关键词: 探地雷达, 粒子群优化算法, 极限学习机, 病害体识别

Abstract: It is the key to prevent road collapse to accurately identify the types of underground diseases in urban roads. 3D ground penetrating radar (GPR) is the most commonly used road disease detection technology, but its data interpretation is mainly manual interpretation, which has the disadvantages of heavy workload and low recognition accuracy. Taking Jinniu district of Chengdu as an example, this paper proposes a PSO-ELM automatic disease body prediction model based on the analysis of the spectral characteristics of underground disease bodies. Seven characteristic parameters, maximum peak amplitude, maximum trough amplitude, amplitude variance, kurtosis factor, mean square value, spectrum variance and spectrum mean value, are selected as the input of the model. PSO is used to optimize the parameters of the ELM model. The optimized model is used to identify the disease body in the study area. The results show that PSO-ELM model has a disease recognition accuracy of 92.5%, which is significantly better than ELM model and traditional artificial image feature recognition method.

Key words: GPR, PSO, ELM, diseases identification