Search Results (766)

Ground-penetrating radar is widely used in non-destructive pavement evaluation, but the occurrence of multiple hyperbolic signatures in radargrams of cracked pavements remains insufficiently characterized, particularly for top-down and bottom-up cracking. This study investigates the occurrence of detectable hyperbolas in numerical GPR radargrams by comparing two crack models under a controlled two-dimensional numerical design. Model A represents top-down cracking, and Model B represents bottom-up cracking. For each model, four parametric studies were performed by varying crack width, crack depth, asphalt-layer thickness, and granular-layer thickness, yielding 32 simulations in total. All cases were modeled in gprMax2D at 2300 MHz and processed in MATLAB through radargram pre-processing, central A-scan candidate detection, lateral tracking of hyperbolic events, and final classification based on stable retained trajectories. Model A was predominantly characterized by 3H responses, whereas Model B was predominantly characterized by 2H responses, with no 3H case observed. In Model A, crack-width increase was associated with the strongest occurrence change, whereas in Model B, greater asphalt-layer thickness was associated with a reduction from 2H to 1H. The first apex TWT provided a complementary discriminator between the two models. These findings provide controlled numerical reference trends that may support the interpretation of hyperbola occurrence in GPR-based pavement crack assessment. Full article

Ground-Penetrating Radar (GPR) serves as an essential non-destructive tool for subsurface exploration, and its antenna system largely determines the performance of the overall system. This paper presents a comprehensive review of advanced GPR antenna technologies, examining six major types: Vivaldi, bowtie, tapered, dipole, envelope, and spiral. This analysis shows that trade-offs among these antennas are unavoidable. High-frequency wideband antennas deliver high gain, but their penetration depth is limited to very shallow targets. Some wideband designs achieve wide bandwidth and reasonable gain with compact footprints, while others are suited for detecting embedded metallic objects. By comparison, low-frequency designs operating in the VHF and UHF bands enable very deep penetration, making them suitable for detecting deeply buried targets in lossy media and subsurface utilities. However, deep penetration often comes at the cost of lower gain or larger physical size. Ultimately, no universal antenna exists; the optimal choice depends on whether depth, resolution, or adaptability to attenuating environments is prioritized. Emerging metasurface-integrated and frequency-selective surface (FSS)-backed antennas represent a promising frontier, enabling better bandwidth, gain, and compactness. Ongoing challenges include miniaturization without compromising performance, reliable operation in heterogeneous and lossy soils, and the development of robust, manufacturable designs for field deployment. This review offers researchers and practitioners a structured reference, guiding the development of next-generation GPR systems that balance deeper penetration, higher resolution, and operational versatility. Full article

There are several methods which are utilized for flooding oil process monitoring, such as the seismic methods, and the electromagnetic methods. As the gas flooding oil process is complicated, conventional methods are not capable of monitoring the gas flooding oil process accurately. This study utilizes the Ground Penetrating Radar (GPR) method to monitor the CO₂ flooding oil and water flooding oil processes, as the difference in dielectric constants and conductivity of CO₂, oil and water is utilized to infer distributions of CO₂, oil and water. Moreover, as GPR data processing is time-consuming, it is impossible to process the GPR data in real-time by a conventional method, such as the full waveform inversion method. This study utilizes U-Net neural networks to invert for the subsurface dielectric constants and conductivity distributions of CO₂, oil and water in real-time. A deep learning inversion network based on the U-Net architecture is trained to extract multi-scale features through an encoder–decoder structure, achieving an end-to-end mapping from GPR echo signals to subsurface electrical parameters. The study utilizes the gprMax forward tool to simulate the dynamic response changes in rock-electrical parameters during flooding and constructs a high-resolution training dataset of 100,000 samples. Each sample contains the relationships between a subsurface electrical parameter model and its corresponding multi-transmitter, multi-receiver GPR responses. This method was first tested by the synthetic data of oil–water flooding and oil–water–gas flooding, and then it was tested by observed data from physical core experiments. Numerical and physical core experimental results show that the method accurately inverts the electrical parameter distributions of oil, water, and gas in the sandstone model, successfully capturing the position and morphology changes in the displacement front. The average relative error of dielectric constant inversion is controlled within 8% with the error mainly from the low dielectric constant regions and the relative error of conductivity is smaller than 10%, with the error mainly concentrated in high-conductivity water regions for conductivity inversion results. The results reveal the feasibility and superiority of the neural network-based deep learning method in GPR electromagnetic inversion, providing a new method for real-time flooding monitoring and intelligent reservoir development during oil and gas flooding. Moreover, the proposed approach offers a fast inversion solution and is less affected by the initial model and noise. Full article

