ABSTRACT Ground Penetrating Radar (GPR) has emerged as a critical non-invasive tool in archaeological investigations, capable of revealing three-dimensional subsurface structures such as foundations, burials and rubble with high precision. This study presents a novel anomaly detection framework designed to enhance the extraction and interpretation of archaeological features from GPR data. Using a handheld Pulse Ekko system at a validated archaeological test site, we implement an adaptive algorithm that integrates dynamic background statistics and distance-based anomaly recognition. The methodology combines advanced preprocessing including signal enhancement, denoising and background suppression with a robust 2D computational approach that leverages both diffraction and reflection hyperbolas for accurate feature localisation. Results are further extended to 3D visualisation, enabling a comprehensive spatial analysis of subsurface anomalies. The algorithm’s effectiveness is validated through multi-profile data analysis, confusion matrices and cross-validation metrics, achieving high classification accuracy. This integrative approach significantly improves the clarity and reliability of GPR interpretations, offering a powerful tool for archaeologists to non-destructively map and interpret buried cultural features with greater confidence and efficiency.

After the completion of open-pit coal mining, land reclamation is implemented to restore the disturbed eco–hydrological system, for which accurate soil moisture characterization is essential. We evaluated the feasibility and performance of an Auto-Regressive Moving Average (ARMA)-based ground-penetrating radar (GPR) inversion scheme for estimating soil moisture in a reclaimed mine area. GPR data were acquired over a reconstructed three-layer soil profile in a reclaimed open-pit coal mine, and soil moisture content was independently determined using the oven-drying method on core samples. An ARMA model was used to describe the relationship between the GPR reflections and soil electromagnetic properties and to invert the vertical distribution of soil moisture. The ARMA-derived GPR estimates reproduced the measured moisture profile well within the depth interval of 1.4–3.0 m and revealed the clear vertical zonation of soil moisture associated with the engineered layering. Correlation coefficients between the ARMA-inverted GPR estimates and oven-drying measurements ranged from 0.64–0.78 for 0–1.4 m, 0.84–0.93 for 1.4–2.2 m, and 0.98–0.99 for 2.2–3.0 m, indicating that inversion accuracy improves systematically with depth. These results demonstrate that ARMA-based GPR inversion provides a reliable and non-destructive approach for quantifying soil moisture in reclaimed mine soils and offers practical support for monitoring and assessing the effectiveness of reclamation in open-pit coal mining areas.

Abstract. In recent years, continuous development in surveying and mapping technologies has also ushered in new developments and breakthroughs in civil engineering projects. Contemporary surveying and mapping technologies, such as remote sensing, GPS (Global Positioning System), Smart Stations, UAV (Unmanned Aerial Vehicles), GPR (Ground Penetrating Radar), LiDAR (Light Detection and Ranging) and GIS (Geographic Information System) have not only provided fast results but also improved the accuracy of mapping infrastructures. This paper discusses the role of these technologies in enhancing the efficiency and effectiveness in civil engineering projects. The modern surveying and mapping technologies have revolutionized the field of civil engineering by providing critical data/information, such as BIM (Building Information Modelling), DEM (Digital Elevation Model) and DSM (Digital Surface Model) required for planning, design, construction, management and monitoring of infrastructure projects. The integration of these technologies with the capabilities of AI (Artificial Intelligence) and ML (Machine Learning) enables accurate terrain mapping, real-time monitoring, and data-driven decision-making for the development of more efficient, sustainable, and resilient infrastructure that supports the needs of modern society, which are crucial for ensuring the safety, sustainability, and resilience of modern infrastructure. This paper also examines the potential benefits and limitations of these technologies.

