Review of ground penetrating radar detection technology for attribute characteristics of cracks in road structural layers

ShiLi GUO, Dong LIANG, HongYan ZHANG, WenCai CAI, PengFei TIAN, MingYu YU, YuHang ZHU

Prog Geophy ›› 2025, Vol. 40 ›› Issue (5) : 2172-2186.

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Prog Geophy ›› 2025, Vol. 40 ›› Issue (5) : 2172-2186. DOI: 10.6038/pg2025JJ0049

Review of ground penetrating radar detection technology for attribute characteristics of cracks in road structural layers

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Abstract

Road cracks, a common type of road distress, compromise structural integrity, accelerate deterioration, induce secondary disasters, and shorten service life. Ground Penetrating Radar (GPR) is currently the primary technological method for long-distance, engineering-scale, efficient, and non-destructive detection of the internal development of road cracks. The paper systematically reviews the formation mechanisms of road cracks and their impacts on road performance. It analyzes the key technical challenges faced by GPR in detecting road cracks and provides a detailed overview of the latest research achievements and application potential of GPR in detecting attributes of road cracks, such as depth, width, and dip. The review offers technical support and practical guidance for the precise detection and quantitative analysis of road cracks using GPR. It also promotes the application of GPR in detecting hidden road distress and implementing precise remediation measures.

Key words

Ground Penetrating Radar (GPR) / Road cracks / Attribute characteristics / Non-destructive testing

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ShiLi GUO , Dong LIANG , HongYan ZHANG , et al . Review of ground penetrating radar detection technology for attribute characteristics of cracks in road structural layers[J]. Progress in Geophysics. 2025, 40(5): 2172-2186 https://doi.org/10.6038/pg2025JJ0049