Reinforced concrete structures, once considered very durable and capable of withstanding a variety of adverse environmental conditions, often suffer from premature reinforcement corrosion, compromising their safety and serviceability. Ensuring the safety of bridges and buildings requires effective, non-destructive inspection and monitoring techniques to assess the state of degradation without damaging the integrity of the asset. Although a wide range of non-destructive testing (NDT) methods is currently available, few are capable of identifying durability issues during the initial stages before the damage becomes critical. To address this gap, this paper describes an innovative laboratory experiment based on an integrated approach that combines Ground-Penetrating Radar (GPR) and electrochemical methods. This research represents an advanced step in our ongoing projects, merging geophysical and electrochemical expertise to enhance diagnostic precision. A reinforced cement mortar specimen was subjected to free corrosion via partial immersion in sodium chloride solutions of varying concentrations (1, 10, and 35 g/L), followed by an accelerated corrosion phase. The phenomenon was monitored simultaneously using GPR and electrochemical tests. Each technique provided specific information, but a data integration method used in the operating system will further improve the overall quality of diagnosis. Specifically, the application of the Hilbert Transform to GPR signals allowed for a correlation between envelope amplitude variations and the electrochemical behavior of the rebars. These laboratory results highlighted that an integrated observation was useful to indirectly observe the evolution of the phenomenon of corrosion in the steel reinforcement embedded in the mortar specimens. Full article

Tree root-system architecture is vital for forest resilience under rising climate stress, yet techniques like excavation are destructive, slow, and unsuitable for large surveys. We evaluated how Scots pine (Pinus sylvestris) root architecture varies across contrasting environments using non-invasive, high-resolution multichannel ground-penetrating radar (GPR). Plots in the Olkusz Forest District (southern Poland) spanned gradients of soil fertility and stand age. A multichannel radar array produced 3D subsurface volumes, from which two traits were derived: the 2D planar root extent and the 3D rooting-envelope volume. Generalized additive models linked these metrics to site, stand, and tree characteristics. Multichannel GPR revealed clear site-driven differences in root structure and delivered markedly better data quality than single-channel systems. Selective excavation of visible roots confirmed close agreement between radar estimates and true root positions. Root architecture shifted along the fertility gradient and depended strongly on tree size, stand density, and age: rooting volume increased with site productivity and diameter at breast height but declined with stand age and relative spacing. Overall, Scots pine shows strong adaptive plasticity, and multichannel GPR provides a powerful way to integrate below-ground traits into monitoring, modeling, and climate-smart forest management. Full article

Moisture damage is a common hidden distress in asphalt pavements in hot and rainy regions, where it can rapidly develop into severe surface deterioration if not detected in time. To address this issue, this study proposes an automated framework integrating ground-penetrating radar (GPR) data and the YOLOv13 model for multi-scale moisture damage detection on the Jingang Expressway in Cambodia. A total of 1672 GPR images containing moisture damage were collected through field surveys using a 2.3 GHz GPR system. Based on field statistical analysis, the detected damage was classified into three scale levels: large-scale (>2 m), medium-scale (0.8–2 m), and tiny-scale (<0.8 m). Several recent YOLO variants were compared, and YOLOv13s was identified as the optimal model, achieving the best balance between detection accuracy and inference efficiency, with an mAP@0.5 of 85.3% and an FPS of 48. The proposed method was further validated through laboratory and field tests. The results indicate that the developed framework can effectively detect and localize multi-scale moisture damage under practical engineering conditions, providing a non-destructive and efficient approach for pavement condition assessment in hot and rainy regions. By enabling early-stage detection of moisture damage deterioration, the proposed framework may contribute to more sustainable pavement maintenance and long-term transportation infrastructure management. Full article

