Search

The integration of remote sensing technologies, such as Mobile Mapping Systems (MMS), Ground Penetrating Radar (GPR), airborne LiDAR, high-speed terrestrial laser scanner and airborne multispectral cameras, is transforming the execution and management of large-scale infrastructure projects. These tools enable efficient and accurate data collection without requiring operators to be physically present in the observed area, enhancing the ability to map, analyze, and model both surface and subsurface features. This paper explores the synergy of these technologies, focusing on their application in critical sectors such as climate change mitigation, smart cities, and digital twins. It examines the benefits of technology integration, the challenges of data interpretation, and the opportunities for improving project efficiency and sustainability. Through a detailed analysis of each remote sensing technology, this paper highlights their potential to redefine large-scale project execution, driving innovation in infrastructure development and to bring business benefits to organisation which implements it. [ABSTRACT FROM AUTHOR]

Handheld Ground Penetrating Radar (GPR) is utilized for detecting rebar, but detecting damage is difficult due to its low reflectance. This study introduces an algorithm to quantitatively estimate damage thickness from GPR-received waveforms. Simple methods to separate peaks from time waveforms at the top and bottom of the crack prove challenging due to destructive interference and side lobes. In previous studies, it has been confirmed that minor variations in damage thickness affect the frequency property. We propose an algorithm to estimate damage thickness using pattern matching with a theoretical amplitude spectrum that accounts for multiple reflections. Initially, the damage thickness is roughly determined by combining low-frequency spectrum centroids with spectrum amplitude. After roughly estimating the damage thickness, subsequent spectral pattern matching is performed within predefined gating and bandwidth ranges. This approach enables quantitative estimation of damage thickness from 2 mm to 180 mm with a millimeter order accuracy, demonstrating its practical application potential. [ABSTRACT FROM AUTHOR]

This study explores the impact of corrosion on Ground Penetrating Radar (GPR) responses through practical experiments and numerical modelling, focusing on rebar diameter reduction, corrosion product layer thickness, crack formation and corrosion product filling in vertical and transverse crack. Practical experiments involved GPR testing of reinforced concrete slab. By analyzing B-scans we identify areas with moderate and severe corrosion. Numerical modelling using the Finite Difference Time Domain (FDTD) Method to model GPR signal propagation in a concrete bridge deck with corrosion is applied. Key finding includes a significant 26.70% increase in reflected wave amplitude when corrosion product filling in vertical crack increased by 400%, highlighting its extensive effect on signal GPR propagation. Reduced rebar diameter led to a 9.79% amplitude decrease and a 0.06 ns arrival time delay. Increased corrosion product layer thickness primarily affected arrival time with a 0.06 ns extension but significantly amplified GPR signal amplitude. These findings offer insights for improving GPR based corrosion detection and assessment methods, leading to more robust systems for concrete bridge deck inspection and maintenance. This paper contributes to understanding how corrosion affects the signal that is detected by GPR. This information can be used to improve the way that we manage and assess corrosion in concrete bridge deck. [ABSTRACT FROM AUTHOR]

In this study, a combined workflow of computational methodologies is introduced to explore the transformative landscape of the ancient city of Savatra (Central Anatolia Region, Türkiye), which faces long‐term risks stemming from natural and anthropogenic threats. Emphasis was placed on regional and local scale landscape analysis, employing aerial and ground‐based remote‐sensing techniques to unravel past settlement patterns and understand the impact of environmental factors, topography and natural resources on both the location of Savatra and spatial organization of its features. On a regional scale, the influence of hydrological conditions, slope and aspect on the landscape was determined through the employment of Geographical Information System (GIS)‐based analysis of digital elevation models (DEMs). At a more local scale, the utilization of the Unmanned Aerial Systems‐derived DEM and geophysical survey helped identify potential archaeological features and also assessed the risk posed to these features. Furthermore, the incorporation of 3D GIS analysis, integrating 3D point cloud representations of the ground‐penetrating radar volume and DEM, provided essential insight into the state of preservation of the buried features. The collaborative application and joint interpretation of these methodologies yielded a wide range of clues and explanations, unravelling the complex palimpsest of past activities. This research not only serves as foundation for future studies specifically for Savatra, but also provides a preliminary remote sensing–based exploration blueprint to other yet to be studied archaeological sites. [ABSTRACT FROM AUTHOR]

In recent years, urban road collapse incidents have been occurring with increasing frequency, particularly in populous cities. To mitigate road collapses, geophysical prospecting plays a crucial role in urban road inspections. Ground Penetrating Radar (GPR), a non-destructive technology, is extensively employed for detecting urban road damage, with manual interpretation of GPR images typically used to identify buried objects. Nonetheless, manual interpretation methods are not only inefficient but also subjective, as they rely on the interpreter's experience, thereby affecting the interpreting reliability. This study investigates the distribution and causes of road collapses and develops a deep learning-based intelligent recognition model using GPR detection images of urban roads in cities of the South China as original samples. The finding reveal that road collapses are concentrated in the months of July and August, mainly caused by pipe leakage and rainfall. Common anomalies in urban road GPR detection comprise seven types of target objects, including cavity, pipeline, etc., with standard GPR images acquired through outdoor field experiments. Utilizing GPR forward simulation and image augmentation methods to expand the sample size, as well as generating anchor box dimensions through clustering analysis, have all been proven to effectively improve the model's performance. The urban road GPR image intelligent recognition model, based on the YOLOv4 algorithm, achieves a detection accuracy of up to 85%, proving effective in GPR detection of urban roads in cities of North China. This research offers valuable insights for the future application of deep learning-based image recognition algorithms in urban road GPR detection. [ABSTRACT FROM AUTHOR]

Characterizing soil moisture around drip irrigation pipes is crucial for precise and optimized farming. Machine learning (ML) approaches are particularly suitable for this task as they can reduce uncertainties caused by soil conditions and the drip pipe positions, using features extracted from relevant datasets. This letter addresses local moisture detection in the vicinity of dripping pipes using a portable microwave imaging system. The employed ML approach is fed with two dimensional images generated using back projection as a radar‐based algorithm and the Born approximation as an inverse scattering method, based on spatio‐temporal (collected data at various positions over the soil surface and at different time points.) measurements at various frequencies. The study investigates the performance of K‐nearest neighbour (KNN) and convolutional neural networks (CNN) algorithms for moisture classification based on these imaging techniques. We also explore the potential of KNN and CNN for moisture estimation around the plant roots and in the presence of pebbles. In general, CNN outperforms KNN in moisture content detection from microwave data, especially after applying imaging algorithms. A combination of CNN as the ML approach and the back projection algorithm to provide training data, yielded 20%$20\%$ accuracy more than other models for moisture content estimation. Also, the practical results demonstrate that our method can detect soil moisture with an estimation error of less than 10%. [ABSTRACT FROM AUTHOR]

Subsurface structural distribution can be detected using Ground-Penetrating Radar (GPR). The distribution can be considered as road fingerprints for vehicle positioning. Similar to the principle of visual image matching for localization, the position coordinates of the vehicle can be calculated by matching real-time GPR images with pre-constructed reference GPR images. However, GPR images, due to their low resolution, cannot extract well-defined geometric features such as corners and lines. Thus, traditional visual image processing algorithms perform inadequately when applied to GPR image matching. To address this issue, this paper innovatively proposes a GPR image matching and localization method based on a novel feature descriptor, termed as central dense structure context (CDSC) features. The algorithm utilizes the strip-like elements in GPR images to improve the accuracy of GPR image matching. First, a CDSC feature descriptor is designed. By applying threshold segmentation and extremum point extraction to the GPR image, stratified strip-like elements and pseudo-corner points are obtained. The pseudo-corner points are treated as the centers, and the surrounding strip-like elements are described in context to form the GPR feature descriptors. Then, based on the feature description method, feature descriptors for both the real-time image and the reference image are calculated separately. By searching for the nearest matching point pairs and removing erroneous pairs, GPR image matching and localization are achieved. The proposed algorithm was evaluated on datasets collected from urban roads and railway tracks, achieving localization errors of 0.06 m (RMSE) and 1.22 m (RMSE), respectively. Compared to the traditional Speeded Up Robust Features (SURF) visual image matching algorithm, localization errors were reduced by 86.6% and 95.7% in urban road and railway track scenarios, respectively. [ABSTRACT FROM AUTHOR]

