Search Results (790)

Structural cracks that develop within semi-rigid asphalt pavement structures may remain concealed beneath the pavement surface, making their opening widths, depths, and morphologies difficult to determine nondestructively. Although ground-penetrating radar (GPR) can localize subsurface reflective cracks, the hyperbolic anomalies in a B-scan, a two-dimensional cross-sectional radar profile obtained from sequential measurements along a survey line, do not directly represent the actual geometrical dimensions of the cracks. To solve this problem, this paper proposes a geometrical dimension detection and morphological inversion (GDMI) method, called the GPR-GDMI. The GPR-GDMI comprises two independently trained networks: GPR-GCDNet for geometrical dimensions detection and GPR-CMIGAN for morphology inversion. GPR-GCDNet utilizes trapezoidal boxes to represent crack dimensions in B-scan, enhanced by directional crack attention (DCA) for hyperbolic pattern extraction, a SetTrap Head for trapezoidal proposal generation, and multi-scale feature fusion for improved receptive field and coverage. GPR-CMIGAN is an unsupervised framework consisting of two independently designed U-Net generators and discriminators, with Gaussian noise injected to prevent mode collapse. The crack-dimension-constrained cycle-consistency loss incorporates GPR-GCDNet’s detected dimensions as physical constraints in the inversion, while Wasserstein loss mitigates over-constraints and improves stability during training. On the test set, GPR-GCDNet achieved an AP of 0.84, an F1-score of 0.89, and an mIoU of 0.74 under an IoU threshold of 0.5. GPR-CMIGAN achieved a performance on MS-SSIM, LPIPS, and FID of 0.9984, 0.0566, and 2.3145, respectively. Ablation studies confirm the contribution of each component. By quantitative experiments, GPR-GDMI achieves accurate detection of crack geometrical dimensions and morphological inversion based on field-collected GPR data. Full article

Deep-water gravity-flow depositional systems are characterized by intricate internal architectural configurations and pronounced heterogeneity. Traditional outcrop studies rely heavily on qualitative observations and often fail to capture the sub-seismic 3D spatial heterogeneity required for precise hydrocarbon exploration. This study leverages an integrated multi-geophysical framework to achieve a high-resolution, quantitative characterization of internal structures within gravity-flow fan units in field outcrops. Focusing on the Upper Ordovician Lashizhong Formation at the Beishan outcrop in Wuhai, northwestern Ordos Basin, the research integrates Terrestrial Laser Scanning (LiDAR) to construct a 3D topographic framework and uses Ground-Penetrating Radar (GPR) to probe subsurface architecture. Furthermore, Full-Waveform Inversion (FWI) is employed for the high-resolution reconstruction of relative permittivity, culminating in the generation of a 3D geological model via Sequential Gaussian Simulation (SGS). Six primary lithofacies were identified: (1) massive-to-graded medium-to-fine-grained sandstone, (2) graded fine- to silty sandstone, (3) parallel-laminated fine sandstone, (4) climbing ripple-laminated siltstone, (5) wave ripple-laminated siltstone, and (6) horizontally bedded mudstone. Geometrically, six channel–levee architectural stages and one sheet-lobe stage within a mid-fan setting. Quantitative analysis shows that channel axes maintain a high net-to-gross (N/G) ratio of 0.82 with 92% connectivity, whereas levee and lobe margins exhibit a significantly reduced N/G of 0.28 and 42% connectivity. The overall cross-validation accuracy of the 3D model reached 86.4%. This research provides an objective, quantitative technical framework for 3D spatial characterization and reservoir modeling of complex gravity-flow systems. Full article