ABSTRACT This article explores the benefits of a novel, non-invasive, and multi-scale methodology for pavement condition monitoring by developing a Key Performance Indicator (KPI) derived from Interferometric Synthetic Aperture Radar (InSAR) results, obtained through the Persistent Scatter Interferometry (PSI) and Quasi-PS (QPS) methods. These methods were applied with SARPROZ software using Sentinel-1 images. InSAR enables network-level identification of subsidence and differential settlements, particularly in transition zones. For the first time, InSAR outputs are synthesized into an interpretable KPI and validated against complementary non-destructive tests—Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR), with the goal of providing a cost-effective and holistic joint assessment methodology, ensuring more, overcoming the interpretation limitations of stand-alone tests, and optimizing the required amount of in-situ measurements. FWD loading tests were performed to provide information on the structural condition of the pavement, while GPR measurements, using air-launched frequency antennas (1.0 GHz and 1.8 GHz), detected changes in pavement structure and possible internal defects. The results obtained demonstrate that implementing a KPI synthesizes InSAR outputs into a more interpretable and actionable metric, validated with more meaningful correlations with FWD and GPR analysis, enabling the detection of transition zones, specific elements, and potential damages. From KPI it was also determined the limit of detection for the case study in a national road in Portugal. The holistic approach offers a more efficient, safer, and cost-effective tool for evaluating pavement condition.

ABSTRACT Detecting and mapping mud structures by using ground penetrating radar (GPR) is often challenging because of low target to background contrast and irregular surfaces of the targets. These characteristics tend to produce unclear signals of the mud structures in the conventional images of the data. In these cases, calculating attributes of the data could be useful to produce alternative images that facilitate or improve the interpretation. In this article, we evaluate the capabilities and deficiencies of a broad set of attributes of the GPR data for studying a mud‐wall building, which was detected in a dry environment in the Andean region of north‐western Argentina by using this method. This building, later interpreted as a dwelling, was inhabited during the 1st and 2nd centuries ad , dating back to the formative period of the region. The analysed attributes belong to four types, which are probably the most used in GPR: instantaneous, geometric, texture and interval attributes. Image characteristics that determine the visibility of the wall signals, such as contrast between the mean values of the property at the wall positions and around them, signal continuity along the walls and signal sharpness are discussed. The visibility of the wall signals is analysed as a function of the input parameters and the time coordinate. The main characteristics of the images are interpreted in terms of soil sources that probably produce them. Different groups of attributes are determined based on their individual abilities to improve the images. The capabilities, limitations and usefulness of calculating these attributes to detect the mud walls of the building and generate a plan view of them are shown. Including attributes in the analysis of the data allowed identifying the mud walls easier and more confidently than not doing so, as well as determining their layout more completely.

Efficient soil moisture monitoring is fundamental to precision agriculture, enabling improved irrigation management, enhanced crop productivity, and sustainable water use. This review comprehensively evaluates soil moisture sensing technologies, classifying them into invasive and non-invasive approaches. The underlying operating principles, strengths, and limitations, as well as documented practical applications, are critically discussed for each technology. Invasive methods, including dielectric sensors, matric potential devices, heat-pulse sensors, and microstructured optical fibres, offer high-resolution data but require careful installation and calibration to account for environmental and soil-specific variables such as texture, salinity, and temperature. Non-invasive technologies—such as microwave remote sensing, electromagnetic induction, and ground-penetrating radar—enable large-scale monitoring without disturbing the soil profile; however, they face challenges in terms of resolution, cost, and data interpretation. Key performance factors across all sensor types include installation methodology, environmental sensitivity, spatial representativeness, and integration with decision-support systems. The review also addresses recent innovations such as biodegradable and Micro–Electro–Mechanical Systems sensors, the incorporation of Internet of Things platforms, and the application of artificial intelligence for enhanced data analytics and sensor calibration. While sensor deployment has demonstrated tangible benefits for irrigation efficiency and yield improvement, widespread adoption remains constrained by technical, economic, and infrastructural barriers, particularly for smallholder farmers. The analysis concludes by identifying research gaps and recommending strategies to facilitate the broader uptake of soil moisture sensors, with a focus on cost reduction, calibration standardisation, and integration into climate-resilient agricultural frameworks.