Detecting sub-surface objects poses significant challenges, partly due to attenuation of the ground medium and cluttered environments. The acquisition polarisation and antenna orientation can also yield significant variation of detection performance. These challenges can be mitigated by developing more versatile systems and algorithms to enhance detection and identification. In this study, a novel application of a 3D SAR inverse algorithm and polarisation synthesis was applied to ultra-wideband polarimetric data of buried objects. The principle of polarisation synthesis facilitates an adaptable technique which can be used to match the target's polarisation characteristics, and the application of this revealed hidden structures, enhanced detection, and increased received power when compared to single polarisation results. This study emphasises the significance of polarimetry in ground-penetrating radar (GPR), particularly for target discrimination in high-lift-off applications. The findings offer valuable insights that could drive future research and enhance the performance of these sensing systems. [ABSTRACT FROM AUTHOR]

Full-waveform inversion (FWI) is one of the most promising techniques in current ground-penetrating radar (GPR) inversion methods. The least-squares method is usually used, minimizing the mismatch between the observed signal and the simulated signal. However, the cycle-skipping problem has become an urgent focus of this method because of the nonlinearity of the inversion problem. To mitigate the issue of local minima, the optimal transport problem has been introduced into full-waveform inversion in this study. The Wasserstein distance derived from the optimal transport problem is defined as the mismatch function in the FWI objective function, replacing the L 2 norm. In this study, the Wasserstein distance is computed by using entropy regularization and the Sinkhorn algorithm to reduce computational complexity and improve efficiency. Additionally, this study presents the objective function for dual-parameter full-waveform inversion of ground-penetrating radar, with the Wasserstein distance as the mismatch function. By normalizing with the Softplus function, the electromagnetic wave signals are adjusted to meet the non-negativity and mass conservation assumptions of the Wasserstein distance, and the convexity of the method has been proven. A multi-scale frequency-domain Wasserstein distance full-waveform inversion method based on the Softplus normalization approach is proposed, enabling the simultaneous inversion of relative permittivity and conductivity from ground-penetrating radar data. Numerical simulation cases demonstrate that this method has low initial model dependency and low noise sensitivity, allowing for high-precision inversion of relative permittivity and conductivity. The inversion results show that it, in particular, significantly improves the accuracy of conductivity inversion. [ABSTRACT FROM AUTHOR]

The increasing variety and frequency of subgrade defects in operational highways have led to a rise in road safety incidents. This study employed ground-penetrating radar (GPR) detection and forward simulation to analyze the characteristic patterns of common subgrade defects, such as looseness, voids, and cavities. Through the integration of instantaneous feature information from different defect patterns with complex signal techniques, the boundary judgment of structural layers and anomalies in GPR images of various subgrade defects was improved. An intelligent recognition platform was established, and a radar image dataset was created and trained to evaluate the recognition performance of the You Only Look Once (YOLO) v3 and Single-Shot Multi-Box Detector (SSD) algorithms. Evaluation metrics such as precision, recall, F1-score, average precision (AP), and mean average precision (mAP) were used to assess the detection efficiency and accuracy for subgrade defect images. The results showed that YOLO v3 achieved an average detection accuracy of 76.69%, while the SSD achieved 75.07%. This study demonstrates that the reliability of the intelligent recognition and classification of highway subgrade defects can be enhanced by using GPR for non-destructive testing. [ABSTRACT FROM AUTHOR]

In order to address the impact of various parameters of non-metallic pipelines on the response of ground penetrating radar, the factors that affect the response of ground penetrating radar are identified. A forward model was established by combining the finite time domain difference method and the vector component finite difference method. Using the electromagnetic wave simulation software GPRMax3. 0, a large number of practical situations were simulated, and different antenna frequencies were used to detect the shape, material, burial depth, and other characteristics of different pipelines. The influence of different parameters of non-metallic pipelines on radar response was simulated using this model, where the diameter of the pipeline has a decisive impact on the echo intensity. The simulation results are consistent with the actual situation, proving the effectiveness of this model. [ABSTRACT FROM AUTHOR]

Obtaining information on tectonic tendencies is a prerequisite for intelligent and accurate mining in mines. In the special mine environment, the co‐polarized ground‐penetrating radar can only identify the spatial location of faults, and it is difficult to analyse the inclination information of fault structures. This paper proposes a mine full‐polarimetric ground‐penetrating radar fault tendency detection method based on this. First, based on the stacking characteristics of the coal depositional, this paper analyses the propagation law of the pulse electromagnetic wave in the coal seam and puts forward the assumption of the overlapping echo reflection of the fault structure. The reasonableness of the fault reflection assumption is verified through a numerical simulation study. Second, based on the cutting relationship of the fault to the coal seam, we divided the reflection structure of the fault structure into plane scattering and dihedral angle scattering. We realized the mingled echoes’ decomposition using the improved Yamaguchi decomposition technique. To analyse the applicability of the modified Yamaguchi and Freeman decomposition methods in the identification of fault inclination, we use the upright fault simulation data for the discussion, and we find that the improved Yamaguchi decomposition method is more advantageous in the identification of fault inclination in the mine. The decomposition results based on the simulation data of fault models with different dip angles found that when the dip angle of the fault is less than 90°, the scattering of the fault structure is dominated by planar scattering and dihedral angle scattering; when the dip angle of the fault is greater than or equal to 90°, the scattering of the fault structure is dominated by planar scattering, and the scattering power of the dihedral angle model is zero. By analysing the effect of fault strike on the decomposition results, it is found that the fault strike angle has little effect on the identification of fault tendency. Finally, the application potential of this paper's method is tested by constructing complex numerical models and probing experiments. Therefore, the method proposed in this paper can solve the fault tendency identification under a thick coal seam. [ABSTRACT FROM AUTHOR]

Electronics and other anthropogenic sources of noise in urban environments interfere with the early time signals of traditional transient electromagnetic (TEM) surveys due to the mutual inductance effect of transmitter and receiver coils. This poses problems for the detection of shallow geohazards such as karst dissolution features that lead to the subsidence and subsequent damage to infrastructure. The opposing-coils transient electromagnetic method (OCTEM) provides an alternative to traditional TEM surveys that is less sensitive to anthropogenic noise, and which is applied in this study to characterize shallow geohazards in a residential area responsible for subsidence and ground collapse. An investigation in Xiacun Town, China, was supplemented by ground-penetrating radar (GPR), drilling, and groundwater level monitoring to verify the OCTEM results and develop a conceptual model relating site hydrogeological factors to the ground collapse. OCTEM accurately identified shallow Quaternary gravel aquifers across the study area. However, OCTEM failed to identify additional subsidence structures near the collapsed pit demonstrated by the GPR results or the presence of a large, soil-filled cave below the pit determined from drilling. The site was concluded to be at further risk of subsidence and ground collapse associated with groundwater erosion driven by extreme precipitation events and excessive groundwater abstraction. [ABSTRACT FROM AUTHOR]