References

Algeo J T. 2022. Application of GPR early-time signal analysis for mapping and monitoring of soil moisture in the shallow vadose zone[Ph. D. thesis]. Rutgers: School-Newark Rutgers.
Chen D P, Dai Q W, Feng D S, et al. Reverse time migration of ground penetrating radar based on normalized cross correlation imaging condition. J. Central South Univ. (Sci. Technol.), 2018, 49(5): 1221- 1227.
Del Río-Barral P, Soilán M, González-Collazo S M, et al. Pavement crack detection and clustering via region-growing algorithm from 3D MLS point clouds. Remote Sens., 2022, 14(22): 5866
Diamanti N, Redman D. Field observations and numerical models of GPR response from vertical pavement cracks. J. Appl. Geophys., 2012, 81: 106- 116.
Dian Q W, Wang H H. Element free method forward modeling of GPR based on random medium model. Chin. J. Nonferr. Met., 2013, 23(9): 2436- 2443.
Du Y C, Pan N, Xu Z H, et al. Pavement distress detection and classification based on YOLO network. Int. J. Pavement Eng., 2021, 22(13): 1659- 1672.
Fan J W, Ma T, Zhu Y J, et al. Ground penetrating radar detection of buried depth of pavement internal crack in asphalt surface: a study based on multiphase heterogeneous model. Measurement, 2023, 221: 113531
Fan Y L, Guo S L, Liang D, et al. Numerical study and application for detection depth of GPR early-time signal. Prog. Geophys., 2024, 39(5): 2069- 2077.
Feng D S, Wang X. The GPR simulation of bi-phase random concrete medium using finite element of B-spline wavelet on the interval. Chin. J. Geophys., 2016, 59(8): 3098- 3109.
Feng X, Zou L L, Liu C, et al. Forward modeling for full-polarimetric ground penetrating radar. Chin. J. Geophys., 2011, 54(2): 349- 357.
Fernandes F M, Fernandes A, Pais J. Assessment of the density and moisture content of asphalt mixtures of road pavements. Constr. Build. Mater., 2017, 154: 1216- 1225.
Fernandes F M, Pais J C. Laboratory observation of cracks in road pavements with GPR. Constr. Build. Mater., 2017, 154: 1130- 1138.
Gao Y X, Cao H B, Cai W W, et al. Pixel-level road crack detection in UAV remote sensing images based on ARD-Unet. Measurement, 2023, 219: 113252
Gong H R, Liu L M, Liang H M, et al. A state-of-the-art survey of deep learning models for automated pavement crack segmentation. Int. J. Transp. Sci. Technol., 2024, 13: 44- 57.
Guo S L, Cai J C, Zhang X Q, et al. Research on bridges hidden diseases detection method by GPR. Prog. Geophys., 2012, 27(4): 1812- 1821.
Guo S L, Zhu P M, Shi X H, et al. Comparative analysis on response of ground penetrating radar wave field to crack width. Chin. J. Radio Sci., 2013, 28(1): 130- 136.
Guo S L, Ji M E, Zhu P M, et al. Study on multiphase discrete random medium model and its GPR wave field characteristics. Chin. J. Geophys., 2015, 58(8): 2779- 2791.
Guo S L, Yan F, Zhu P M, et al. Numerical study on response of ground penetrating radar wave field to crack width. Prog. Geophys., 2016, 31(4): 1803- 1808.
Guo S L, Duan J X, Zhang J F, et al. Application of GPR in urban road hidden diseases detection. Prog. Geophys., 2019, 34(4): 1609- 1613.
Guo S L, Xu Z W, Li X Z, et al. Detection and characterization of cracks in highway pavement with the amplitude variation of GPR diffracted waves: insights from forward modeling and field data. Remote Sens., 2022, 14(4): 976
Guo S L, Cai W C, Tian P F, et al. Research on GPR diagnostic system for hidden road defects based on YOLO. Prog. Geophys., 2025, 40(2): 827- 837.
Guo S L, Yu M Y, Xu Z W, et al. Study on the attribute characteristics of road cracks detected by Ground penetrating radar. Sensors, 2025, 25(3): 595
Hussein R, Etete B, Mahdi H, et al. Detection and delineation of cracks and voids in concrete structures using the ground penetrating radar technique. J. Appl. Geophys., 2024, 226: 105379
Jiang Y J, Dai J L, Chen Z D. Analyzing about cracking mechanism and prevention and cure measures of semi-rigid base course. J. Chongqing Jiaotong Univ., 2002, 21(2): 54- 57.
