Identification method of underground disease body based on 3D ground penetrating radar and PSO-ELM: a case study of Jinniu district of Chengdu

doi:10.13474/j.cnki.11-2246.2024.0810

Abstract

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

CLC Number:

XIE Xiaoguo, LUO Bing, HUANG Changbing, ZHANG Yuling, YANG Shengbo. Identification method of underground disease body based on 3D ground penetrating radar and PSO-ELM: a case study of Jinniu district of Chengdu[J]. Bulletin of Surveying and Mapping, 2024, 0(8): 54-59.

References

[1] 聂俊丽,李法滨,张磊,等.路基病害及检测技术研究综述[J].公路交通科技(应用技术版),2019,15(2): 35-38.
[2] 肖敏,陈昌彦,贾辉,等.金属管线对探地雷达探测道路地下病害的干扰[J].物探与化探,2016,40(5): 1046-1050.
[3] 赵镨.三维探地雷达在城市地下管线及周边土体病害探测中的应用[J].测绘通报,2016(S1): 73-76.
[4] 许献磊,杨峰,乔旭,等.基于GPR的城市道路地下病害差值检测方法研究[J].科学技术与工程,2016,16(12): 83-88.
[5] 贾辉,陈昌彦,白朝旭,等.城市道路病害检测中常见干扰源探地雷达图像特征分析[J].工程勘察,2012,40(11): 86-92.
[6] 王大为,吕浩天,汤伏蛟,等.三维探地雷达道路隐性病害检测分析与数字化技术综述[J].中国公路学报,2023,36(3): 1-19.
[7] 韩海航,莫佳笛,周春鹏,等.基于注意力融合的三维探地雷达道路病害检测[J].计算机工程与设计,2022,43(9): 2669-2677.
[8] PHAM M T,LEFōVRE S.Buried object detection from B-scan ground penetrating radar data using Faster-RCNN[J].IEEE,2018.DOI:10.1109/IGARSS.2018.8517683.
[9] ZHOU Haoqiu,FENG Xuan,ZHANG Yan,et al.Combination of support vector machine and H-alpha decomposition for subsurface target classification of GPR[C]//Proceedings of the 17th International Conference on Ground Penetrating Radar (GPR).Rapperswil,Switzerland: IEEE,2018: 1-4.
[10] 徐昕军,勾妍妍,杨峰.基于探地雷达与概率神经网络的城市道路路基病害预警模型研究[J].科学技术与工程,2017,17(17): 118-124.
[11] 黄宏伟,李庆桐.基于深度学习的盾构隧道渗漏水病害图像识别[J].岩石力学与工程学报,2017,36(12): 2861-2871.
[12] 张建智.城市道路病害特征地质雷达正演模拟及快速识别[J].科学技术与工程,2020,20(14): 5499-5505.
[13] 施龙青,董晨磊,衡培国,等.煤矿突水水源判别的PCA-PSO-ELM模型[J].中国科技论文,2021,16(9): 919-924.
[14] 王林生,黄长兵,朱键,等.基于PSO-ELM的水平井自喷期“多段式” 产量预测方法: 以玛湖油田百口泉组致密砾岩油藏为例[J].科学技术与工程,2023,23(5): 1931-1936.

[1]	HE Zhengwei, SUN Bingyuan. An indoor positioning method based on geomagnetic sequence-assisted correction for PSO-PF [J]. Bulletin of Surveying and Mapping, 2024, 0(7): 17-23.
[2]	BI Lingyu, SUN Chengzhi, QIAO Shen. Surface subsidence monitoring and predictive analysis in Hexi area of Nanjing based on SBAS-InSAR and MA-PSO-BP [J]. Bulletin of Surveying and Mapping, 2024, 0(4): 48-53,82.
[3]	ZHAO Zhen, HUANG Yong, FENG Kun. Research and application of 3D ground penetrating in road detection [J]. Bulletin of Surveying and Mapping, 2024, 0(2): 148-152.
[4]	HU Rongming, LI Xin, JING Xia, WU Jianqiang, WEI Qingbo. Application of YOLOv7 in GPR B-Scan image interpretation [J]. Bulletin of Surveying and Mapping, 2023, 0(8): 29-33.
[5]	ZHAO Yiwen, YANG Tianran, MIAO Lili, MA Yuntao. Helmert variance component estimation method for GPS+BDS and UWB integrated positioning [J]. Bulletin of Surveying and Mapping, 2023, 0(8): 91-96.
[6]	DOU Zhanshu, CUI Lizhen, HONG Jinxiang, SHI Mingquan. Improved OSELM localization algorithm in dynamic environment of underground coal mine [J]. Bulletin of Surveying and Mapping, 2023, 0(5): 90-95.
[7]	JIA Xiuli. GPS multipath effect snow depth estimation by Gaussian process regression-assisted [J]. Bulletin of Surveying and Mapping, 2022, 0(7): 78-82.
[8]	LIU Guochao, PENG Weiping, YANG Shuihua, HU Zhouwen. Detection and application of urban road disease based on ground penetrating radar+3D measuring endoscope [J]. Bulletin of Surveying and Mapping, 2022, 0(7): 134-137.
[9]	ZHOU Chunxia, WEN Yaohui, CHEN Yiming, WANG Shaoxian, LIU Yong, LI Mingci. Accuracy verification and analysis of ground-based real aperture radar GPRI [J]. Bulletin of Surveying and Mapping, 2021, 0(4): 28-32.
[10]	FU Huanian, ZHANG Xudong, WANG Yanlin. Analysis of the area difference of different coordinate systems: taking Ningbo city as an example [J]. Bulletin of Surveying and Mapping, 2021, 0(3): 109-112,155.
[11]	CHEN Zeyuan. Using Gauss projection to realize band change of AutoCAD topographic map [J]. Bulletin of Surveying and Mapping, 2020, 0(12): 138-143.
[12]	LIU Xiaohua. Research on GIS engineering coordinate system [J]. Bulletin of Surveying and Mapping, 2020, 0(11): 158-162.
[13]	GUO Yinggang, LI Zongchun, LI Guangyun, LIU Zhonghe. Progress and prospect of engineering control network for particle accelerator [J]. Bulletin of Surveying and Mapping, 2020, 0(1): 136-141.
[14]	FU Yongheng, ZHANG Lijie. Random error analysis and prediction of MEMS gyroscope based on allan variance and SVR [J]. 测绘通报, 2019, 0(5): 88-92.
[15]	YU Shasha, YU Jie, ZHU Teng, WANG Yanbing. A Modified Classification Algorithm of MCSM/H-PSO of Fully Polarimetric SAR Image [J]. 测绘通报, 2018, 0(11): 53-57.