Understanding spatial and temporal variations in soil moisture is critical for sustainable agriculture. Contactless approaches such as ground-penetrating radar and satellite sensing are either expensive or offer limited resolution, making them less scalable. In-situ sensors provide higher resolution but often involve challenging data retrieval processes and wired infrastructure. In this work, we propose EC-Sense, a wire-free system for in-situ soil moisture sensing that employs ultra-low-power sensor Tags buried underground and reference Tags placed on the surface. EC-Sense introduces a novel sensing modality based on energy capture time (EC Time), defined as the time taken by a sensor Tag to harvest sufficient energy to activate a response. EC Time serves as a proxy for path loss in soil, which correlates strongly with soil moisture. By leveraging differential path loss—estimated from the EC Times of both surface and buried Tags—EC-Sense isolates soil-induced attenuation from environmental effects above ground. This differential sensing approach, combined with a decoupled sensing and communication architecture, enables EC-Sense to overcome limitations of existing wideband RF-based sensors. Despite using an active radio, our Tags consume only 3.7 μW on average and achieve a projected lifetime of 10 years under realistic conditions. We deployed EC-Sense in an open agricultural field for over a week and measured soil moisture at multiple depths daily, achieving 98% accuracy compared to ground truth. We further evaluate EC-Sense across three representative soil types—sandy loam, silt loam, and silty clay loam—and demonstrate reliable sensing at depths of 40 cm, 30 cm, and 25 cm, respectively, at field capacity.

Abstract. Problem. The reliability of transport infrastructure depends on timely assessment of road pavement technical condition. Even local defects can cause accelerated deterioration, reducing traffic capacity and road safety. The complexity lies in the fact that a significant part of damage and deformation occurs in hidden layers of road pavement, and their timely detection requires specialized control methods. Traditional diagnostic methods have significant limitations as they are destructive, require considerable material costs and time. Goal. The aim of this work is comprehensive diagnostics of road pavement technical condition on the restored section of Chernyshevskaya Street in Kharkiv by combining ground penetrating radar survey and terrestrial laser 3D scanning. The study focuses on evaluating the effectiveness of the integrated method for road pavement diagnostics. Methodology. The research utilized the ODYAG-1 ground penetrating radar complex with antenna block of 1.2 GHz central frequency for assessing the internal structure of road pavement. The survey was conducted according to standard non-destructive control methodology. Terrestrial laser 3D scanning was performed using Trimble TX6 scanner to capture geometric parameters of surface deformations. Results. Ground penetrating radar surveys of the current repair zone revealed key structural anomalies. On sections outside repair, relatively homogeneous reflection structure was observed, confirming stability of road pavement construction. In contrast, the repaired area clearly showed abnormal reflection at 4.2 ns depth, indicating structural violations of the restored pavement - possible voids, insufficient compaction, or material density changes. Instrumental studies recorded maximum subsidence values up to 2.0 cm in the defect zone, confirming development of deformation processes in road pavement after repair works. Laser scanning results created high-precision three-dimensional point cloud of the studied section with millimeter accuracy, allowing quantitative assessment of surface deformation parameters. Originality. The research demonstrates integration of ground penetrating radar technology and terrestrial laser 3D scanning for comprehensive road pavement diagnostics. Practical Value. The developed integrated approach enables rapid surveying with possibility of operation in urban conditions without traffic interruption, providing relatively high accuracy and low cost compared to destructive methods. Detected defects in the repair zone require immediate intervention to prevent pavement destruction.

Dikes are critical components of flood control infrastructure in China. Ground-penetrating radar (GPR) is widely used for non-destructive detection of subsurface anomalies (e.g., cavities, seepage paths, and soil loosening) in dikes. Conventional GPR surveys are typically conducted along longitudinal lines on the dike crest, which limits comprehensive cross-sectional assessment. This study introduces a novel trapezoidal survey layout that integrates data from upstream/downstream slopes and the crest, coupled with a terrain-correction algorithm. The method projects slope-acquired radar signals onto a topographic model, enabling fused visualization of subsurface anomalies and dike morphology. Validated via borehole and cone penetration tests (CPT) at a Yellow River dike site, the corrected GPR profiles accurately delineated soil stratification and localized a loose-soil zone at the downstream slope foot. This approach significantly enhances interpretability for dike safety assessments.

