GPR identification of voids inside concrete based on the support vector machine algorithm

Abstract

Voids inside reinforced concrete, which affect structural safety, are identified from ground penetrating radar (GPR) images using a completely automatic method based on the support vector machine (SVM) algorithm. The entire process can be characterized into four steps: (1) the original SVM model is built by training synthetic GPR data generated by finite difference time domain simulation and after data preprocessing, segmentation and feature extraction. (2) The classification accuracy of different kernel functions is compared with the cross-validation method and the penalty factor (c) of the SVM and the coefficient (σ2) of kernel functions are optimized by using the grid algorithm and the genetic algorithm. (3) To test the success of classification, this model is then verified and validated by applying it to another set of synthetic GPR data. The result shows a high success rate for classification. (4) This original classifier model is finally applied to a set of real GPR data to identify and classify voids. The result is less than ideal when compared with its application to synthetic data before the original model is improved. In general, this study shows that the SVM exhibits promising performance in the GPR identification of voids inside reinforced concrete. Nevertheless, the recognition of shape and distribution of voids may need further improvement.