Jol H M. Ground Penetrating Radar: Theory and Applications. Oxford: Elsevier Science & Technology., 2009,
Krysiński L, Sudyka J. GPR abilities in investigation of the pavement transversal cracks. J. Appl. Geophys., 2013, 97: 27- 36.
Li X Z. Study on pavement cracks detection with ground penetrating radar. Road Mach. Constr. Mech., 2013, 30(8): 39- 47.
Li Z, Torbaghan M E, Zhang T, et al. An automated 3D crack severity assessment using surface data for improving flexible pavement maintenance strategies. IEEE Trans. Intell. Transp. Syst., 2024, 25(9): 12490- 12503.
Liu F Y, Liu J, Wang L B. Asphalt pavement crack detection based on convolutional neural network and infrared thermography. IEEE Trans. Intell. Transp. Syst., 2022, 23(11): 22145- 22155.
Liu L B, Qian R Y. Ground penetrating radar: a critical tool in near-surface geophysics. Chin. J. Geophys., 2015, 58(8): 2606- 2617.
Liu Z, Gu X Y, Li J, et al. Deep learning-enhanced numerical simulation of ground penetrating radar and image detection of road cracks. Chin. J. Geophys., 2024, 67(6): 2455- 2471.
Lu C M, Qin Z, Zhu H L, et al. Practical methods for detection of concealed cracks in highway pavement using ground penetration radar data. Chin. J. Geophys., 2007, 50(5): 1558- 1568.
Lu Q, Liu K X, Zeng Z F, et al. Estimation of the soil water content using the early time signal of Ground penetrating radar in heterogeneous soil. Remote Sens., 2023, 15(12): 3026
Mackiewicz P. Fatigue cracking in road pavement. IOP Conf. Ser. Mater. Sci. Eng., 2018, 356: 012014
Marecos V, Solla M, Fontul S, et al. Assessing the pavement subgrade by combining different non-destructive methods. Constr. Build. Mater., 2017, 135: 76- 85.
Merazi Meksen T, Boudraa B, Drai R, et al. Automatic crack detection and characterization during ultrasonic inspection. J. Nondestruct. Eval., 2010, 29(3): 169- 174.
Minhoto M J C, Pais J C, Pereira P A A. The temperature effect on the reflective cracking of asphalt overlays. Road Mater. Pavement Des., 2008, 9(4): 615- 632.
Rasol M A, Pérez-Gracia V, Fernandes F M, et al. GPR laboratory tests and numerical models to characterize cracks in cement concrete specimens, exemplifying damage in rigid pavement. Measurement, 2020, 158: 107662
Sha A M. Material characteristics of semi-rigid base. China J. Highway Transport, 2008, 21(1): 1- 5.
Tan X, Mahjoubi S, Zou X X, et al. Metaheuristic inverse analysis on interfacial mechanics of distributed fiber optic sensors undergoing interfacial debonding. Mech. Syst. Signal Proc., 2023, 200: 110532
Van Gestel J P, Stoffa P L. Application of Alford rotation to Ground penetrating radar data. Geophysics, 2001, 66(6): 1781- 1792.
Zhang X B, Pei J X, Sha X D, et al. Experimental co-polarimetric GPR survey on artificial vertical concrete cracks by the improved time-varying centroid frequency scheme. Remote Sens., 2024, 16(12): 2095
德鹏, 前伟, 德山, 等. 基于归一化互相关成像条件的GPR逆时偏移成像. 中南大学学报(自然科学版), 2018, 49(5): 1221- 1227.
前伟, 洪华. 基于随机介质模型的GPR无单元法正演模拟. 中国有色金属学报, 2013, 23(9): 2436- 2443.
永亮, 士礼, , 等. 探地雷达早期信号探测深度数值研究与应用. 地球物理学进展, 2024, 39(5): 2069- 2077.
德山, . 区间B样条小波有限元GPR模拟双相随机混凝土介质. 地球物理学报, 2016, 59(8): 3098- 3109.
, 立龙, , 等. 全极化探地雷达正演模拟. 地球物理学报, 2011, 54(2): 349- 357.
士礼, 建超, 学强, 等. 探地雷达检测桥梁隐蔽病害方法研究. 地球物理学进展, 2012, 27(4): 1812- 1821.
士礼, 培民, 兴华, 等. 裂缝宽度对探地雷达波场影响的对比分析. 电波科学学报, 2013, 28(1): 130- 136.
士礼, 孟恩, 培民, 等. 多相离散随机介质模型及其探地雷达波场特征研究. 地球物理学报, 2015, 58(8): 2779- 2791.
士礼, , 培民, 等. 裂缝宽度探地雷达波场响应的数值研究. 地球物理学进展, 2016, 31(4): 1803- 1808.
士礼, 建先, 建锋, 等. 探地雷达在城市道路塌陷隐患探测中的应用. 地球物理学进展, 2019, 34(4): 1609- 1613.
士礼, 文才, 朋飞, 等. 基于YOLO的道路隐性病害探地雷达图谱智能诊断系统研究. 地球物理学进展, 2025, 40(2): 827- 837.
应军, 经梁, 忠达. 半刚性基层裂缝产生机理分析及防治措施. 重庆交通学院学报, 2002, 21(2): 54- 57.
修忠. 地质雷达检测道路路面裂缝方法研究. 筑路机械与施工机械化, 2013, 30(8): 39- 47.
澜波, 荣毅. 探地雷达: 浅表地球物理科学技术中的重要工具. 地球物理学报, 2015, 58(8): 2606- 2617.
, 兴宇, , 等. 探地雷达数值模拟与道路裂缝图像检测的深度学习增强方法. 地球物理学报, 2024, 67(6): 2455- 2471.
成明, , 海龙, 等. 探地雷达检测公路结构层隐含裂缝实用方法研究. 地球物理学报, 2007, 50(5): 1558- 1568.
爱民. 半刚性基层的材料特性. 中国公路学报, 2008, 21(1): 1- 5.

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