Underground infrastructure inspection requires sensing methods capable of characterizing both ferromagnetic and non-ferromagnetic subsurface features. This study evaluated a portable nitrogen-vacancy (NV) quantum diamond magnetometer (QDM) together with multi-frequency ground-penetrating radar (GPR; 250, 500, and 800 MHz) at a 90 m full-scale pavement test site containing documented subsurface targets. Repeated stationary QDM measurements were first conducted to characterize temporal variability and establish site-specific magnetic baselines, followed by a 1 m interval spatial survey and corresponding GPR measurements. The QDM showed reproducible spatial magnetic patterns despite substantial variability in reinforcement-dense sections, and all 10 cross-session measurements remained within ±1.5 standard deviations of their repeated-measurement baselines. Ferromagnetic targets produced localized magnetic responses, whereas post hoc classification using disturbance-normalized magnetic anomalies showed limited target-level discrimination (AUC = 0.495). GPR likewise showed limited post hoc target classification (500 MHz AUC = 0.472), but section-level radar responses differed significantly across pavement environments. The two modalities exhibited contrasting section-level responses while showing weak and inconsistent point-level correspondence. These results indicate that NV-diamond magnetometry and GPR provide complementary physical information, while repeated magnetic measurements are important for interpreting QDM observations under heterogeneous field conditions. Full article

Accurate identification and risk assessment of hidden defects in urban roads are essential for preventing road collapse and ensuring infrastructure safety. Based on a large-scale investigation of urban roads in Hunan Province, China, this study proposed a multi-scale collaborative detection framework integrating three-dimensional ground-penetrating radar (3D GPR) wide-area screening, manual interpretation, two-dimensional (2D) multi-frequency verification, and borehole endoscopic validation. Typical electromagnetic response characteristics of cavity, void, and loose-zone defects were summarized, and qualitative recognition criteria for different defect types were established. A total of 153 endoscopically validated defect cases, including 115 loose zones, 28 voids, and 10 cavities, were analyzed to investigate defect distribution and associated formation factors. The results show that underground pipeline damage, engineering disturbance, and inadequate backfill compaction are the main factors associated with defect development. Inadequate backfill compaction accounts for approximately 70% of all detected defects and is mainly related to early-stage defects, whereas pipeline damage is associated with nearly 90% of cavity cases despite accounting for only 30% of all cases. Risk assessment results show that cavities and voids are mostly high-risk defects, while loose zones are mainly moderate- to low-risk defects. The proposed framework provides a practical basis for subsurface defect identification, risk warning, targeted remediation, and preventive maintenance of urban roads. Full article

Ground-penetrating radar (GPR) is a rapid and non-destructive technique for road sub-surface distress (RSD) detection. However, reliable interpretation of GPR images remains challenging because subsurface anomalies often present weak boundaries, ambiguous textures, and high similarity to non-distress targets. This study proposes a cross-verification intelligent algorithm that exploits complementary information from different views of 3D GPR data. Three YOLO-based detectors are trained on view-specific GPR images to identify RSD-related targets, including voids, loose structures, and manholes. By sequentially verifying detection results across different views, the proposed method improves recognition reliability under vague subsurface imaging conditions. The models are trained and evaluated on an expert-annotated field 3D GPR dataset containing 2134 location-level multi-view samples. At the selected operational thresholds, the complete cross-verification procedure achieved 95.9% precision and 98.6% recall for RSD detection in the testing subset. In a field evaluation on 15 roads, all 69 RSD locations in the expert-identified reference set were matched by automatic indications. When integrated into an automatic detection system, the method reduced manual inspection workloads by approximately 90% while maintaining high field reliability. These results demonstrate the potential of multi-view cross-verification for post-survey RSD screening and expert-assisted review. Full article

Vegetation above the soil surface can have a considerable influence on ground-penetrating radar (GPR) measurements, especially when shallow buried objects are to be detected. Plant water content, biomass, and the structural arrangement of leaves and stems can attenuate, scatter, or obscure reflections from both the soil surface and buried targets. This study therefore examines how different vegetation types and soil moisture conditions affect the GPR response of a shallow buried reference target under controlled laboratory conditions. For the analysis, a GPR operating in a down-looking configuration is used, which is moved across the study area on an equidistant grid. The evaluation is based on the analysis of multiple intensity pixels and heuristic statistics to characterise the radar reflections. The Normalised Difference Vegetation Index (NDVI) is used to describe the vegetation; this index approximates, in particular, the water content of the plants, as well as the relationship between biomass and dry matter content. The analysis reveals a relationship between water content, biomass volume, and the signal-to-clutter ratio (SCR) in relation to the detectability of targets. The condition of the vegetation significantly influences radar target reflection and thus the detectability of subsurface targets. In particular, higher water content in vegetation correlates with increased scattering within the vegetation layer, thereby preventing ground reflection and target reflection. Full article

