论文标题
使用Georadar的建筑墙中物质层的评估
Assesment of material layers in building walls using GeoRadar
论文作者
论文摘要
用非侵入性方法评估建筑物的结构是一个重要问题。可能的方法之一是使用Georadar通过分析从扫描获得的数据来检查墙壁结构。我们提出了一种数据驱动的方法，以评估壁从其GPR雷达的材料组成。为了生成培训数据，我们使用GPRMAX对扫描过程进行建模。使用仿真数据，我们使用卷积神经网络来预测每层墙壁的厚度和介电特性。我们根据从真实建筑物收集的数据评估了训练有素的模型的概括能力。
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.