This study presents a procedure in which 3D electrical resistivity tomography (ERT) and ground-penetrating radar (GPR) were used to determine singularities in the foundations of protected historic towers, where space is limited due to their characteristics and location in highly populated areas. This study was carried out on the Tower of the Cathedral "Santa Iglesia Catedral de Santa María" in Murcia, Spain. The novel distribution of a continuous nonlinear profile along the outer and inner perimeters of the Tower allowed us to obtain a 3D ERT model of the subsoil, even under its load-bearing walls. This nonlinear configuration of the electrodes allowed us to reach adequate investigation depths in buildings with limited interior and exterior space for data collection without disturbing the historic structure. The ERT results were compared with GPR measurements and with information from archaeological excavations conducted in 1999 and 2009. The geometry and distribution of the cavities in the entire foundation slab of the Tower were determined, verifying the proposed procedure. This methodology allows the acquisition of a detailed understanding of the singularities of the foundations of protected historic towers in urban areas with limited space, reducing time and costs and avoiding the use of destructive techniques, with the aim of implementing a more efficient and effective strategy for the protection of other tower foundations. [ABSTRACT FROM AUTHOR]

Radar polarimetry is a technique that can be used to enhance target detection, identification and classification; however, the quality of these measurements can be significantly influenced by the characteristics of the radar antenna. For an accurate and reliable system, the calibration of the antenna is vitally important to mitigate these effects. This study presents a methodology to calibrate Ultra-Wideband (UWB) dual-polarised antennas in the near-field using a thin elongated metallic cylinder as the calibration object. The calibration process involves measuring the scattering matrix of the metallic cylinder as it is rotated, in this case producing 100 distinct scattering matrices from which the calibration parameters are derived, facilitating a robust and stable solution. The calibration procedure was tested and validated using a Vector Network Analyser (VNA) and two quad-ridged antennas, which presented different performance levels. The calibration methodology demonstrated notable improvements, aligning the performance of both functioning and under-performing antennas to equivalent specifications. Mid-band validation measurements indicated minimal co-polar channel imbalance (<0.3 dB), low phase error (<0.8°) and improved cross-polar isolation (≈48 dB). [ABSTRACT FROM AUTHOR]

Hyperbolae in radargrams are caused by a variety of small subsurface objects. The analysis of their curvature enables the determination of propagation velocity in the subsurface, which is important for exact time-to-depth conversion and migration and also yields information on the water content of the soil. Using deep learning methods and fitting (DLF) algorithms, it is possible to automatically detect and analyze large numbers of hyperbola in 3D Ground-Penetrating Radar (GPR) datasets. As a result, a 3D velocity model can be established. Combining the hyperbola locations and the 3D velocity model with reflection depth sections and timeslices leads to improved archaeological interpretation due to (1) correct time-to-depth conversion through migration with the 3D velocity model, (2) creation of depthslices following the topography, (3) evaluation of the spatial distribution of hyperbolae, and (4) derivation of a 3D water content model of the site. In an exemplary study, we applied DLF to a 3D GPR dataset from the multi-phased (2nd to 12th century CE) archaeological site of Goting on the island of Föhr, Northern Germany. Using RetinaNet, we detected 38,490 hyperbolae in an area of 1.76 ha and created a 3D velocity model. The velocities ranged from approximately 0.12 m/ns at the surface to 0.07 m/ns at approx. 3 m depth in the vertical direction; in the lateral direction, the maximum velocity variation was ±0.048 m/ns. The 2D-migrated radargrams and subsequently created depthslices revealed the remains of a longhouse, which was not known beforehand and had not been visible in the unmigrated timeslices. We found hyperbola apex points aligned along linear strong reflections. They can be interpreted as stones contained in ditch fills. The hyperbola points help to differentiate between ditches and processing artifacts that have a similar appearance as the ditches in time-/depthslices. From the derived 3D water content model, we could identify the thickness of the archaeologically relevant layer across the whole site. The layer contains a lot of humus and has a high water retention capability, leading to a higher water content compared to the underlying glacial moraine sand, which is well-drained. [ABSTRACT FROM AUTHOR]

Crosshole ground penetrating radar (GPR) is an efficient method for ensuring the quality of retaining structures without the need for excavation. However, interpreting crosshole GPR data is time-consuming and prone to inaccuracies. To address this challenge, we proposed a novel three-dimensional (3D) reconstruction method based on a generative adversarial network (GAN) to recover 3D permittivity distributions from crosshole GPR images. The established framework, named CGPR2VOX, integrates a fully connected layer, a residual network, and a specialized 3D decoder in the generator to effectively translate crosshole GPR data into 3D permittivity voxels. The discriminator was designed to enhance the generator's performance by ensuring the physical plausibility and accuracy of the reconstructed models. This adversarial training mechanism enables the network to learn non-linear relationships between crosshole GPR data and subsurface permittivity distributions. CGPR2VOX was trained using a dataset generated through finite-difference time-domain (FDTD) simulations, achieving precision, recall and F1-score of 91.43%, 96.97% and 94.12%, respectively. Model experiments validate that the relative errors of the estimated positions of the defects were 1.67%, 1.65%, and 1.30% in the X-, Y-, and Z-direction, respectively. Meanwhile, the method exhibits noteworthy generalization capabilities under complex conditions, including condition variations, heterogeneous materials and electromagnetic noise, highlighting its reliability and effectiveness for practical quality assurance of retaining structures. [ABSTRACT FROM AUTHOR]

The underground detection environment in coal mines is complex, with numerous interference sources. Traditional ground-penetrating radar (GPR) methods suffer from limited detection range, high noise levels, and weak deep signals, making it extremely difficult to accurately identify geological structures without stable feature feedback. During research, it was found that the detection energy of the same target significantly changes with the antenna direction. Based on this phenomenon, this paper proposes a geological radar advanced detection method using spatial scanning. This method overcomes constraints imposed by the underground coal mine environment on detection equipment, enhancing both detection range and accuracy compared to traditional approaches. Experiments using this method revealed pea-shaped response characteristics of planar geological structures in radar images, and the mechanisms behind their formation were analyzed. Additionally, this paper studied the changes in response characteristics under changes in target inclination, providing a basis for understanding the spatial distribution of geological structures. Finally, application experiments in underground coal mine environments explored the practical potential of this method. Results indicate that, compared to drilling data, this method achieves identification accuracies of 91.88%, 90.42%, and 78.72% for the depth and spatial extent of geological structures, providing effective technical support for coal mining operations. [ABSTRACT FROM AUTHOR]

This paper investigates the use of ground-penetrating radar (GPR) technology for detecting shallow buried objects, utilizing an air-coupled stepped frequency continuous wave (SFCW) radar system that operates within a 2 GHz bandwidth starting at 500 MHz. Different GPR data acquisition methods for air-coupled systems are compared, specifically down-looking, side-looking, and circular acquisition strategies, employing the back projection algorithm to provide focusing of the acquired GPR data. Experimental results showed that the GPR can penetrate up to 0.6 m below the surface in a down-looking mode. The developed radar and the back projection focusing algorithm were used to acquire data in the side-looking and circular mode, providing focused images with a resolution of 0.1 m and detecting subsurface objects up to 0.3 m below the surface. The proposed approach transforms B-scans of the GPR-based data into 2D images. The provided approach has significant potential for advancing shallow object detection capabilities by transforming hyperbola-based features into point-like features. [ABSTRACT FROM AUTHOR]

Coal–rock interface recognition is one of the key challenges in advancing unmanned rapid tunneling systems. To address this, a method based on variations in ground-penetrating radar (GPR) reflection wave amplitude is proposed for identifying coal-rock interfaces in soft coal seams. A mathematical model derived from Maxwell's equations calculates the coal seam area based on the reflected wave amplitude. Experiments using a 400 MHz radar antenna show that the method achieves identification errors of 3.58 cm, 3.74 cm, and 4.23 cm for coal-to-rock ratios of 1:2, 1:1, and 2:1, respectively. In practical applications within a coal mine excavation face, the identification error is 6.64 cm. This method provides crucial data for drum height adjustment and cutting arm speed regulation in unmanned tunneling systems. [ABSTRACT FROM AUTHOR]

