Search Results (26)

Ground-Penetrating Radar (GPR) has demonstrated significant advantages in the non-destructive detection of road structural defects due to its speed, safety, and efficiency. This paper proposes a three-dimensional (3D) reconstruction method for GPR images, integrating the back-projection (BP) imaging algorithm to accurately determine the size, location, and other parameters of road structural defects. Initially, GPR detection images were preprocessed, including direct wave removal and wavelet denoising, followed by the application of the BP algorithm to effectively restore the defect’s location and size. Subsequently, a 3D data set was constructed through interpolation, and the effective reflection data were extracted by using a clustering algorithm. This algorithm distinguished the effective reflection data from the background data by determining the distance threshold between the data points. The 3D imaging of the defect was then performed in MATLAB. The proposed method was validated using both gprMax simulations and laboratory test models. The experimental results indicate that the correlation between the reconstructed and actual defects was approximately 0.67, demonstrating the method’s efficacy in accurately achieving the 3D reconstruction of road structural defects. Full article

In the structure of composite pavement, the formation of voids beneath concrete panels poses significant risks to structural integrity and operational safety. Ground-Penetrating Radar (GPR) detection serves as an effective method for identifying voids beneath concrete pavement panels. This paper focuses on analyzing the morphological features of GPR echo signals. Leveraging the GprMax numerical simulation software, a numerical simulation model for void conditions in concrete pavement is established by setting reasonable pavement structure parameters, signal parameters, and model space parameters. The reliability of the numerical simulation model is validated based on field data from full-scale test sites with pre-fabricated voids. Various void conditions, including different void thicknesses, sizes, shapes, and filling mediums, are analyzed. The main conclusions of the study are as follows: the correlation coefficient between measured and simulated signals is above 0.8; a noticeable distinction exists between echo signals from intact and voided structures; signals exhibit similar phase and time delays for different void thicknesses and sizes but significant differences are observed in the A-scan signal intensity, the signal intensity, and the width of the B-scan signal; the impact of void shape on GPR echo signals mainly manifests in the variation of void thickness at different measurement points; and the relationship between the dielectric properties of the void-filling medium and the surrounding environment dictates the phase and time delay characteristics of the echo signal. Full article

Ground-penetrating radar (GPR) is a rapid and non-destructive geophysical technique widely employed to detect and quantify subsurface structures and characteristics. Its capability for time lapse (TL) detection provides essential insights into subsurface hydrological dynamics, including lateral flow and soil water distribution. However, during TL-GPR surveys, field conditions often create discrepancies in surface geometry, which introduces mismatches across sequential TL-GPR images. These discrepancies may generate spurious signal variations that impede the accurate interpretation of TL-GPR data when assessing subsurface hydrological processes. In responding to this issue, this study introduces a TL-GPR image alignment method by employing the dynamic time warping (DTW) algorithm. The purpose of the proposed method, namely TLIAM–DTW, is to correct for geometric mismatch in TL-GPR images collected from the identical survey line in the field. We validated the efficacy of the TLIAM–DTW method using both synthetic data from gprMax V3.0 simulations and actual field data collected from a hilly, forested area post-infiltration experiment. Analyses of the aligned TL-GPR images revealed that the TLIAM–DTW method effectively eliminates the influence of geometric mismatch while preserving the integrity of signal variations due to actual subsurface hydrological processes. Quantitative assessments of the proposed methods, measured by mean absolute error (MAE) and root mean square error (RMSE), showed significant improvements. After performing the TLIAM–DTW method, the MAE and RMSE between processed TL-GPR images and background images were reduced by 96% and 78%, respectively, in simple simulation scenarios; in more complex simulations, MAE declined by 27–31% and RMSE by 17–43%. Field data yielded reductions in MAE and RMSE of >82% and 69%, respectively. With these substantial improvements, the processed TL-GPR images successfully depict the spatial and temporal transitions associated with subsurface lateral flows, thereby enhancing the accuracy of monitoring subsurface hydrological processes under field conditions. Full article

Among many agricultural practices proposed to cut carbon emissions in the next 30 years is the deposition of carbon in soils as plant matter. Adding rooting traits as part of a sequestration strategy would result in significantly increased carbon sequestration. Integrating these traits into production agriculture requires a belowground phenotyping method compatible with high-throughput breeding (i.e., rapid, inexpensive, reliable, and non-destructive). However, methods that fulfill these criteria currently do not exist. We hypothesized that ground-penetrating radar (GPR) could fill this need as a phenotypic selection tool. In this study, we employed a prototype GPR antenna array to scan and discriminate the root and rhizome mass of the perennial sorghum hybrid PSH09TX15. B-scan level time/discrete frequency analyses using continuous wavelet transform were utilized to extract features of interest that could be correlated to the biomass of the subsurface roots and rhizome. Time frequency analysis yielded strong correlations between radar features and belowground biomass (max R −0.91 for roots and −0.78 rhizomes, respectively) These results demonstrate that continued refinement of GPR data analysis workflows should yield an applicable phenotyping tool for breeding efforts in contexts where selection is otherwise impractical. Full article