A nondestructive, rapid, and portable detection method for moisture content (MC) in living tree trunks remains unavailable. Tree radar, developed based on ground-penetrating radar (GPR) technology, represents a promising approach for tree trunk MC detection owing to its high penetration depth and low susceptibility to environmental interference. However, its application to living tree MC detection is constrained by curvature-induced wave propagation complexity, interspecific structural heterogeneity and the limited availability of labeled MC samples obtained through destructive coring, collectively resulting in poor model performance. The study proposed a novel GPR-based MC detection method employing a multi-scale one-dimensional convolutional neural network integrated with an attention mechanism and mixed data augmentation (mixed-MS1DCNNAM). GPR amplitude data extracted from the first 6.5 ns of B-scan signals were used to capture MC-related features via a custom program developed in MATGPR. A mixed model for four tree species with 15–30 cm diameters at breast height (DBH) achieved an R² of 0.7908 and an RMSE value of 0.1059, outperforming traditional models, with test metrics calculated at the tree level by averaging predictions from five directional GPR scans per tree. Furthermore, three DBH-specific sub-models (15–20 cm, 20–25 cm, and 25–30 cm) and four single-species sub-models were developed, yielding improved performance (R² ≥ 0.7246, RMSE ≤ 0.1033; RMSE ≤ 0.0959, MAE ≤ 0.0626, except for European white birch). These results highlighted the effectiveness of stratification by DBH class and tree species. Overall, this study effectively addresses aforementioned challenges and establishes a generalizable nondestructive approach for living trees under field conditions, facilitating sustainable forest management in tree growth monitoring, forest disaster monitoring, harvested timber storage and wood quality assessment. Full article

The reinforced concrete structures of many bridges and buildings in Taiwan are over 30 years old. Seismic retrofitting of these structures requires an accurate assessment of reinforcement configuration and corrosion conditions to ensure structural safety and seismic performance. In this study, a 1 GHz ground-penetrating radar (GPR) antenna was used to scan reflected signals from single- and double-row reinforcing bars embedded in concrete. Based on established principles reported in previous studies, detailed analyses were conducted, including the use of the approximate circumference method to estimate reinforcing bar dimensions and the determination of spacing between double-row reinforcing bars (6–8 cm). The synthetic aperture focusing technique was first applied to process the original GPR data matrix. Subsequently, physical parameters related to interface diffraction, such as the perimeter S of the reinforcing bar, were extracted using the dielectric constant of the material interface, the calculated power reflection coefficient, and the First Fresnel Zone. These approaches enabled more accurate estimation of reinforcing bar dimensions (e.g., equivalent to #3 bar size) and improved resolution of spacing between double-row reinforcing bars to 3–6 cm. The results demonstrate that using the synthetic aperture focusing technique to process GPR data enhances the ability to determine reinforcing bar dimensions, interpret bar spacing, and improve imaging resolution, thereby providing a reliable reference for the safety assessment of reinforced concrete structures. Full article

To address the large variability in existing pavement distress in expressway reconstruction and expansion projects in Zhejiang Province, China, a differentiated overlay design and decision-making method based on multi-index evaluation was proposed using the Ningbo section of the Yongtaiwen Expressway as a case study. Based on 3D ground-penetrating radar (GPR), falling weight deflectometer (FWD), and field coring tests, the existing pavement was classified into five conditions: intact pavement, slight and severe surface-layer distress, and slight and severe base-layer distress. For pavements with surface-layer distress, two alternative overlay schemes were designed. Scheme I was defined as a performance-oriented scheme using high-performance SMA/Superpave asphalt layers and an ATB-25 transition layer where necessary to improve fatigue resistance and coordinated structural performance. Scheme II was defined as an economy-oriented scheme using conventional AC layers and crack-resistant or bonding measures to reduce construction cost while maintaining adequate structural capacity. An ABAQUS-based temperature–load coupled finite element model considering the temperature-sensitive viscoelastic characteristics of asphalt layers was established to analyze the mechanical responses and service lives of the overlay schemes, and the entropy weight–TOPSIS method was used for multi-objective comprehensive decision-making. The results showed that temperature–load coupling markedly increased the tensile strain at the bottom of the asphalt overlay and was a key controlling factor in design. All schemes satisfied the 15-year design requirement, while the base-layer fatigue life of the performance-oriented scheme (Scheme I) was generally no lower than that of the cost-oriented scheme (Scheme II), indicating better long-term service reliability. In addition, the relative closeness coefficients of Scheme I under slight and severe surface-layer distress were 0.586 and 0.546, respectively, both higher than those of the cost-oriented scheme. The proposed method can effectively balance technical performance and life-cycle cost and provides a useful reference for differentiated overlay design in similar expressway reconstruction and expansion projects in hot–humid regions. Full article