Shrinking of intermontane basins and expansion of their flanking ranges by reverse faulting and backthrusting in two counter-dipping systems is a typical mechanism of crustal shortening and mountain building in Central Asia. This mechanism is realized along the Kurai Fault Zone (southeastern Gorny Altai). Motions on two reverse fault systems maintained thrusting of the Kurai Range and the Kubadru Uplift on the Kurai Basin sediments and caused the growth of a foreberg before the mountain front. Forberg separates narrow Aktash Basin from the Kurai Basin. The paleoearthquakes were generated by reverse faults that delineate the foreberg. Analysis of the QuickBird satellite images, drone imagery, trenching, archaeoseismological research, radiocarbon dating, dendrochronology, and previous results show that eleven large (МW = 6.5–7.6) paleoearthquakes left traces as surface ruptures along the Kurai Fault Zone: twice before 7.5 ka BP, three events between 7.5 and 5.9 ka BP (7.0, 6.3, and 5.9–5.8 ka BP), one from 5.8 to 4.6 ka BP, four more at 4.6, 3.2, 1.5, and 1.3–1.2 ka BP, and the ultimate earthquake no older than 1450–1650 AD. The time difference between large earthquakes was from 200 to 1700 years. Surface faulting occurred mainly along the northern border of the foreberg where fault scarps are progressively younger northward and the Cenozoic sediments of the Aktash Basin thus become involved into uplift. GPR data to a depth of 12 m confirm the complex structure and slip geometry of the observed surface ruptures. The fault scarps are located < 1 km from the planned route of the gas pipeline from Russia to China, and the potential seismic hazard has to be taken into account in its design and construction. [ABSTRACT FROM AUTHOR]

To address low detection accuracy and speed due to the multisolvability of the ground‐penetrating radar signal, we proposed a novel centralized feature pyramid‐YOLOv6l–based model to enhance detection precision and speed in road damage and pipeline detection. The centralized feature pyramid was used to obtain rich intra‐layer features and improve the network performance. Our proposed model achieves higher accuracy compared with the existing detection models. We also built two new evaluating indexes, relative average precision and relative mean average precision, to fully evaluate the detection accuracy. To verify the applicability of our model, we conducted a road field detection experiment on a ground‐penetrating radar dataset we collected and found that the proposed model had good performance in increasing detection precision, achieving the highest mean average precision compared with YOLOv7, YOLOv5 and YOLOx models, with relative mean average precision and frame rate per second at 16.38% and 30.5%, respectively. The detection information for the road damage and pipeline were used to conduct three‐dimensional imaging. Our model is suitable for object detection in ground‐penetrating radar images, thereby providing technical support for road damage and underground pipeline detection. [ABSTRACT FROM AUTHOR]

Water seepage in tunnel lining cracks considerably influences the structural safety of a tunnel. In this study, chitosan was introduced in the enzyme-induced carbonate precipitation (EICP) to repair the cracks in in-service tunnel linings. The influence of chitosan incorporation on the Ca2+ precipitation ratio during EICP was analyzed by aqueous solution experiments, and the optimal content of added chitosan was obtained. Moreover, in terms of specimen scale and field tests, the determination of permeability characteristics, and mass loss, scanning electron microscopy and ground penetrating radar technology were used to analyze the changes in permeability coefficient and mass loss of cracked concrete repaired by chitosan-combined EICP under normal temperature (25°C±2°C) and freeze–thaw (FT) cycling conditions. The effect, feasibility, and action mechanism of chitosan-combined EICP for tunnel crack repair in extreme environments were explored. The results showed that the incorporation of an appropriate amount of chitosan in traditional EICP could accelerate Ca2+ precipitation, provide nucleation sites for the precipitation of CaCO3 , promote the existence of the deposited CaCO3 crystals in the form of calcite with higher strength, and reduce mass loss in extreme circumstances. The combination of hydrogel and CaCO3 makes the impermeable layer more compact and reduces the permeability coefficient of repair concrete. The permeability coefficient descends exponentially with the decrease of mass loss under FT conditions. Chitosan-combined EICP represents an environmentally friendly and feasible method for crack repair in in-service tunnel linings. [ABSTRACT FROM AUTHOR]

For ground-penetrating radar (GPR) detection, multiphase heterogeneous pavement models were built and on-site detections were conducted. The statistic distribution rules of relative permittivity in pavement materials including asphalt mixture and cement-stabilized macadam were determined, and the heterogeneous error correction method of the layer thickness and distress buried depth were studied. The heterogeneous interferences in the A-scan curve before and after interlayer reflection wave filtering were quantified. The relationship between the crack width and homogeneous reflection wave was proposed, and the heterogeneous interference on the identification of the internal crack in the surface layer was discussed. The results show that the relative permittivity values of the surface and base layers obey Gaussian distribution obviously. Heterogeneous error formulas of the layer thickness and distress buried depth were proposed, and the concept of assurance rate was introduced in the error correction. The heterogeneous interference ratio (HIR) index was proposed to represent the heterogeneous interference in A-scan curves, and its Gaussian distribution characteristics were put forward. The heterogeneous interference increases with the wave frequency increase, and the standard deviation of HIR in the surface layer is higher than that in the base layer because of its finer gradation. The average value subtraction method filters the interlayer boundary reflection wave, which has no or minimal influence on the mean value and standard deviation of HIR, respectively. The crack interference ratio (CIR) index was proposed to quantify the heterogeneous interference on the crack reflection wave. Higher wave frequency, wider crack width, and higher water content of the crack medium are conducive to decrease the heterogeneous interference on the crack identification. The reflection waves of the millimeter-scale internal cracks have close magnitude compared with the heterogeneous interference wave, so filtering the heterogeneous interference wave is the premise to identify the millimeter-scale internal cracks in the surface layer. [ABSTRACT FROM AUTHOR]

Madīnat Ilbīra was the capital of one of the territorial organization units of the Umayyad state. The city was founded in the second half of the ninth century, and then abandoned in the eleventh century as a result of the collapse of the Caliphate of Córdoba and the transfer of the centre of the region to Granada. Its remains were located only in the nineteenth century. Modern excavations and non-invasive geophysical prospections carried out in this century allow the reconstruction of the spatial system of the city in the era of its greatest prosperity, the location of the citadel and the main mosque, and the reconstruction of the city's buildings. [ABSTRACT FROM AUTHOR]

Many machine learning (ML)-based detection methods for interpreting ground-penetrating radar (GPR) data of concrete tunnels require extensive labeled damage-state data for model training, limiting their practical use in concealed damage detection of in-service tunnels. This study presents a probabilistic, data-driven method for GPR-based damage detection, which exempts the requirement in the training process of supervised ML models. The approach involves extracting a radar feature vector (RFV), building a Bayesian baseline model with healthy data, and quantifying damage severity with the Bayes factor. The RFV is a complex vector obtained by radargram data fusion. Bayesian regression is applied to build a model for the relationship between real and imaginary parts of the RFV. The Bayes factor is employed for defect identification and severity assessment, by quantifying the difference between the RFV built with new observations and the baseline RFV predicted by the baseline model with new input. The probability of damage is calculated to reflect the influence of uncertainties on the detection result. The effectiveness of the proposed method is validated through simulated data with random noise and physical model tests. This method facilitates GPR-based hidden damage detection of in-service tunnels when lacking labeled damage-state data in the model training process. [ABSTRACT FROM AUTHOR]

Sorghatmesh mosque is a historical structure that was constructed in Cairo, Egypt, by Prince Saif El-Din Sorghatmesh in 1356. A dual-frequency ground-penetrating radar (GPR) with 250–700 MHz was used to investigate the subsurface of the Sorghatmesh mosque for restoration purposes. A total of 37 lines were surveyed on the ground floor of the mosque. The subsurface utilities were detected, and the status of the concrete base and the medium of the ground floor were assessed. A set of subsurface anomalies were detected and interpreted within the ground floor area of the mosque. In order to validate the interpretation, a trial pit was drilled on the ground floor, allowing for the visual inspection of the subsurface, and a Georadar numerical simulation was carried out to study the responses of the subsurface materials and conditions. For a better comprehension of the results, the ground floor area was categorized into five zones where the GPR interpretations between survey lines are almost similar. This work not only demonstrates the effectiveness of GPR as a non-invasive investigation tool but also highlights the potential of integrating advanced technologies into cultural heritage preservation by offering refined methodologies and insights for future research and restoration efforts. [ABSTRACT FROM AUTHOR]