The Raša Fault is a prominent seismically active strike-slip fault within the Dinaric Fault System in SW Slovenia, seismotectonically estimated to be capable of producing earthquakes up to Mw = 7.4. Since the surface exposure of fault-related markers is discontinuous, and the near-surface expression of deformation is poorly constrained, there is a need to improve the detection of fault-related features in complex sedimentary environments. In such settings, signal attenuation, complex stratigraphy, and irregular fault-zone geometries often obscure subtle deformation features, limiting the interpretability of standard 2-D geophysical profiles. A pseudo 3-D Ground-Penetrating Radar (GPR) survey, along with complementary Electrical Resistivity Tomography (ERT) surveys and reprocessing of LiDAR (light detection and ranging) data to obtain high-resolution Digital Elevation Models (DEMs), was conducted in selected environments dominated by low-resistivity karstic deposits and highly heterogeneous fluvial sediments to assess and improve the capability to detect and characterize subtle shallow deformations associated with the Raša Fault. Tectonic geomorphological mapping facilitated the recognition of potentially active fault traces and the identification of Quaternary sedimentary and erosional features, where recent deformations are usually preserved and can be dated in further paleoseismological investigations. The analysis of dense GPR data and complementary ERT profiles enabled us to clearly image the fault deformation pattern and obtain quantitative information about the subsurface, showing details of faulting and related deformation structures not evident at the surface. Furthermore, it enabled the detection of fault zone complexity, revealing it as an irregular and laterally changing area with sediment infillings, rather than a single vertical discontinuity. The complexity of faulting in the near surface depends on many factors, including the competence and age of the faulted material, as well as the local geomorphology. This study has demonstrated the applicability of pseudo 3-D GPR surveying, combined with ERT profiles, for subsurface mapping of active strike-slip faults in karstic and fluvial sedimentary environments. The methodology can be recommended in particular for rapid and cost-effective investigation of sites with subtle surface evidence of active faulting in order to determine near-surface fault splaying. Full article

The archaeological reconstruction of historic monuments from fragmentary remains requires transforming limited evidence into verifiable hypotheses. This study proposes a methodological framework in which structural analysis plays an active interpretative role, rather than serving merely as verification, provided that input data are independently constrained and modelling assumptions align with the available level of knowledge. The framework is designed to be replicable and transferable to archaeological contexts characterised by incomplete preservation. It combines non-invasive survey techniques—high-resolution digital documentation and ground-penetrating radar (GPR)—to establish reliable geometric and physical constraints. Thrust-based limit analysis, following the Heyman Safe Theorem, is then applied to evaluate reconstruction hypotheses through static equilibrium under self-weight. The methodology is tested on the cavea of the Circus of Maxentius in Rome, a Roman concrete construction in which significant portions of the vaulted substructure are collapsed or buried. Three typological cross-sections are virtually reconstructed using construction-archaeology reasoning, measured geometry, and geophysical evidence. Their stability is assessed through thrust-line admissibility and geometric safety factors. Only one section approaches limit equilibrium when analysed independently, while the others prove inadmissible, suggesting that rear backfills, transverse supporting walls near the imperial corridor, and vaulted structures were essential to the original structural system. Overall, the study demonstrates how integrating non-invasive data and limit analysis reduces interpretative uncertainty in reconstructing partially preserved Roman architecture. Full article