Root diameter is an important indicator of plant growth and development to a large extent. However, the field monitoring of these parameters is severely limited by the lack of appropriate methods, and some traditional methods may harm the plant and its growing environment. Ground-penetrating radar (GPR) is a new nondestructive detection method for underground root systems. A new method for the estimation of the diameter of coarse roots using GPR with 900 MHz frequency was proposed in this paper. First, a simulation model was established to simulate the root system under natural conditions, and the root diameter estimation model based on the scanning results of GPR was obtained. Secondly, by studying the influence of soil and root relative permittivity on the diameter estimation model, a method was found to devise a coarse root diameter estimation model under different soil and root conditions of relative permittivity. Thirdly, the applicability of the diameter estimation model to roots with different growth orientations was tested by simulating roots with different growth orientations. Finally, the practical applicability of the estimation method was verified by field experiments. The results suggest that the root diameter estimation model can be constructed by extracting the pixel distance (∆p) of waveform parameters from the 900 MHz scanning results. This method can be used to estimate the diameter of coarse roots with diameters of no less than 2 cm and a relative permittivity greater than 5, and to estimate the diameter of roots in any orientation and soil environment effectively. At the same time, the application in the field experiment also resulted in a good estimation effect. This method provides a new opportunity to achieve more reliable root diameter estimation in complex situations. The estimation of coarse root diameter provides an experimental basis and data support for the healthy growth of trees, and also provides some information for the study of coarse root ecology. Full article

When ground penetrating radar (GPR) is used for the non-destructive evaluation of concrete bridge decks, the rebar reflection amplitudes should be corrected for rebar depths to account for the geometric spreading and material attenuation of the electromagnetic wave in concrete. Most current depth-correction methods assume a constant EM wave velocity in the entire bridge deck and correct GPR amplitudes based on the two-way travel time (TWTT) instead of the actual rebar depth. In this paper, we proposed a depth-correction algorithm based on the real rebar depths. To compare different depth-correction methods, we used gprMax software to simulate GPR signals in four models with various dielectric constants and conductivity. The comparison shows that the TWTT-based depth-correction method tends to over-correct GPR amplitudes so that underestimates the deterioration level of concrete decks at certain locations. Two depth-based correction methods are proposed that use migrated amplitudes and further normalize the corrected amplitude by rebar depth (attenuation rate). These methods are then applied to GPR data collected on two bridges, and the results were validated by other NDE methods and chloride concentration test. Full article

In this paper, we analysed the effect of trench-hill soil surface on ground-penetrating radar (GPR) full-wave inversion for soil moisture measurement. We conducted numerical experiments by modelling the trench-hill surface using finite-difference time–domain (FDTD) simulations. The FDTD simulations were carried out with the open-source software gprMax, using different centre frequencies, namely, 150 MHz, 250 MHz and 450 MHz. The gprMax source/receiver for each centre frequency was calibrated, respectively, to transform the FDTD radar signal to normalized Green’s functions for wave propagation in multilayered media. The radar signals and inversion results of the three different frequency ranges are compared. The FDTD Green’s functions of the trench-hill surface with a flat surface are also compared. The results show that the trench-hill surface only slightly affects the inversion when frequency is lower than 190 MHz, which agrees with Rayleigh’s criterion. Field measurements were performed as well, using a prototype radar mounted on an irrigation robot. The low-frequency antenna was calibrated over a large water plane. The optimal operating frequency range was set to 130–190 MHz. TDR measurements were performed as well for comparison. The results demonstrated promising perspectives for automated and real-time determination of the root–zone soil moisture in potato fields, and thereby for precise and automatic irrigation. Full article

Assessing the structure of a building with non-invasive methods is an important problem. One of the possible approaches is to use GeoRadar to examine wall structures by analyzing the data obtained from the scans. However, so far, the obtained data have to be assessed manually, relying on the experience of the user in interpreting GPR radargrams. We propose a data-driven approach to evaluate the material composition of a wall from its GPR radargrams. In order to generate training data, we use gprMax to model the scanning process. Using simulation data, we use a convolutional neural network to predict the thicknesses and dielectric properties of walls per layer. We evaluate the generalization abilities of the trained model on the data collected from real buildings. Full article