Revealing the untold stories of sinkhole land subsidence over the remains of an underground river in tuff soil by Ground Penetrating Radar

Abstract. Problem. Modern transport networks of Ukraine operate under challenging conditions due to constant growth of traffic intensity, vehicle overloading, climatic factors influence, and limited funding for maintenance and repair. In such conditions, timely detection of road pavement defects and quality control of repair works becomes particularly important. Traditional diagnostic methods have significant disadvantages: they are destructive, require considerable material costs and time, and cause damage to the road surface. Therefore, non-destructive diagnostic methods, particularly ground penetrating radar technologies, have been actively developing in recent decades. Goal. The aim of this work is to control the quality of road pavement on Chernyshevskaya Street section in Kharkiv city by applying the ODYAG-1 ground penetrating radar complex. The study focuses on post-repair quality control and establishing the effectiveness of GPR technology for monitoring pavement condition after reconstruction works. Methodology. The research utilized the ODYAG-1 ground penetrating radar complex with antennas operating at 1.2 GHz central frequency. The methodology included three-stage calibration: free space measurements, metal reflector calibration, and reference section verification. A comprehensive survey scheme was developed with control points at regular 3-meter intervals. The signal decomposition method was applied based on representing the reflection signal from asphalt as a sum of reflections from individual layers. Data processing involved digital signal processing methods to enhance image quality, eliminate interference, and emphasize subsurface structural features. Results. The study successfully determined layer thicknesses of road pavement structure through analysis of GPR signal reflections. Before repair, measurements showed first layer thickness of 10.0-11.0 cm with dielectric permittivity ranging from 4.64-5.5, and second layer thickness of 8.0-9.0 cm with permittivity 3.06-3.7. Post-repair measurements revealed structural anomalies in the repaired zone, including abnormal reflection at 4.2 ns depth indicating possible voids, insufficient compaction, or material density changes. Calibration optimization significantly improved measurement accuracy and stabilized dielectric permittivity values within narrower ranges. Originality. The research demonstrates the high accuracy of GPR method for diagnosing hidden defects in road structures and establishes the effectiveness of the ODYAG-1 complex for post-repair quality control. The study provides a practical methodology for calibration and systematic surveying of road pavements using non-destructive testing approaches. Practical Value. The developed approach enables rapid surveying with the possibility of operation in urban conditions without traffic interruption, relatively high accuracy, and low cost compared to destructive methods. The detected defects in the repair zone require immediate intervention to prevent pavement destruction, emphasizing the irreplaceable value of GPR monitoring for ensuring durability of repaired road sections. The methodology provides a foundation for continuous monitoring of pavement condition over time and early detection of defects that may lead to repeated failures.

Lithological control on the geometry of strike-slip faults – insight from ground-penetrating radar (GPR) survey and analogue modelling

Ground-penetrating radar (GPR) tomographic imaging and estimation of the volumetric water content of a viaduct pillar using the simultaneous iterative reconstruction technique algorithm

Ground-penetrating radar and magnetic survey of Saruq Al-Hadid, United Arab Emirates: Revealing archaeological features

This publication presents the results of comparative tests of ground-penetrating radar equipment from 2019 to 2022. Various types of GPR were studied, differing: in their contact method (contact and non-contact); signal nature (pulse and multi-frequency); number of channels (single-channel and multi-channel); and application (universal and specialized). The study places particular emphasis on the practical application of GPR in road construction. The capabilities and error of determining the following are analyzed: pavement layer thickness; weakened zones in the road base; areas of reduced road structure strength based on the analysis of the GPR signal. It is also considered a new approach to replacing traditional destructive methods of calibrating GPR data with modern non-destructive technologies. The paper analyzes in detail the effectiveness of various models of GPR equipment and associated software. Modern methods for processing GPR data, including those using artificial intelligence technologies, are presented.

ABSTRACT The quality of tunnel lining is a key factor affecting the quality of tunnel construction. Currently, lining defect identification relies on manual interpretation of ground-penetrating radar (GPR) data, which is inefficient and its accuracy depends on the experience of on-site personnel. Additionally, developing high-accuracy automatic detection models requires a large number of data samples, but it is difficult to obtain sufficient samples in practical research. Existing data augmentation methods suffer from issues such as loss of details in GPR images and low image quality. To address this issue and improve tunnel lining void identification performance, this paper proposes a Spectrum-Constrained PGGAN (SC-PGGAN) network model, which can generate a large number of high-quality GPR images. Experiments show that the images generated by the SC-PGGAN model outperform the GAN and PGGAN models in terms of FID, PSNR, and SSIM metrics. Furthermore, a method for automatic identification of lining voids based on bidirectional collaborative data augmentation is proposed to improve void detection accuracy. Experiments show that training the YOLOv13 model with this method achieves an mAP@50 of 91.70%, outperforming Faster R-CNN (76.04%), RT-DETR (82.54%), and YOLOv13 (84.50%). This paper provides an effective method for high-precision identification of tunnel lining voids.