The potential impact of emerging technologies is challenging for construction management researchers to study, as these technologies have yet to become embedded in current organisational practices. Cultural-Historical Activity Theory (CHAT) offers a method called formative interventions that may assist in this challenge. However, existing formative intervention methods are not adequately tailored to the study of emerging technologies, necessitating a more immersive engagement of the researcher-interventionist. This article proposes a renewed participatory take on the role of the researcher-interventionist and outlines the actions that researchers can undertake to investigate the future impacts of emerging technology. Specifically, we describe the interventionist role through a study of utility detection activities in which we intervened with emerging Ground Penetrating Radar (GPR) technology at twelve construction sites. We analysed our role through an inductive coding approach using interviews and field visit data. Our findings reveal five interventionist action types for intervention studies with emerging technology. These include shaping conditions, exposing tensions, supporting problem resolution, operating tools, and facilitating reflection. The action types prompted subjects to reevaluate elements of the activity system and helped describe three potential future activity systems that integrated GPR as a new tool. These findings demonstrate that a participatory take on formative interventions provides a potent means to unveil possible activity systems incorporating emerging technologies. We contribute five formal intervention action types to the literature that equip interventionist researchers with methodological tools to use CHAT in a practice-based study of emerging technologies on construction sites. [ABSTRACT FROM AUTHOR]

According to the key technology of TBM underground starting chamber group construction in metal mines, the design basis of TBM underground starting chamber group was analyzed, and the composition and layout of integrated assembly starting chamber group and split assembly starting chamber group, as well as the design and calculation method of structural parameters of each chamber were proposed. Taking the TBM project of a metal mine as an example, the structural parameters of the starting chamber group in the two schemes were obtained by comprehensively considering the TBM equipment, assembly and hoisting equipment, geological conditions and safety factors, etc. Finally, the optimal scheme was selected by analyzing the engineering quantity, construction period, etc. According to the optimal scheme, the key construction technologies of the TBM starting chamber group were introduced. The research results can provide reference for similar projects. [ABSTRACT FROM AUTHOR]

Despite improvements in nondestructive testing (NDT) technologies, the quality assurance of concrete reinforcing bar placement is still primarily conducted with conventional methodologies, which can be time-consuming, ineffective, and damaging to the concrete components. This study investigated the performance of two commercially available cover meters and one groundpenetrating radar (GPR) device. A cover meter was found to have the greatest accuracy for depths smaller than 3.19 in. (81.0 mm), while the GPR performed better for greater depths. The effect of reinforcing bar depth, diameter, and type; neighboring reinforcing bars; and concrete conditioning on the performance of the devices was quantified. The use of epoxy-coated reinforcing bar, galvanized reinforcing bar, and stainless-steel reinforcing bar were found to have a negligible effect on cover meter accuracy. A model was developed to predict the precision of the GPR post-measurement analysis given a depth and concrete dielectric constant. [ABSTRACT FROM AUTHOR]

The United Arab Emirates holds great historical importance, as evidenced by many archaeological sites, such as the Jebel Hafit Tombs and the Hili Archeological Park in Al Ain. At the western edge of Mutaredh Oasis, a major new archaeological site was discovered in 2023 during a construction project. Several important archeological features have been documented, including an earthen mosque and boundary walls, Iron Age irrigation systems, and a circular stone tomb dating to the Bronze Age. However, the eastern edge of the Mutaredh site has remained unexplored to date. Ground Penetrating Radar (GPR) has been proven to be a successful method in mapping archaeological remains. Accordingly, a high-resolution GPR survey was carried out to identify the continuity of the unearthed archeological elements and delineate new unexplored features. A comprehensive 3D model of buried archaeological features was constructed using the acquired high-resolution GPR data in the eastern part of the Mutaredh site. Several selected anomalies have been observed, similar to the uncovered features in the western part of the Mutaredh site and structures documented in other nearby archaeological sites. The geometry and extension of these anomalies have enabled the possible identification of a further two Bronze Age circular tombs, as well as the delineation of a system of water channels (falaj), and irrigation networks with tree pits from the Iron Age. Moreover, walls probably belonging to the Late Islamic Age are identified. These findings suggest that Mutaredh has been a site of intensive human activity from the Bronze Age through to the Late Islamic period. Given the density of identified anomalies, further significant features are anticipated to lie buried in the immediate surrounding areas, promising continued insights into the area's rich archaeological heritage. The findings of this study may guide archaeologists to specific locations and assist in selecting the most appropriate excavation techniques for the verification stage. [ABSTRACT FROM AUTHOR]

ABSTRACT Electrical resistivity tomography (ERT) is nowadays widely used in archaeological prospection. This study deals with an approach to make ERT more time‐efficient and flexible. It is based on calculating arbitrary 4‐point configurations by superposition of multiple pole–pole measurements. Investigating its applicability for archaeological purposes is the objective of this work. To do so, a synthetic study and a case study are conducted to gain insights into effects of nonideal field conditions, noise susceptibility and other challenges during processing and interpretation. Remains of an early modern manor in Noer served as an exemplary object of investigation. Their high resistivity contrast in relation to the surrounding soil makes them ideal for a functionality test. Beforehand, ground penetrating radar measurements were carried out to constrain the forward model used in the synthetic study. It turns out that the pole–pole conversion is well applicable for archaeological prospection under some conditions. The synthetic study shows that the approach is relatively prone to systematic errors. Therefore, it is recommended to locate the external electrodes at a distance of at least 0.7 times (preferably 1.7 times) the maximum internal electrode spacing from the area of investigation. Other error sources like nonideal electrode coupling must be excluded to keep relative noise levels below 1%. The pole–pole conversion can be considered reliable for absolute noise levels below 0.3 mV. Therefore, an A/D converter resolution of, for example, 16‐bit should be sufficient for a dynamic range of ±10 V. If all conditions are met, the pole–pole conversion has a great potential to make ERT more time‐efficient (up to 50%, depending on configuration sets) and flexible, as it allows to calculate nearly every arbitrary 4‐point configuration in the given setup. Combined with optimization approaches like the ‘Compare R’ method, data sets can also be adapted for specific (archaeological) questions or conditions. [ABSTRACT FROM AUTHOR]

In this article, the inverse problems for the wave equation in a medium in which multiple types of cavities and inclusion exist in a mixture are considered. From the point of view of the indicator function of the enclosure method, there are two types of heterogeneous parts: “minus group” and “plus group.” For example, cavities with the Dirichlet boundary condition belong to the minus group, while inclusions with smaller propagation velocity belong to the plus group. The heterogeneous part of the minus group gives a negative sign to the indicator function, and the heterogeneous part of the plus group gives a positive sign. In general, the presence of many types of heterogeneous parts causes cancelation of the sign of the indicator function. Such cases are referred to as “mixed cases.” Here, we consider the case that the shortest length obtained from the indicator function is attained only by heterogeneous parts of the same group. This case is called the “mixed but separated case,” and it is shown that the method of elliptic estimates developed by Ikehata works well. We also show that the case of a two‐layered background medium with a flat layer can be considered in the same way as the case of a homogeneous background medium. [ABSTRACT FROM AUTHOR]

