Ground Penetrating Radar Signal Research Articles

Evidential transformer for buried object detection in ground penetrating radar signals and interval‐based bounding box

AbstractThree‐dimensional (3D) buried object detection using ground penetrating radar (GPR) benefits from the powerful capacity of image‐wise deep neural networks. However, it still faces the challenge of information loss from raw GPR signals to two‐ and three‐dimensional images, such as the frequency‐domain information loss when normalizing GPR signals into gray‐scale images and spatial information loss when using stacked B‐ and C‐scan images to replace raw GPR signals as inputs. To solve the challenge, this study has proposed an ENNreg‐transformer model, directly using raw 3D GPR signals to perform buried object detection. In the proposed model, 3D GPR signals are first converted into sequential voxelization to obtain spatiotemporal features. The features are then aggregated by an intuition‐guided feature aggregation layer to simulate the expert behavior to analyze 3D GPR data. Finally, an evidential detection header outputs 3D interval‐based bounding boxes for buried object detection. The experiment on two 3D GPR road datasets demonstrates that the proposed model exceeds other state‐of‐the‐art models on the tasks thanks to raw 3D signals and intuition‐guided feature aggregation. In addition, the interval‐based bounding box represents the spatial bounding‐box uncertainty, which derives from the inherent limitations of GPR and deep networks.

Numerical parametric study for ballast assessment using SVM applied to GPR data

Abstract This study explores novel radar-based methodologies for ballast fouling evaluation and structural anomalies in railway structures. Ground Penetrating Radar (GPR), coupled with numerical modeling, is utilized to provide insights into ballast condition and structure. Various data processing methods, including the Matrix Pencil Method (MPM) and Full Waveform Inversion (FWI), are investigated for their effectiveness in detecting fouling. Support Vector Machine (SVM)-based machine learning approaches are employed to enhance classification accuracy. The results demonstrate that integrating raw GPR signals with MPM yields the most accurate classification results, facilitating efficient assessment of ballast condition and contributing to improved railway maintenance strategies.

Numerical modelling of bridge deck reinforcement corrosion based on analysis of GPR data

AbstractThis 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.

Improving reverse time migration of ground-penetrating radar under zero-time imaging conditions using cross-correlation superposition projection

ABSTRACT 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.

Preliminary Insights on Moisture Content Measurement in Square Timbers Using GPR Signals and 1D-CNN Models

Accurately measuring the moisture content (MC) of square timber is crucial for ensuring the quality and performance of wood products in wood processing. Traditional MC detection methods have certain limitations. Therefore, this study developed a one-dimensional convolutional neural network (1D-CNN) model based on the first 8 nanoseconds of ground-penetrating radar (GPR) signals to predict the MC of square timber. The study found that the mixed-species model exhibited effective predictive performance (R2 = 0.9864, RMSE = 0.0393) across the tree species red spruce, Dahurian larch, European white birch, and Manchurian ash (MC range 0%–133.1%), while single-species models showed even higher accuracy (R2 ≥ 0.9876, RMSE ≤ 0.0358). Additionally, the 1D-CNN model outperformed other algorithms in automatically capturing complex patterns in GPR full-waveform amplitude data. Moreover, the algorithms based on full-waveform amplitude data demonstrated significant advantages in detecting wood MC compared to those based on a traditional time–frequency feature parameter. These results indicate that the 1D-CNN model can be used to optimize the drying process and detect the MC of load-bearing timber in construction and bridge engineering. Future work will focus on expanding the dataset, further optimizing the algorithm, and validating the models in industrial applications to enhance their reliability and applicability.

Assessment of Soil Horizons and Their Matric Potential from Ground-Penetrating Radar Signal Attributes

Soil plays significant roles in different phases and in the continuous existence of human life. Its comprehensive knowledge, particularly as related to its physical characteristics, enhances its utilization, conservation, and management. The traditional methods of soil study are characterized with some pitfalls such as much time needed to perform such assessments. There are also issues of invasiveness that affect the soil structures and discrete sampling that may not reflect true spatial attributes in the outcome of such techniques. These problems are largely due to the concealing nature of soil layers that made its thorough evaluation difficult. In this study, an alternative geophysical approach has been adopted. The technique is the ground-penetrating method (GPR) that utilizes electromagnetic pulse energy via its equipment’s sensors, which can allow the investigation of soil properties, even in its concealing state. This study aimed at qualitatively evaluating the soil horizons and the matric potentials using the GPR signal attributes within the unsaturated zone with a view of having insight into the test field’s characterization. Field data measurements were obtained using MALA ProEX GPR equipment with its accessories manufactured by MALA Geosciences, Stockholm, Sweden. Evaluation of the processed field data results and computed attributes show soil characteristics variations with depth that was interpreted as the layers. This can be seen from the GPR data presentation as an image representing the subsurface of the zones of propagation of the pulse energy. Spectral analysis of the GPR signals allows for the delineation of two zones of contrasting features, which were tagged as high and low matric potentials. Although the conventional direct measurement of the matric potential was not made at the time of the study to complement and confirm the veracity of the approach, the results indicate the possibility of the approach towards a quick and in situ technique of soil investigations. Such evaluation may be valuable input in precision agriculture where accurate data are sought for implementation.