This paper introduces a navigation system, and a data processing algorithm tailored for a handheld groundpenetrating radar (GPR), along with results from real-world tests conducted using a physical model. Handheld GPR systems are indispensable for scanning challenging and inaccessible terrains, particularly to detect buried landmines and other explosive remnants of war, where vehicle-mounted GPR systems cannot operate effectively. Building on previous research, which focused on a system designed with stationary and mobile ultrawideband radio transceivers tested via simulations, this study addresses practical challenges encountered in a real-world physical model. A novel data processing algorithm is proposed to handle key issues, including a variable number of distance measurements per estimation step and the presence of measurement outliers. Furthermore, the methodology and results of the real-world testing of the positioning system, conducted using an industrial robot for controlled experimentation, are presented and discussed.

Metal detection plays an important role in applications ranging from security to industrial quality control. This work presents a novel ground-less circular patch resonator which behaves as an open stub, engineered for high-sensitivity detection of metallic objects. The key innovation lies in utilizing electric field coupling between the stub’s top surface and nearby metallic targets. The detection is achieved by monitoring changes in the transmission coefficient. The proposed design offers two key advancements by extending detection range up to 8 cm and selective sensitivity exclusively to metallic materials by remaining unresponsive to dielectric objects. Experimental results show that as the distance between the metallic object and the sensor decreases, the resonance coupling becomes stronger, demonstrated by a resonance frequency shift and dip of approximately -10 dB in the transmission coefficient. The sensor is also capable of “see-through” metal detection behind dielectric barriers, and its selective sensitivity to metallic materials helps minimize false responses from non-metallic objects. This approach allows for the detection of cracks in metallic objects and applications involving ground penetration radar (GPR) systems.

ABSTRACT The development of thermokarst lakes on the Qinghai–Tibetan Plateau (QTP) serves as a prominent indicator of permafrost degradation driven by climate warming and increased humidity. However, quantitative observations of surface change and relationships between lakes and permafrost during thermokarst development remain inadequate. This study utilized long‐term terrestrial laser scanning (TLS) to capture high‐resolution data on the surface contour changes of the lake in the Beiluhe Basin over 3 years. Between June 2021 and September 2023, the area of BLH‐B Lake increased by 19.23% to 6634 m 2 , with a maximum shoreline retreat distance of 14.37 m. Lake expansion exhibited pronounced seasonal characteristics, closely correlated with temperature and precipitation variations, with the most significant changes occurring during thawing periods. Notably, the lake expanded by up to 505 m 2 during extreme rainfall events in the 2022 thawing period, accounting for 47.20% of the total expansion observed over 3 years. Integrated geophysical methods, including electrical resistivity tomography (ERT) and ground‐penetrating radar (GPR), revealed substantial permafrost degradation, particularly along the northwestern and western shores, where talik formation occurred within 40 m of the lakeshore. Heat from groundwater flow within the active layer and direct solar radiation contributes to accelerated permafrost degradation around the lake. The integration of TLS with geophysical methods revealed both surface contour changes and subsurface permafrost conditions, providing a comprehensive view of the dynamics of thermokarst lakes. This integrated monitoring approach proves effective for studying thermokarst lake evolution, offering critical quantitative insights into permafrost degradation processes on the QTP and providing essential baselines for climate change impact assessment.

To overcome the limitations of conventional methods, this study developed a novel aerial-ground collaborative framework for multi-dimensional quality assessment of asphalt pavement. The quality inspection of asphalt pavement in the whole construction process is realized. Multiple non-destructive testing (NDT) techniques were integrated, including drone-based infrared thermography, ground-penetrating radar (GPR), and a nuclear-free density gauge. Results showed a strong correlation (R2 > 0.95) between the radar-derived dielectric constant and core samples, enabling rapid, full-coverage characterization. The density gauge achieved less than 3% error. Furthermore, a compactness prediction model based on the dielectric constant and an air void content evaluation model based on temperature parameters are further constructed. This system enables aerial screening, point verification, and ground diagnosis, significantly enhancing inspection efficiency and comprehensiveness.