This study addresses the features of the internal structure of the geological layers adjacent to the Polychrome Hall ceiling of the Cave of Altamira (Spain) and their link to the distribution of moisture and geological discontinuities mainly as fractures, joints, bedding planes and detachments, using 3D Ground Penetrating Radar (GPR) mapping. In this research, 3D GPR data were collected with 300 MHz, 800 MHz and 1.6 GHz center frequency antennas. The data recorded with these three frequency antennas were combined to further our understanding of the layout of geological discontinuities and how they link to the moisture or water inputs that infiltrate and reach the ceiling surface where the rock art of the Polychrome Hall is located. The same 1 × 1 m2 area was adopted for 3D data acquisition with the three antennas, obtaining 3D isosurface (isoattribute-surface) images of internal distribution of moisture and structural features of the Polychrome Hall ceiling. The results derived from this study reveal significant insights into the overlying karst strata of Polychrome Hall, particularly the interface between the Polychrome Layer and the underlying Dolomitic Layer. The results show moisture patterns associated with geological features such as fractures, joints, detachments of strata and microcatchments, elucidating the mechanisms driving capillary rise and water infiltration coming from higher altitudes. The study primarily identifies areas of increased moisture content, correlating with earlier observations and enhancing our understanding of water infiltration patterns. This underscores the utility of 3D GPR as an essential tool for informing and putting conservation measures into practice. By delineating subsurface structures and moisture dynamics, this research contributes to a deeper analysis of the deterioration processes directly associated with the infiltration water both in this ceiling and in the rest of the Cave of Altamira, providing information to determine its future geological and hydrogeological evolution. [ABSTRACT FROM AUTHOR]

We present a technique for the detection of vertebrate skeletons buried at shallow depths through the use of a ground-penetrating radar (GPR). The technique is based on the acquisition of high-resolution data by medium-to-high frequency GPR antennas and the analysis of the radar profiles by a new forward modelling method that is applied on a set of representative traces. This approach allows us to obtain synthetic traces that can be used to build detailed reflectivity diagrams that plot spikes with a distinct amplitude and polarity for each reflector in the ground. The method was tested in a controlled experiment performed at the top of Cerro Los Quesos, one of the most fossiliferous localities in the Ica Desert of Peru. We acquired GPR data at the location of a partially buried fossil skeleton of a large whale and analyzed the reflections associated with the bones using the new technique, determining the possible signature of vertebrae, ribs, the cranium (including the rostrum), and mandibles. Our results show that the technique is effective in the mapping of buried structures, particularly in the detection of tiny features, even below the classical (Ricker and Rayleigh) estimates of the vertical resolution of the antenna in civil engineering and forensic applications. [ABSTRACT FROM AUTHOR]

Aiming to address the issue of significant artefact interference in conventional reverse time migration (RTM) imaging under zero-time imaging conditions of ground-penetrating radar (GPR), this study proposes an improvement of RTM using cross-correlation superposition projection (CSP) to enhance imaging quality. The method is based on the original RTM imaging framework, where all single-trace signals are individually processed using RTM. The individual imaging results are then multiplied pairwise and added together to create a CSP. After normalizing the CSP and properly setting the adaptive threshold, the CSP can focus RTM imaging on the GPR signal reflection interface area, effectively removing artefact interference. Synthetic data test confirms that the CSP-improved RTM, when compared to conventional RTM and Laplace filtering methods, not only preserves all effective information in imaging but also eliminates artefact interference. This improvement significantly enhances the quality and resolution of the imaging results. Furthermore, the practicality and effectiveness of the CSP-improved RTM method in engineering applications have been validated using a set of measured GPR data. [ABSTRACT FROM AUTHOR]

The RIMFAX ground‐penetrating radar (GPR) on Mars2020 Perseverance Rover is the first GPR operated on the Martian surface since February 2021, searching for stratigraphy beneath the Jezero crater. During its operations, GPR detected several strong reflectors extending from the exposed section of the Séitah formation down to depths of 15 m, with derived relative permittivity of ∼9.0 consistent with low‐porosity mafic rocks. We reprocessed all the SHAllow RADar (SHARAD) observations at a higher resolution and combined repeat‐passes coherently and/or incoherently for clutter mitigation. We then examined the bright returns searching for subsurface structures. The reprocessed data did not show any shallow reflectors like those detected by RIMFAX. We investigated possible factors influencing the lack of shallow reflectors in SHARAD radargrams, including the properties of the older volcanic lithologies and the significant variability of subsurface reflectors within the SHARAD km‐wide spatial footprint, which prevents the formation of coherent reflections. Plain Language Summary: The detection of subsurface sequences by RIMFAX beneath the Jezero crater has sparked renewed debate about the possibility of detecting the boundaries between the crater's units from orbit using the Shallow Radar instrument. We have taken full advantage of the entire SHARAD coverage to examine the near‐subsurface regions beneath the Jezero crater, looking for subsurface returns. The detection of the reflectors recorded by SHARAD may be compromised by the spurious radar returns, which tend to mask the subsurface signals. Therefore, advanced processing specifically designed for resolution improvement and clutter mitigation have been applied to the entire sounder data set prior to searching for interfaces. Despite the signal enhancements, our survey of 56 observations found no returns from the subsurface stratigraphy. The lack of detections may be ascribed to several aspects, including the properties of the rough ancient materials that characterize the crated floor and the physical limitations of the orbital observations. Key Points: RIMFAX‐detected interfaces are within SHARAD's ranges; however, no reflector can be identified in standard radar data at overlap regionsResolution improvements and clutter mitigation techniques still highlight a lack of detectable subsurface reflectionsRough surface, volume scattering and attenuation over older volcanics (>1.5 Ga) significantly inhibits orbital data interpretation [ABSTRACT FROM AUTHOR]

Time-lapse ground-penetrating radar (GPR) surveys, combined with automated infiltration experiments, provide a non-invasive approach for investigating the distribution of infiltrated water within the soil medium and creating three-dimensional images of the wetting bulb. This study developed and validated an experimental protocol aimed at quantifying and visualizing water distribution fluxes in layered soils under both unsaturated and saturated conditions. The 3D images of the wetting bulb significantly enhanced the interpretation of infiltration data, enabling a detailed analysis of water movement through the layered system. We used the infiltrometer data and the Beerkan Estimation of Soil Transfer parameters (BEST) method to determine soil capacitive indicators and evaluate the physical quality of the upper soil layer. The field survey involved conducting time-lapse GPR surveys alongside infiltration experiments between GPR repetitions. These experiments included both tension and ponding tests, designed to sequentially activate the soil matrix and the full pore network. The results showed that the soil under study exhibited significant soil aeration and macroporosity (represented by AC and pMAC), while indicators related to microporosity (such as PAWC and RFC) were notably low. The RFC value of 0.55 m3 m−3 indicated the soil's limited capacity to retain water relative to its total pore volume. The PAWC value of 0.10 m3 m−3 indicated a scarcity of micropores ranging from 0.2 to 30 μm in diameter, which typically hold water accessible to plant roots within the total porosity. The saturated soil hydraulic conductivity, Ks, values ranged from 192.2 to 1031.0 mm h−1, with a mean of 424.4 mm h−1, which was 7.9 times higher than the corresponding unsaturated hydraulic conductivity measured at a pressure head of h = −30 mm (K−30). The results indicated that the upper soil layer supports root proliferation and effectively drains excess water to the underlying limestone layer. However, this layer has limited capacity to store and supply water to plant roots and acts as a restrictive barrier, promoting non-uniform downward water movement, as revealed by the 3D GPR images. The observed difference in hydraulic conductivity between the two layers suggests that surface ponding and overland flow are generated through a saturation excess mechanism. Water percolating through the soil can accumulate above the limestone layer, creating a shallow perched water table. During extreme rainfall events, this water table may rise, leading to the complete saturation of the soil profile. [ABSTRACT FROM AUTHOR]

