Intelligent Identification of Urban Road Hazards by BP Neural Network From 3D-GPR GLCM Attributes

Road collapses seriously threaten human safety in cities. The effective and rapid identification of hidden urban road hazards using 3D ground-penetrating radar (3D-GPR) data is crucial for preventing and controlling road collapse accidents. We propose a novel intelligent identification method for urban road hazards using a backpropagation (BP) artificial neural network based on multiscale gray-level co-occurrence matrix (GLCM) attributes derived from 3D-GPR data. The characteristic differences in multiscale GLCM attributes between hidden hazards and other detected targets are leveraged to construct a road hazard identification model using a BP neural network. This model is applied to identify hazards from 3D-GPR data, enabling rapid localization of hidden urban road hazards. Initially, GPR data are processed using zero-time correction, background suppression, and filtering. Next, labeled data are created using the radar responses of known hazards, wells, pipelines, and strata. Subsequently, after applying gradient enhancement processing to the GPR data, the GLCM attributes are calculated at different scales to distinguish hazards in three-dimensional space from other target bodies. Finally, a neural network model for hidden hazard identification is constructed using a backpropagation (BP) artificial neural network and applied to identify hidden hazards beneath urban roads from GPR measurement data. The results show that the prediction accuracy exceeds 85% using the proposed approach. The combination of multi-scale GLCM attributes and BP neural network is valuable and effective for the detection and identification of hidden hazards beneath urban roads.