Acromegaly is caused by excessive secretion of GH and IGF-I mostly from somatotroph tumors. Various genetic and epigenetic factors are involved in the pathogenesis of somatotroph tumors. While somatic mutations of GNAS are the most prevalent cause of somatotroph tumors, germline mutations in various genes (AIP, PRKAR1A, GPR101, GNAS, MEN1, CDKN1B, SDHx, MAX) are also known as the cause of somatotroph tumors. Moreover, recent findings based on multiple perspectives of the pangenomic approach including genome, transcriptome, and methylome analyses, histological characterization, genomic instability, and possible involvement of miRNAs have gradually unveiled the whole landscape of the underlying mechanisms of somatotroph tumors. In this review, we will focus on the recent advances in genetic and epigenetic pathogenesis of somatotroph tumors. Full article

Cavity under urban roads has increasingly become a huge threat to traffic safety. This paper aims to study cavity morphology characteristics and proposes a deep learning (DL)-based morphology classification method using the 3D ground-penetrating radar (GPR) data. Fine-tuning technology in DL can be used in some cases with relatively few samples, but in the case of only one or very few samples, there will still be overfitting problems. To address this issue, a simple and general framework, few-shot learning (FSL), is first employed for the cavity classification tasks, based on which a classifier learns to identify new classes given only very few examples. We adopt a relation network (RelationNet) as the FSL framework, which consists of an embedding module and a relation module. Furthermore, the proposed method is simpler and faster because it does not require pre-training or fine-tuning. The experimental results are validated using the 3D GPR road modeling data obtained from the gprMax3D system. The proposed method is compared with other FSL networks such as ProtoNet, R2D2, and BaseLine relative to different benchmarks. The experimental results demonstrate that this method outperforms other prior approaches, and its average accuracy reaches 97.328% in a four-way five-shot problem using few support samples. Full article

In the field of geophysics and civil engineering applications, ground penetrating radar (GPR) technology has become one of the emerging non-destructive testing (NDT) methods thanks to its ability to perform tests without damaging structures. However, NDT applications, such as concrete rebar assessments, utility network surveys or the precise localization of embedded cylindrical pipes still remain challenging. The inversion of geometric parameters, such as depth and radius of embedded cylindrical pipes, as well as the dielectric parameters of its surrounding material, is of great importance for preventive measures and quality control. Furthermore, the precise localization is mandatory for critical underground utility networks, such as gas, power and water lines. In this context, innovative signal processing techniques associated with GPR are capable of performing physical and geometric characterization tasks. This paper evaluates the performance of a supervised machine learning and ray-based methods on GPR data. Support vector machines (SVM) classification, support vector machine regression (SVR) and ray-based methods are all used to correlate information about the radius and depth of embedded pipes with the velocity of stratified media in various numerical configurations. The approach is based on the hyperbola trace emerging in a set of B-scans, given that the shape of the hyperbola varies greatly with pipe depth and radius as well as with velocity of the medium. According to the ray-based method, an inversion of the wave velocity and pipe radius is performed by applying an appropriate nonlinear least mean squares inversion technique. Feature selection within machine learning models is also implemented on the information chosen from observed hyperbola travel times. Simulated data are obtained by means of the finite-difference time-domain (FDTD) method with the 2D numerical tool GprMax. The study is carried out on mono-static, ground-coupled GPR datasets. The preliminary study showed that the proposed machine learning methods outperforms the ray-based method for estimating radius, depth and velocity. SVR, for instance, calculates depth and radius values with mean absolute relative errors of 0.39% and 6.3%, respectively, with regard to the ground truth. A parametric comparison of the aforementioned methodologies is also included in the performance analysis in terms of relative error. Full article

In a context of accelerating deployment of distributed generation in power distribution grid, this work proposes an answer to an important and urgent need for better management tools in order to ‘intelligently’ operate these grids and maintain quality of service. To this aim, a model-based predictive control (MPC) strategy is proposed, allowing efficient re-routing of power flows using flexible assets, while respecting operational constraints as well as the voltage constraints prescribed by ENEDIS, the French distribution grid operator. The flexible assets used in the case study—a low-voltage power distribution grid in southern France—are a biogas plant and a water tower. Non-parametric machine-learning-based models, i.e., Gaussian process regression (GPR) models, are developed for intraday forecasting of global horizontal irradiance (GHI), grid load, and water demand, to better anticipate emerging constraints. The forecasts’ quality decreases as the forecast horizon grows longer, but quickly stabilizes around a constant error value. Then, the impact of forecasting errors on the performance of the control strategy is evaluated, revealing a resilient behaviour where little degradation is observed in terms of performance and computation cost. To enhance the strategy’s resilience and minimise voltage overflow, a worst-case scenario approach is proposed for the next time step and its contribution is examined. This is the main contribution of the paper. The purpose of the min–max problem added upstream of the main optimisation problem is to both anticipate and minimise the voltage overshooting resulting from forecasting errors. In this min–max problem, the feasible space defined by the confidence intervals of the forecasts is searched, in order to determine the worst-case scenario in terms of constraint violation, over the next time step. Then, such information is incorporated into the decision-making process of the main optimisation problem. Results show that these incidents are indeed reduced thanks to the min–max problem, both in terms of frequency of their occurrence and the total surface area of overshooting. Full article