Refined Modeling of Heterogeneous Medium for Ground-Penetrating Radar Simulation

Ground-penetrating radar (GPR) has been widely used for subsurface detection and testing. Numerical simulations of GPR signal are commonly performed to aid the interpretation of subsurface structures and targets in complex environments. To enhance the accuracy of GPR simulations on heterogeneous medium, this paper proposes a hybrid modeling method that combines the discrete element method with a component fusion strategy (DEM–CFS). Taking the asphalt pavement as an example, three 3D stochastic models with distinctly different porosities are constructed by the DEM–CFS method. Firstly, the DEM is utilized to establish the spatial distribution of random coarse aggregates. Then, the component fusion strategy is employed to integrate other components into the coarse aggregate skeleton. Finally, the GPR response of the constructed asphalt models is simulated using the finite-difference time-domain method. The proposed modeling method is validated through both numerical and laboratory experiments and demonstrates high precision. The results indicate that the proposed modeling method has high accuracy in predicting the dielectric constant of heterogeneous media, as generated models are closely aligned with real-world conditions.

Accurate gain method for ground-penetrating radar signals based on stationary wavelet packet transform

In this study, we propose an accurate gain method for ground-penetrating radar (GPR) signals based on the characteristics of refined time-frequency analysis and translation invariance offered by the Stationary Wavelet Packet Transform (SWPT), combined with the conventional signal gain approach. This method aims to address the issue of low signal resolution resulting from the direct gain processing of GPR signals with a low signal-to-noise ratio (SNR). Specifically, the GPR signals are initially decomposed into appropriate wavelet packet coefficients using SWPT, wherein only those coefficients with high SNR undergo gain processing, followed by reconstruction of the signals through SWPT. By employing accurate gain processing on low SNR GPR signals acquired during concrete crack detection tests, we have confirmed that the proposed method effectively distinguishes the target reflected signals from most noise, thereby achieving accurate amplification of the desired reflected signals and significantly enhancing the GPR signals resolution under low SNR conditions.

Construction Environment Noise Suppression of Ground-Penetrating Radar Signals Based on an RG-DMSA Neural Network

Ground-penetrating radar (GPR) is often used to detect targets in a construction environment. Due to the different construction environments, the noise exhibits different characteristics on the GPR signal. When the noise is widely distributed on the GPR signal, and its spectrum and the spectrum of the active signal are aliased, it is difficult to separate and suppress the noise by traditional filtering methods. In this paper, we propose a deep learning GPR image noise suppression method based on a recursive guided and dual multi-scale self-attention mechanism neural network (RG-DMSA-NN), which uses a recursive guidance module and a dual multi-scale self-attention mechanism module to improve the feature extraction ability of the image and enhance the robustness and generalization ability in image noise suppression. Through the application of noise suppression on the synthesized test data and the GPR data actually collected by the Macao Science and Technology Museum, the advantages of this method over the traditional filtering, DnCNN and UNet noise suppression methods are demonstrated.

Experimental validation of the horizontal resolution improvement by ultra-wideband metasurfaces for GPR systems

Achieving high horizontal resolution is crucial for general non-destructive testing (NDT) applications, and in the domain of ground penetrating radar (GPR), it can be more challenging due to inefficient transmissions of electromagnetic waves into and through complex material under test (MUT). In this study, we showcase the validation results of our recently designed ultra-wideband metasurface (UWM) in improving the horizontal resolution for GPR B-Scan images. This UWM has an ultra-wide working frequency band (100%) from 1 to 3 GHz, in which high-frequency GPR signals can be enhanced and transmitted into MUT. Our fourteen-day consecutive GPR experiments demonstrate that two buried pipes with an edge-to-edge spacing of 8 cm that are hard to distinguish by traditional GPR surveys can be visually identified by depositing the UWM atop the MUT. The underlying mechanism is the sustained and improved transmission of high-frequency signals into the MUT, enabled by the removal of transmission coupling loss by the UWM at the air–MUT interface and the resulting enhanced transmission of more high-frequency components. Our simulations also provide quantitative analysis of such enhanced behavior with a nominal transmittance increase of 30% ∼50%. We believe the horizontal resolution improvement enabled by UWM will open a new corridor for high-resolution GPR system design.

Extraction of the three-dimensional architecture of deformation bands from ground-penetrating radar cubes using multiattribute analysis

Fault zones in porous siliciclastic rocks can be dominated by deformation bands (DBs), which are small-scale tabular structures that usually occur as cluster features. DBs can reduce permeability, contributing to the compartmentalization of oil reservoirs and aquifers. DBs cannot be imaged by seismic methods, but can be imaged by Ground-penetrating radar (GPR). Although DBs and host rocks share the same lithology, GPR imaging is possible because DBs can cause small vertical offsets and reduce the amplitude of the GPR signals. We present an automatic approach for extracting the three-dimensional (3D) architecture of DBs from GPR cubes using multiattribute analysis. We used a 200 MHz GPR cube surveyed on an outcrop of a sandstone formation highly impacted by DBs in the Rio do Peixe Basin, northeastern Brazil. The multiattribute analysis is based on edge evidence and sequential ant-tracking, a combination that can identify narrow zones of attenuated GPR signals. Furthermore, the 3D architecture of DBs was extracted as a geobody using an opacity balancing operator. The geological reliability and limitations of the geobody were demonstrated by comparing slices of the geobody with images of exposed DBs in similar positions, in addition to structural measurements obtained in field and in the geobody.

