Search Results (786)

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

This study addresses the challenge of consolidating heterogeneous railway infrastructure data into a unified framework to support advanced analysis and data-driven asset management. The primary objective is the development of a spatial–temporal database that systematically integrates diverse data sources, including asset information, operational loading, track geometry measurements, maintenance records, and Ground Penetrating Radar (GPR) data. The methodology focuses on data harmonization, preprocessing, spatial referencing, and temporal alignment to ensure consistency across datasets with differing structures and resolutions. The resulting database enables network-wide analyses of track condition and deterioration behavior. The results indicate a non-linear relationship between traffic load and deterioration, as well as a significant influence of drainage conditions on both deterioration rates and post-maintenance quality. These findings demonstrate the added value of integrated data analysis in revealing interactions between operational and structural factors. The study concludes that a consistent and scalable database architecture is a key prerequisite for modern railway asset management and provides a robust foundation for predictive modeling and optimized maintenance strategies. Full article

This study presents a compact controlled urban geophysics test site developed at the University of Malta to evaluate the response of complementary near-surface sensing methods under known shallow subsurface conditions. The experimental setup is designed to investigate buried target detection and the response to a simulated shallow leak, used here as a controlled water-release experiment in a shallow carbonate setting characterized by thin, laterally variable soil cover and anthropogenic disturbance. A preliminary passive seismic survey based on the horizontal-to-vertical spectral ratio (HVSR) method was used to compare candidate sectors and select the most suitable area for installation. The test site includes a buried iron plate and a perforated PVC pipe, the latter used to release water under controlled shallow conditions. Ground-penetrating radar (GPR), smartphone magnetometry, electrical resistivity tomography (ERT), and UAV-based thermal imaging were applied to assess target detectability and leak-related surface–subsurface responses. Results show that GPR provides the clearest response for static target detection, while smartphone magnetometry identifies the buried ferrous target under favourable conditions. For the simulated leak experiment, ERT provides the most robust subsurface evidence of moisture redistribution after water injection. UAV thermal imaging captures a complementary surface thermal response influenced by both moisture dynamics and local surface disturbance. The results show that a compact controlled test site can support the comparison of professional and low-cost sensing methods for shallow target detection and simulated leak assessment. In this configuration, the controlled water-release experiment provides a practical basis for evaluating leak-related surface–subsurface responses under known shallow conditions. The proposed setup has implications for methodological assessment, training, and near-surface environmental monitoring in heterogeneous urban settings. Full article

Ensuring the integrity of pavement structures necessitates a thorough evaluation of both surface-level damage and subsurface mechanical performance. This study proposes an integrated, non-destructive assessment framework tailored for semi-rigid base asphalt pavements subjected to repeated vehicular loading via MLS66 full-scale accelerated testing equipment. The proposed methodology integrates ground-penetrating radar (GPR) using the CO4080 system and dynamic response measurements from a falling weight deflectometer (FWD) to characterize structural conditions across multiple depths. Comparative analysis between pre-loading and post-loading data revealed significant deterioration trends in the surface layers, with stiffness loss closely associated with increasing load repetitions. In contrast, the underlying base layers exhibited stable deformation characteristics, with variations in deflection basin indices remaining within ±5%. Subgrade dielectric properties derived from GPR data confirmed consistent compaction quality throughout the test site. Statistical analysis further validated the synergy between GPR and FWD results, demonstrating that the combined application enhances diagnostic accuracy. The dual-method approach improved overall evaluation reliability by approximately 22–35% compared to using individual techniques alone under accelerated pavement testing scenarios. These findings support broader implementation of integrated sensing systems and highlight the potential for application across varied pavement types and loading conditions. Full article

Bridge decks are continuously subjected to high environmental exposure, traffic loading, and material aging, leading to progressive delamination which can negatively affect structural integrity and public safety. More specifically, subsurface delamination of concrete and corroded steel reinforcement must be repaired to keep the decks operational. Among non-destructive evaluation techniques, Ground-Penetrating Radar (GPR) and Infrared Thermography (IRT) offer complementary capabilities for detecting subsurface and near-surface defects; however, effective GPR-IRT data fusion remains challenging due to fundamental differences in sensing principles, spatial resolution and sensitivity. This study introduces a Physics-Enhanced Multi-Modal Fusion (PE-MMF) framework that integrates GPR and IRT data to improve delamination detection in reinforced concrete bridge decks. The proposed approach leverages transfer learning, cross-modal attention mechanisms, and gated fusion to enable robust learning from heterogeneous sensor inputs. Furthermore, a systematic feature selection protocol is integrated to identify physically meaningful indicators that remain consistent across different bridges, enhancing generalization capability. The framework is trained and validated using the publicly available SDNET2021 dataset, comprising co-registered GPR and IRT measurements from five in-service bridge decks with verified delamination ground truth. Results demonstrate substantial performance improvements, with average F1-score gains of up to 55% over IRT-based methods and 25% over GPR-based methods across all tested bridges. Comparative analysis against state-of-the-art methods confirmed the superior generalization capability of the proposed multi-modal approach over single-modality approaches. The findings highlight the potential of deep learning-based sensor fusion as a scalable and data-efficient decision-support tool to prioritize regions for detailed physical investigation during long-term infrastructure monitoring. Full article