The traditional manure management strategy, based on crop N needs, results in accumulation of phosphorus (P) in soil due to the imbalance of N/P ratio between crop requirement and manure supply. This study was conducted from 2004 to 2013 to evaluate the effects of P-based liquid and solid swine manure (LMP and SMP, for P-based liquid and solid swine manure, respectively) application, in comparison with N-based application (LMN and SMN, for N-based liquid and solid swine manure, respectively), on crop yield and soil residual P under corn (Zea mays L.)–soybean (Glycine max L.) rotation in a Brookston clay loam soil of the Lake Erie basin, ON, Canada. Chemical fertilizer P (CFP) and non-P treatments were included as controls (CK). For liquid manure treatments, corn yield for LMN showed a lower annual corn yield (7.82 Mg ha⁻¹) than LMP (9.36 Mg ha⁻¹), and their differences were even statistically significant at p < 0.05 in some cropping years. The annual corn yield of LMP was also higher than those of SMP (7.45 Mg ha⁻¹) and SMN (7.41 Mg ha⁻¹), even the CFP (8.61 Mg ha⁻¹), although the corresponding yield differences were not significant (p < 0.05) in some cropping years. For soybean, the plots with P application produced an average of 0.98 Mg ha⁻¹ greater annual yields than CK. No significant differences were found between CFP and manure treatments. The annual corn yield of SMN was close to that of the CK (7.19 Mg ha⁻¹). The grain P removal (GPR) of SMN (18.6 kg ha⁻¹) for soybean was significantly higher than that of the other treatments. The above-ground-P uptake (AGPU) in SMN, for both corn and soybean, was significantly higher than that of the other five treatments. The soil test P (STP) presented clear stratification, concentrating in the top 30 cm soil depth after 10 years of application. The contents of STP with LMN and SMN increased from 7.1 mg P kg⁻¹ to 12.4 and 45.5 mg P kg⁻¹, respectively. The sum of STP mass (0–30 cm) with LMP (31.6 kg ha⁻¹) was largely identical to that with CFP (30.1 kg ha⁻¹); however, with SMN (173.7 kg ha⁻¹), it was significantly higher than the rest of the treatments. Manure P source availability coefficients were averaged at 1.06 and 1.07 for LMP and SMP, respectively. The addition of phosphorus-based liquid or solid swine manure can overcome the drawback of traditional N-based applications by potentially reducing the adverse impact on water quality while sustaining crop agronomic production. Full article

Ground penetrating radar (GPR), as a newly nondestructive testing technology (NDT), has been adopted to explore the spatial position and the structure of the tree roots. Due to the complexity of soil distribution and the randomness of the root position in the natural environment, it is difficult to locate the root in the GPR B-Scan image. In this study, a novel method for root detection in the B-scan image by considering both multidirectional features and symmetry of hyperbola was proposed. Firstly, a mixed dataset B-Scan images were employed to train Faster RCNN (Regions with CNN features) to obtain the potential hyperbola region. Then, the peak area and its connected region were filtered from the four directional gradient graphs in the proposed region. The symmetry test was applied to segment the intersecting hyperbolas. Finally, two rounds of coordinate transformation and line detection based on Hough transform were employed for the hyperbola recognition and root radius and position estimation. To validate the effectiveness of this approach for tree root detection, a mixed dataset was made, including synthetic data from gprMax as well as field data collected from 35 ancient tree roots and fresh grapevine controlled experiments. From the results of hyperbola recognition as well as the estimation for the radius and position of the root, our method shows a significant effect in root detection. Full article

Ground-penetrating radar (GPR) signal recognition depends much on manual feature extraction. However, the complexity of radar detection signals leads to conventional intelligent algorithms lacking sufficient flexibility in concrete pavement detection. Focused on these problems, we proposed an adaptive one-dimensional convolution neural network (1D-CNN) algorithm for interpreting GPR data. Firstly, the training dataset and testing dataset were constructed from the detection signals on pavement samples of different types of distress; secondly, the raw signals are were directly inputted into the 1D-CNN model, and the raw signal features of the radar wave are extracted using the adaptive deep learning network; finally, the output used the Soft-Max classifier to provide the classification result of the concrete pavement distress. Through simulation experiments and actual field testing, the results show that the proposed method has high accuracy and excellent generalization performance compared to the conventional method. It also has practical applications. Full article