Tunnel lining defects pose significant threats to structural integrity and operational safety. Traditional image processing and machine learning methods often suffer from limited accuracy and poor generalization under complex backgrounds. To address these limitations, this study proposes a lightweight intelligent recognition method based on You Only Look Once version 11 nano (YOLOv11n) for Ground Penetrating Radar (GPR) images of tunnel linings. The backbone is replaced with Mobile Network Version 3 (MobileNetV3) to reduce parameters and Floating Point Operations (FLOPs), while depthwise separable convolution and a streamlined Compressed 2-Stage Fused-Lite (C2f-Lite) structure are integrated into the Neck to further decrease computational overhead. Channel mapping layers are employed to ensure smooth feature transfer, and selective use of Squeeze-and-Excitation (SE) attention and Hard-Swish (H-swish) activation balances detection accuracy with efficiency. Evaluated on a low-power mobile workstation acting as an edge-precursor proxy platform, experimental results demonstrate that the improved YOLOv11n_MobileNetV3 model achieves high accuracy with a mean Average Precision (mAP) at 0.5 of 94.4% and mAP@0.5:0.95 of 62.4%, low computational cost of 4.7 Giga Floating Point Operations (GFLOPs), and fast inference speed of 45 Frames Per Second (FPS). Comparative analysis further confirms its superior balance of detection performance and efficiency over YOLO version 5 (YOLOv5) and YOLO version 8 (YOLOv8) baselines. The proposed approach provides a highly optimized, edge-oriented engineering solution for real-time tunnel lining defect inspection, establishing strong structural and theoretical feasibility for future deployment in embedded systems. Full article

The presence of near-surface cavities poses a significant geohazard due to potential ground subsidence and structural collapse. To mitigate threats to urban stability, this study presents an integrated geophysical framework to locate and characterize abandoned mining galleries and exploitation voids near Linares (Jaén, Spain). The approach combines four complementary techniques: electrical resistivity tomography (ERT), ground-penetrating radar (GPR), frequency-domain electromagnetics (FDEM), and microgravity. The resulting multi-physics responses were cross-referenced with visible surface subsidence features and archival mine plans. Air-filled galleries and shafts generated highly pronounced high-resistivity anomalies. Shallow voids detected at depths of 2–5 m were undocumented in 19th-century mining maps, suggesting older historical origins, whereas deeper ERT profiles and structural disturbance trends (up to 30 m) correlated well with historical records. Within this framework, FDEM provided high-resolution lateral mapping, GPR excelled at resolving ultra-shallow structural boundaries, and ERT characterized deep gallery networks. Crucially, microgravity mitigated inversion non-uniqueness by directly confirming physical mass deficits over the anomalies. This integrated workflow overcomes individual resolution limits, offering a practical tool for land-use planning and early geohazard risk assessment in collapse-susceptible areas. Full article