We present a concise methodology to model and visualise mole-rat burrows by integrating 3D ground-penetrating radar (GPR) volumes, high-resolution 3D surface texture, and interpretative 3D visualisation with open-code software, such as Blender and Houdini. The workflow shows the processing and conversion steps for converting surface and subsurface raw datasets into point clouds, then the amalgamation of those 3D objects into a voxelised volume. The voxelisation script creates a text file, a *.CSV file, that masks the voxels with the values of 0 and 1 depending on whether they are inside or outside a burrow. This parametrisation resulted in a total of 7,730,587 voxels generated, of which 48,952 have a value of 1 within them. This indicates the presence of one burrow system, in which there were about 60–80 burrow segments that were initially identified by GPR but remained rather interpretative than a verified geometry. The entire process enables handling and combining different, complex, 3D datasets into a simple text file and thus enables merging with covariates for further spatial modelling of burrow systems from incomplete, indirect, noisy measurements. Full article

With the increasing development of underground spaces adjacent to urban levees, contact seepage frequently occurs at the interface between the soil and underground structures. However, traditional geophysical detection methods are often rendered ineffective in such environments due to spatial restrictions and detection blind spots. To address these challenges, this paper proposes a vertical-array lateral scanning detection method. This approach utilizes electrical resistivity tomography (ERT) with flat-base electrodes and ground-penetrating radar (GPR) to acquire data directly from vertical wall surfaces. The feasibility of this method is validated through numerical simulations and field data. The results indicate that the proposed method effectively overcomes the high-resistance shielding effect of hardened walls and clearly reveals the electrical structure of the soil behind the wall. Specifically, the contact seepage zone manifests as a layered low-resistivity feature immediately adjacent to the wall, while the penetrating leakage channel presents as a continuous low-resistivity anomaly extending from the contact interface deep into the levee body. These findings confirm the applicability of this technology for the qualitative identification and effective detection of hazards in complex, space-restricted urban environments. Full article

Being capable of high-resolution imaging and non-contact measurement, Ground Penetrating Radar (GPR) is a promising technology for the detection of unexploded ordnance (UXO). However, UXO detection is severely hindered by clutter, particularly in environments with significant surface roughness where conventional suppression methods prove ineffective. To address this, we propose a polarimetric GPR clutter suppression method based on an improved non-convex Tensor Robust Principal Component Analysis (TRPCA) framework. Specifically, a polarization-aware tensor construction scheme is designed by stacking the HH and VV channel data. This approach exploits the strong inter-channel correlation of clutter to enhance its low-rank property, while highlighting the distinct sparse signatures of targets derived from their polarimetric responses. To further optimize tensor decomposition, we introduce a non-convex Tensor Adjustable Logarithmic Norm (TALN) to overcome the estimation bias inherent in the conventional Tensor Nuclear Norm (TNN). Serving as a tighter surrogate for tensor rank, the proposed TALN regularizer improves the approximation accuracy of the low-rank component, thereby ensuring a clearer separation between clutter and targets. The resulting non-convex optimization problem is efficiently solved using Alternating Direction Method of Multipliers (ADMM). Numerical simulations and laboratory experiments demonstrate that the proposed method suppresses strong clutter stemming from rough-surface reflections more effectively than existing methods, achieving a Signal-to-Clutter Ratio (SCR) improvement of over 20 dB. Full article