The main purpose of this study is the subsurface investigation of two complex geological environments focusing on the improvement of data acquisition and processing parameters regarding electric and seismic tomographic techniques. Two different study areas, in central–east Peloponnese and SE Attica, were selected, where detailed geological mapping and surface geophysical survey were carried out. The applied geophysical survey included the application of electrical resistivity tomography (ERT), seismic refraction tomography (SRT) and ground penetrating radar (GPR). The geoelectrical measurements were acquired with different arrays and electrode configurations. Moreover, various types of seismic sources were used at seventeen shot locations along the seismic arrays. For the processing of geoelectrical data, clustered datasets were created, increasing the depth of investigation and discriminatory capability. The seismic data processing included the following: (a) the creation of synthetic models and seismic records to determine the effectiveness and capabilities of the technique, (b) spectral analysis of the seismic records to determine the optimal seismic source type and (c) inversion of the field data to create representative subsurface velocity models. The results of the two techniques successfully delineated the complex subsurface structure that characterizes these two geological environments. The application of the ERT combined with the SRT are the two dominant, high-resolution techniques for the elucidation of complex subsurface structures. [ABSTRACT FROM AUTHOR]

We present new data of White Sea Coast dynamics within NE of the Gorlo Strait that were generated by satellite images, geomorphological and ground penetrating radar (GPR) profiling, aerial photography, and topographic surveys of coastal terraces and dunes. Our paleogeomorphological studies, supported by laboratory findings (diatom analysis and 14C dating), allowed us to reconstruct the morphodynamics of coastal and aeolian landforms. The obtained reconstruction enabled the evaluation of relative sea level (RSL) changes and the evolution of coastal landforms over the past ~ 3.7 cal ka BP. According to our research, sand was supplied to coastal zone and coastal dunes through scarp erosion (0.5–3.7 m/year) as well as from the shoreface, while the role of alluvial runoff is insignificant. The largest dune massifs were formed in the areas of longshore drift (from NE to SW) discharge. At the mouth of the Maida River, the barrier spit and foredune plain have been evolving from the end of the Holocene transgression (∼3.7–2.3 cal ka BP). The mean high water at this time was slightly higher (up to ∼2.5 m a.s.l.), and coastal processes were significantly more intense than current ones. Then, against a decrease in sea level and weakening sediment flows, the growth of the spit slowed. The ancient foredunes were stabilized by vegetation. Aeolian processes were activated ∼2.1 and after ∼0.8–0.7 cal ka BP. [ABSTRACT FROM AUTHOR]

The site of Amasya-Oluz Höyük was discovered during a surface survey conducted in 1999, with archaeological excavations commencing in 2007. The site has revealed cultural layers and cities belonging to significant civilizations. Through archaeological excavations at Oluz Höyük, significant findings that could reshape the religious history of Anatolia have been uncovered. In the settlement dating back to the 5th century BC, important architectural complexes related to the Fire Cult (associated with early Zoroastrianism) of the Persian Settlement have been discovered. As civil and religious structures of the Persian Settlement continue to be unearthed at the höyük, these 2,500-year-old findings are expected to add new dimensions to the religious history of the period. The richness of the archaeological artifacts uncovered so far at Oluz Höyük is evidence that the mound is a significant archaeological heritage site in Anatolia. According to the "Charter for the Protection and Management of the Archaeological Heritage" prepared by ICOMOS and adopted in Lausanne in 1990, "the protection of the archaeological heritage must be based on the fullest and most detailed knowledge possible of its nature and characteristics" (Article 4). In this context, Ground Penetrating Radar (GPR) surveys were conducted in the southern section of Trench A, located at the top of the mound, near the western edge of the religious structure remains and in the area thought to be either a civil structure or part of a temple. Two- and three-dimensional subsurface images were obtained from these surveys. The GPR studies revealed high-amplitude anomalies that displayed linear, circular, right-angled, and geometrically structured forms. Archaeological excavations were conducted in this area, uncovering the foundations of a grain storage facility that had been affected by fire. [ABSTRACT FROM AUTHOR]

The Greek marble statue of the Auriga of Mozia Island, in Sicily, is the most important artwork displayed at the Whitaker Foundation Archaeological Museum. It underwent geophysical investigations twice, in 2012 and 2021, to assess the marble's degradation. The 2012 investigation prepared the statue for transfer to the Paul Getty Museum in New York and repositioning on an anti-seismic pedestal. The 2021 investigation evaluated potential new damage before another transfer. Both investigations utilized 3D ultrasonic tomography (UST) to detect degraded marble areas and ground-penetrating radar (GPR) to identify internal discontinuities, such as fractures or lesions, and locate metal pins that were previously inserted to reassemble the statue and its pedestal. Results from the UST indicate an average marble velocity of approximately 4700 m/s, suggesting good mechanical strength, with some areas showing lower velocities (~3000 m/s) within the material's variability range. The GPR profiles demonstrated internal signal homogeneity, excluding internal fracture surfaces or lesions, and confirmed the presence of metallic pins. This study highlights the effectiveness of integrating UST and GPR for non-invasive diagnostics of marble sculptures, providing detailed insights into the marble's condition and identifying hidden defects or damage. [ABSTRACT FROM AUTHOR]

In this paper, the advantages of the joint use of MHz- and GHz-frequency band impulses when employing contactless ground penetration radar (GPR) for the remote sensing of biomass, the height of the wheat canopy, and underlying soil moisture were experimentally investigated. A MHz-frequency band nanosecond impulse with a duration of 1.2 ns (average frequency of 750 MHz and spectrum bandwidth of 580 MHz, at a level of –6 dB) was emitted and received by a GPR OKO-3 equipped with an AB-900 M3 antenna unit. A GHz-frequency band sub-nanosecond impulse with a duration of 0.5 ns (average frequency of 3.2 GHz and spectral bandwidth of 1.36 GHz, at a level of −6 dB) was generated using a horn antenna and a Keysight FieldFox N9917B 18 GHz vector network analyzer. It has been shown that changes in the relative amplitudes and time delays of nanosecond impulses, reflected from a soil surface covered with wheat at a height from 0 to 87 cm and fresh above-ground biomass (AGB) from 0 to 1.5 kg/m2, do not exceed 6% and 0.09 ns, respectively. GPR nanosecond impulses reflected/scattered by the wheat canopy have not been detected. In this research, sub-nanosecond impulses reflected/scattered by the wheat canopy have been confidently identified and make it possible to measure the wheat height (fresh AGB up to 2.3 kg/m2 and height up to 104 cm) with a determination coefficient (R2) of ~0.99 and a bias of ~−7 cm, as well as fresh AGB where R2 = 0.97, with a bias = −0.09 kg/m2, and a root-mean-square error of 0.1 kg/m2. The joint use of impulses in two different MHz- and GHz-frequency bands will, in the future, make it possible to create UAV-based reflectometers for simultaneously mapping the soil moisture, height, and biomass of vegetation for precision farming systems. [ABSTRACT FROM AUTHOR]

Assessing the compaction quality of rockfill materials is an essential link in the runway construction. However, traditional on-site limited sampling detection is not only time-consuming and labour-intensive, but also destructive. Common ground penetrating radar (GPR) calculates the relative permittivity of a material by measuring its thickness. Nonetheless, assessing the thickness of runway construction materials poses a significant challenge. To address this challenge, this paper focuses on the Hilbert -Huang transform (HHT) analysis of GPR signal of different compacting materials, and then replaces the traditional method of calculating the relative permittivity by measuring the thickness of materials. At the same time, taking the runway as an example, this work verified a crest factor (CF) index effectiveness in predicting the relative compaction of rockfill material through HHT analysis of GPR signals collected in the field, with an average error rate of 4.03%. Finally, Kriging interpolation method is used to estimate the compaction quality of any point, and the corresponding heat map of compaction quality evaluation is generated to determine the area of insufficient compaction in the construction process. Highlights The GPR method is used for non-destructive testing of the compaction quality of runway rockfill materials; Compaction quality evaluation by multiple indicators of GPR data signal processing. The influence of number of roller passes, materials mix ratios and different rockfill materials on the compaction quality of runway are analysed. The applicability of the GPR method is verified by runway construction tests. [ABSTRACT FROM AUTHOR]