Ground-penetrating radar (GPR) and electrical resistivity tomography (ERT) are physically complementary non-destructive evaluation methods for reinforced concrete, yet their integrated diagnostic use has been limited by the absence of controlled, ground-truth-validated calibration of the joint-signature space. This paper presents a laboratory calibration programme in which a single C30/37 reinforced concrete beam (3000 mm × 300 mm × 200 mm, three T12 bars at 35 mm cover, CEM I 42.5N, w/c = 0.50) was sequentially conditioned through four controlled deterioration states—intact reference (Model A), water-filled saw-cut crack (Model B), full saturation by seven-day top-surface ponding (Model C), and chloride-induced active corrosion (Model D). Seven RES2DINV inverted ERT sections at three electrode spacings (a = 7, 15, and 30 mm) and three 800 MHz GPR profiles were acquired across the four known ground-truth conditions. The intact-reference resistivity ρ₀ = 558 Ω·m (full-section median of the mlab dataset at a = 7 mm) and GPR-calibrated velocity v = 0.095 ± 0.008 m/ns (from hyperbola fitting at 35 mm rebar cover) establish the absolute baselines. The four conditions produce systematically distinct joint signatures: Model A exhibits uniform high resistivity with clean rebar hyperbolae and no anomalous reflections; Model B produces a localised ERT low-ρ anomaly (ρ_min = 1.46 Ω·m) co-located with a negative-polarity (R = −0.68) GPR crack-mouth reflection confirming water-fill; Model C produces pervasive low-ρ with a smooth depth gradient and 50–65% GPR amplitude attenuation (−6.0 to −9.1 dB); Model D produces the same bulk GPR signatures as Model C but with a critically different ERT spatial texture—a heterogeneous near-surface layer above a sharp boundary at z ≈ 40 mm with depth-preferential low-ρ concentrated at rebar level. This depth-preferential signature, quantified here by a reproducible Depth-Preferential Index (DPI), is the primary ERT-only diagnostic criterion distinguishing active corrosion from pervasive saturation. For the Model C versus Model D distinction, the GPR response is non-discriminating; this high-risk distinction is resolved exclusively by the ERT depth-preferential criterion. The calibration demonstrates that GPR and ERT are physically non-redundant in the strict sense: neither method alone can unambiguously discriminate all four states, but their combination yields correct classification within the controlled laboratory conditions and subject to the stated qualification conditions. The corrosion state was confirmed at the regime level (chloride above the depassivation threshold, under accelerated polarisation) but was not quantified electrochemically, so the depth-preferential signature is interpreted as an indirect spatial proxy for active corrosion rather than a measurement of corrosion rate. Seven failure modes are quantitatively characterised and embedded in the framework as a priori qualification conditions. The calibrated reference values (ρ₀, A₀, Stage 2 thresholds, depth-preferential criterion) are specific to the laboratory mix and curing history and require local Stage 1 recalibration for field application. Full article

The accurate and nondestructive quantification of subgrade moisture content is a core demand for highway construction quality control and long-term performance maintenance. In order to study the response relationship between subgrade moisture content and ground-penetrating radar (GPR) signal eigenvalues, this study constructs the volumetric moisture content–dielectric constant relationship of Guangxi high-plasticity clay and carries out gprMax forward numerical simulations. Fourteen radar signal eigenvalues are extracted from preprocessed signals via time-domain waveform analysis, Hilbert transform analysis, and power spectral density analysis. Seven key eigenvalues are screened out through Pearson correlation coefficient-based dimensionality reduction. Three machine learning algorithms—artificial neural network (ANN), random forest (RF), and light gradient boosting machine (LightGBM)—are adopted to optimize the subgrade moisture-content inversion model, which is finally validated through indoor model box tests and field subgrade tests. The results show that: (1) The linear fitting formula is the most suitable for describing the volumetric moisture content–dielectric constant relationship of Guangxi clay, with a coefficient of determination (R²) of 0.979 and a mean absolute error (MAE) of 0.31. (2) The feature matrix after dimensionality reduction effectively alleviates the degradation of model generalization ability and interpretability. (3) The LightGBM model achieves the highest prediction accuracy for clay volumetric moisture content, with an R² of 0.99926 and an MAE of 0.172%. (4) For gravimetric moisture-content inversion, the maximum relative error is 1.6% in the indoor model box test and 1.7% in the field test, both within the 2% tolerance of engineering requirements. This study verifies the feasibility of the proposed subgrade moisture-content identification method based on GPR signal eigenvalues. The proposed method provides an efficient technical path for the large-area and nondestructive detection of subgrade moisture and has promising application prospects in subgrade construction quality acceptance, daily maintenance monitoring and hidden disease early warning. Full article

Ground penetrating radar (GPR) is widely used for non−destructive evaluation of pavement structures, yet the automatic detection of internal cracks remains challenging due to variations in crack geometry, infilling materials, and antenna configurations that significantly alter signal responses. Most existing machine learning approaches focus on improving detection accuracy but pay limited attention to the inherent symmetries and invariances present in GPR data. This study proposes a symmetry−informed learning framework for robust pavement crack detection across different antenna orientations and material conditions. Laboratory concrete slabs containing cracks with varying widths (2–30 mm) and depths (10–110 mm) were constructed and tested under five representative crack states: air−filled, dry sand, fresh water, saturated sand, and bitumen−filled. GPR data were collected using a 2.3 GHz system under perpendicular and parallel broadside antenna orientations to capture rotational variability. A deep learning model was developed with symmetry−aware training strategies that exploit rotational consistency and material−invariant feature learning. Comparative experiments were conducted to evaluate detection performance and cross−condition generalization. Results demonstrate that incorporating symmetry improves model robustness and generalization across unseen orientations and filling conditions. The proposed framework highlights the importance of symmetry−informed learning for reliable AI−driven GPR inspection of pavement infrastructure. Full article