Ground penetrating radar (GPR), as an efficient non-destructive testing technique, plays a crucial role in the structural condition assessment and defect identification of railway ballast. Typical defects such as mud pumping generally exhibit characteristics in B-scan images including weak reflections, blurred boundaries, and irregular structures, which pose significant challenges for stable detection and precise localization using existing methods that rely primarily on spatial feature modeling. Most current deep learning approaches focus on modeling spatial or temporal information, while lacking effective utilization of frequency-domain features, thereby limiting their discriminative capability under complex electromagnetic environments. To address these issues, this paper proposes a single-stage object detection framework, termed YOLO-DGW, based on time-frequency collaborative modeling. Built upon YOLOv8, the proposed method introduces a structure-aware spatial enhancement module to improve the representation of continuous GPR echo structures. Meanwhile, frequency-domain information is incorporated as a modulation prior to guide spatial feature learning, enhancing the model’s sensitivity to weak reflections and complex-shaped targets. In addition, A-CIoU loss function is designed to improve localization accuracy and stability for defect regions of varying scales. Experimental results demonstrate that YOLO-DGW achieves an F1-score of 63.06% and an AP@0.50 of 62.07%, representing improvements of approximately 7.41% and 2.8%, respectively, over the strongest baseline method. Compared with several mainstream object detection models, the proposed approach exhibits superior performance in both detection accuracy and cross-region generalization capability. These findings indicate that integrating frequency-domain information into spatial feature learning through a modulation mechanism can effectively enhance the model’s ability to discriminate weak-reflection anomalies, providing a novel time-frequency collaborative modeling paradigm for railway GPR defect detection. Full article

Investigating slope deformation in densely vegetated or remote areas is a major challenge for slope stability assessment. This study introduces and validates an integrated UAV-borne low-frequency Ground Penetrating Radar (UAV-GPR) and LiDAR methodology to characterize an unstable slope in Melizzano, Southern Italy. Radar data were acquired along an east–west transect at ~1 m above ground level, while high-resolution LiDAR were used to generate a detailed Digital Terrain Model for topographic correction and geomorphological analysis. The processed radargram images subsurface features down to ~15 m, revealing a laterally continuous high-amplitude reflector at ~10 m, interpreted as a key main sliding surface. Chaotic reflections above this interface indicate heterogeneous deposits associated with gravitational deformation, while more homogeneous reflections below correspond to stable geological units. The geometry of the reflector suggests a compound landslide mechanism. Borehole data validate the geophysical interpretation, showing depth discrepancies lower than 2 m. The integration of UAV-GPR and LiDAR enables a reliable correlation between surface morphology and subsurface structures. This non-invasive, spatially continuous approach provides an effective framework for subsurface characterization and for improving the interpretation of landslide geometry and internal structure in challenging environments. This study demonstrates the capability of low-frequency UAV-borne GPR to detect deep-seated sliding surfaces (>10 m) in vegetated environments when integrated with high-resolution LiDAR topography. Full article

Ground-penetrating radar (GPR) has applications across many domains, including archaeology, mining, and infrastructure inspection. This review is specifically focused on urban subsurface utility mapping, where accurate detection of buried pipelines, cables, and conduits is critical for excavation safety and infrastructure management. Within this scope, two major barriers are identified: event–utility mismatch and the synthetic–field domain gap. Bibliometric analysis shows increasing reliance on deep learning, yet most methods remain limited to event-level hyperbola detection rather than utility-level inference. In real urban environments, radar responses are often affected by orientation-dependent signatures, clutter, overlapping reflections, and non-utility anomalies, making detected events difficult to map directly to physical infrastructure. In parallel, models trained on synthetic data frequently show limited field generalization because simulated radargrams do not fully reproduce soil heterogeneity, acquisition variability, and system artifacts. The review argues that future progress in urban utility mapping requires a shift toward utility-level reasoning supported by multi-sensor fusion, physics-guided learning, hybrid simulation–field datasets, and uncertainty-aware interpretation. Such advances are essential for making GPR outputs more reliable and actionable in urban engineering practice. Full article