building walls using
               GeoRadar",
  author =     "Ildar Gilmutdinov and Michael Wimmer",
  year =       "2022",
  abstract =   "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.",
  keywords =   "ddep learning, georadar",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2022/gilmutdinov-2022-aomlbwug/",
}