Ground-penetrating radar (GPR) was utilized for subsurface soil investigation in the Yellow River Delta, aiming to provide a scientific basis for the remediation performance of saline soils. The study particularly focuses on the red clay layer, a typical and characteristic soil horizon in this region. GPR antennas with central frequencies of 400 MHz and 900 MHz were adopted to investigate shallow soils within 1 m of the ground surface across three experimental plots (pits, undisturbed soils, and tilled soils) and 18 scattered measurement sites, followed by systematic analysis and interpretation of the acquired GPR profiles. During data acquisition, reasonable survey lines were deployed across the patchy bare areas of cultivated lands covering the experimental plots and measurement points to collect raw GPR data. Meanwhile, subsurface soil data were collected via test pits and borehole sampling along the survey lines. Raw GPR data were further preprocessed and postprocessed to characterize soil horizons and interpret subsurface stratigraphic structures. Finally, the correlations between the relative dielectric permittivity, reflection coefficient, and reflected wave amplitude of each soil layer were systematically analyzed. The results demonstrate that the 400 MHz antenna enables effective identification of soil layers within 1 m depth, while the 900 MHz antenna provides high-resolution detection for soil layers above 0.5 m. The red clay layer presents a distinct strong-amplitude reflection on GPR profiles, and the average relative dielectric permittivity of soils across the study area reaches 30.57. GPR profiles reveal that soil horizons with an absolute reflection coefficient greater than 0.01 yield detectable continuous reflection signals and allow uninterrupted stratigraphic interpretation. An empirical formula was established to calculate soil relative dielectric permittivity from soil moisture content, with a correlation coefficient of 0.9173. However, this formula ignores the influences of soil salinity and other trace soil elements. This study realizes rapid and accurate characterization of the depth and thickness of shallow soil layers, providing technical support for soil remediation of saline–alkali land in the Yellow River Delta. The findings also provide a valuable reference for evaluating the remediation effects, optimizing arable land utilization, preventing and mitigating soil salinization risks, and promoting the sustainable economic development of the study area. Full article

Reliable and scalable landmine detection technologies are essential for humanitarian mine action (HMA), yet standardized benchmarks for Unmanned Aerial Vehicle (UAV)-based sensing in operationally relevant environments remain limited. This study presents a comprehensive evaluation of 34 multimodal datasets acquired over a standardized seeded test site for landmine and unexploded ordnance detection. Nine sensing modalities, including RGB, thermal, multispectral, hyperspectral, LiDAR, and Synthetic Aperture Radar (SAR), are evaluated using the Anomaly, Identifiable Anomaly, Unique Identifiable Anomaly (AIU) index to establish a unified framework for quantifying detection fidelity. Results indicate that RGB imagery achieves the highest surface detection rate (94.8%), with 45.4% of targets classified as uniquely identifiable, reducing false-positive risk. For sub-surface detection, handheld electromagnetic induction (EMI) and magnetometry exceed 95% detection for ferrous items but fall below 10% for plastic ordnance. Ground-penetrating radar (GPR) is the only modality capable of detecting buried plastic targets (55.6% for cart-based systems), whereas UAV-mounted GPR remains limited (18.2%) at current operational flight heights. Based on the comparative analysis, we discuss the gaps in current detection capabilities, compare false-positive rates across modalities, and perform a cost–benefit analysis fitting contamination scenarios with the most cost-effective detection method. All datasets are publicly released, along with an interactive web-map, to support reproducible benchmarking and cross-modality comparison in UAV-enabled explosive hazard detection. Full article