We consider application of full-waveform inversion (FWI) to radio-frequency electromagnetic (EM) data. Radio-frequency imaging (RIM) is a cross-borehole technique to image EM subsurface properties from measurements of transmitted radio-frequency waves. It is used in coal seam imaging, ore exploration and various engineering and civil engineering applications. RIM operates at frequencies from 50 kHz to several tens of MHz. It differs from other geophysical EM methods, because the frequency band includes the transition between the wave propagation and diffusion regimes. RIM data are acquired in 2-D cross-hole sections in a reciprocal manner. Traditionally, radio-frequency data are inverted by straight-ray tomography because it is inexpensive and easy to implement. It is argued that due to attenuation, the sensitivity of the transmitted electric field is the strongest within the first Fresnel zone of the ray connecting the transmitter and receiver. While straight-ray tomography is a simple method to implement and fast, the nonlinearity in the relationship between model parameters and data is often strong enough to warrant nonlinear inversion techniques. FWI is an iterative high-resolution technique, in which the physical properties are updated to minimize the misfit between the measured and modelled wavefields. Full-waveform techniques have been used and extensively studied for the inversion of seismic data, and more recently, they have been applied to the inversion of ground penetrating radar data. Nonlinear inversion methods for RIM data are less advanced. Their use has been hindered by the high cost of full-wave modelling and the high conductivity contrasts of many RIM targets, and, to some extent, by the limitations of the measuring instruments. We present the first application of this methodology to perform simultaneous conductivity and permittivity inversion of RIM data. We implement the inversion in the frequency domain in two dimensions using Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimization. We analyse the sensitivity of the data to the model parameters and the parameter trade-off and validate the proposed methodology on a synthetic example with moderate conductivity variations and localized highly conductive targets. We then apply the FWI methodology to a field data set from Sudbury, Canada. For the field data set, we determine the most appropriate pre-processing steps that take into account specific peculiarities of RIM: the insufficient prior information about the subsurface and the limitations of the measuring equipment. We show that FWI is applicable under the conditions of RIM and is robust to imperfect prior knowledge: we obtain satisfactory model recoveries starting from homogeneous initial models in all of our examples. Just as other methods, FWI underestimates large conductivity contrasts due to the loss of sensitivity of the transmitted electric field to the conductivity variations as the conductivity increases above a certain level. The permittivity inside high conductors cannot be recovered, however, recovering permittivity variations in the resistive zones helps obtain better focused conductivity images with fewer artefacts. Overall, FWI produces cleaner, less noisy and higher resolution reconstructions than the methods currently used in practice. [ABSTRACT FROM AUTHOR]

Monitoring and determining the development of frozen walls is a critical aspect of ensuring safe tunnel construction during freezing periods. Traditional temperature measurements for estimating freezing front expansion are constrained due to the limited number of measurement points and their fixed distribution. Here we introduce an innovative non-destructive detection method to investigate freezing wall expansion behind tunnel shield segments. First, dielectric properties of frozen and unfrozen clay and sandy soil were measured through laboratory experiments. Second, a freezing front model was established using a combination of numerical simulations and laboratory experiments. A 600 MHz radar was employed for detection and identification. Results indicate substantial differences in the dielectric constants of frozen and unfrozen soil, which can be employed as a convincing parameter for GPR to distinguish between them. Detection results demonstrate that GPR possesses the capability to determine the development pattern and approximate location of the frozen front behind shield tunnel segments. This method exhibits significant potential as an efficient and rapid means of detecting freezing wall expansion in tunnel engineering. Highlights: The difference in dielectric properties of frozen & unfrozen soils depends on the unfrozen water content. The electromagnetic responses of freezing front were studied by numerical simulation and indoor experiment. The accuracy of detection results from ground-penetrating radar was verified by conventional thermal methods. A fast and non-destructive method for determining the development of freezing fronts behind shield tunnel segments was proposed. [ABSTRACT FROM AUTHOR]

The location of covert graves is an important but challenging part of missing persons investigations. Although traditional search techniques, such as foot searches and cadaver dogs, have proved successful, the incorporation of higher technology methods, such as geophysical techniques, can be used to increase the chances of locating covert graves. This article will present a field method for the use of two geophysical techniques, including ground penetrating radar and electrical resistivity tomography, which can successfully locate covert graves in an Australian environment. If the soil and climate conditions permit, this technical protocol can be applied to other clandestine grave search sites as well. Ultimately, by increasing chances of locating the covert grave, and by extension the missing person, a successful judicial outcome can be achieved and highly sought after answers can be provided to the family. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is a widely utilized nondestructive testing technique for the detection and assessment of internal corrosion in concrete bridge decks. However, abnormal data generated during the practical application of this technology can reduce the accuracy of concrete bridge deck corrosion assessment. Aiming at this problem, this paper analyzes some common abnormal data from actual bridges GPR data and proposes corresponding automatic algorithms for anomaly correction to enhance assessment accuracy. The automatic algorithm focuses on two main aspects: correcting anomalies in direct coupling wave amplitudes based on data statistics and mitigating the impact of abnormal data due to incorrectly picked rebar on depth correction using density clustering. The specific process of the automatic method can be divided into four steps. First, automatic rebar picking is performed based on the preprocessed GPR data. Next, data statistics analysis is implemented on the extracted rebar data to identify and correct abnormal amplitude data. Then, the true rebar data are identified for depth correction based on density clustering. Finally, the bridge deck corrosion map is generated based on the corrected rebar reflection amplitudes and rebar positions. The feasibility of this method was verified through a case study with GPR data from two in-service bridges. The results show that this method can effectively and automatically identify and correct abnormal data. Moreover, the bridge deck corrosion map obtained by the proposed method is also more accurate. It can be concluded that the proposed algorithms can be used in bridge deck corrosion detection and assessment with GPR. Practical Applications: Ground penetrating radar (GPR) is a widely utilized nondestructive testing technique for concrete bridge deck corrosion detection and assessment. However, abnormal data generated during the practical application of this technology can reduce the accuracy of concrete bridge deck corrosion assessment. Aiming at this problem, this paper proposes a set of automatic data processing procedures for anomaly correction to improve the corrosion assessment accuracy. The feasibility of the proposed algorithms was validated through a case study with GPR data from two in-service bridges. The results show that these algorithms can effectively automatically identify and correct abnormal data. Moreover, the bridge deck corrosion map obtained by these algorithms is also more accurate. It can be concluded that the proposed algorithms can be used in bridge deck corrosion detection and assessment with GPR. [ABSTRACT FROM AUTHOR]

The nondestructive testing of asphalt pavement using ground-penetrating radar (GPR) is based on studies on the dielectric properties of asphalt concrete. The equivalent circuit concept was used in this study, where different mediums are equivalent to a single multilayer medium. The capacitance of each layer medium was used to derive the composite dielectric constant. Using the open coaxial probe method, the dielectric constants of AC-13 and AC-16 concrete composed of No. 70, No. 90, and styrene-butadiene-styrene (SBS)–modified asphalt binder were measured. The results reveal that the dielectric constant of asphalt concrete falls as the binder-aggregate ratio increases, and the change in aggregate volume ratio is the most important element in determining its dielectric constant. For every 1% increase in the binder-aggregate ratio, the dielectric constant of asphalt concrete typically decreases by 0.094, while the volume ratio of asphalt binder increases by 1.39%. In addition, the volume ratios of aggregate and air decreased by 1.05% and 0.33%, respectively. When it comes to estimating the dielectric constant of asphalt concrete, the developed model outperforms the conventional dielectric model with an average relative error of roughly 1%. This model successfully improved the prediction accuracy of the dielectric properties of asphalt concrete materials, which is significant for GPR-based asphalt pavement quality assessment. [ABSTRACT FROM AUTHOR]