Bayesian optimization based extreme gradient boosting and GPR time-frequency features for the recognition of moisture damage in asphalt pavement

Moisture damage is one of the major defects in asphalt pavement, and will evolve into potholes in a short time which will affect traffic safety. Ground Penetrating Radar (GPR) is an effective non-destructive testing (NDT) method for detecting moisture damage but its data explanation replies on human experience and subjects to labor intensive. To address this issue, an automatic detection method based on extreme gradient boosting (XGBoost) combined with Bayesian hyper-parameter optimization (BHPO) was proposed to detect moisture damage area from GPR traces. High frequency GPR antenna with 2.3 GHz was used to detect the moisture damage area from simulation, laboratory and field tests, and moisture damage dataset with 7960 traces was collected. Thirty time-frequency parameters were extracted from each GPR trace, normalized to unify the three data source, and then optimized into 12 sensitive parameters by feature importance method. These 12 parameters were used to build the recognition model with XGBoost, and the model tuning parameters were optimized by BHPO. To obtain optimization model, random forest (RF) and artificial neural network (ANN) were also trained with BHPO, and compared with XGBoost model. The results indicate that performance of XGBoost model with BHPO achieves the highest accuracy and lowest time cost both in moisture damage and normal trace classification, the accuracies for moisture damage are XGBoost (96.9%) > ANN (95.6%) > RF (95.4%), respectively, and normal are XGBoost (96.5%) > RF (96.1%) and ANN (96.0%), respectively. On this basis, field tests were conducted by core samples, which verified the correct result of XGBoost model. Our method provides a swift and accurate method to locate subsurface targets directly from GPR signals.

Evaluation of the variation of the GPR frequency spectra created by the activities of earthworms

The analysis and utilization of frequency spectrum in emission signals from ground penetrating radar (GPR) are essential for investigating soil characteristics and enhancing urban management. This research focuses on assessing the effectiveness of earthworm activities in improving rainwater management in regions with clay soil, where water infiltration is challenging. Specifically, we examined the impact of two earthworm species, Lumbricus terrestris and Aporrectodea caliginosa, as well as a combination of both, on the permeability of clay loam soil. To evaluate earthworm activities, a gravity-driven film flow setup with nine tanks was employed. Furthermore, we suggest utilizing the Discrete Fourier transform to analyse the frequency spectra of different data types (traces, transects, and the average of tanks) and comprehend their influence on earthworm behavior. To investigate the relationship between fluctuations in infiltration velocity and the frequency spectrum signal over time and activity position, a correlation analysis was performed using linear regression models. The goodness of fit was assessed based on the coefficient of determination (R2). For the earthworm species L1, L2, and L12, the R2 values were determined as 0.8858, 0.847, and 0.9493, respectively. These results demonstrate that the frequency domain was derived from variations in ground penetrating radar signals, and a significant correlation exists between infiltration velocity and spectra influenced by earthworm activity.

Rock Layer Classification and Identification in Ground-Penetrating Radar via Machine Learning

Ground-penetrating radar (GPR) faces complex challenges in identifying underground rock formations and lithological structures. The diversity, intricate shapes, and electromagnetic properties of subsurface rock formations make their accurate detection difficult. Additionally, the heterogeneity of subsurface media, signal scattering, and non-linear propagation effects contribute to the complexity of signal interpretation. To address these challenges, this study fully considers the unique advantages of convolutional neural networks (CNNs) in accurately identifying underground rock formations and lithological structures, particularly their powerful feature extraction capabilities. Deep learning models possess the ability to automatically extract complex signal features from radar data, while also demonstrating excellent generalization performance, enabling them to handle data from various geological conditions. Moreover, deep learning can efficiently process large-scale data, thereby improving the accuracy and efficiency of identification. In our research, we utilized deep neural networks to process GPR signals, using radar images as inputs and generating structure-related information associated with rock formations and lithological structures as outputs. Through training and learning, we successfully established an effective mapping relationship between radar images and lithological label signals. The results from synthetic data indicate a rock block identification success rate exceeding 88%, with a satisfactory continuity identification of lithological structures. Transferring the network to measured data, the trained model exhibits excellent performance in predicting data collected from the field, further enhancing the geological interpretation and analysis. Therefore, through the results obtained from synthetic and measured data, we can demonstrate the effectiveness and feasibility of this research method.

Detection of air- and water-filled cavities beneath concrete plate using electromagnetic and acoustic waves

Underground excavation and tunneling may result in cavities filled with air or water. This study aims to detect cavities beneath underground concrete walls or tunnel linings and assess their risks by estimating the cavity thickness and width using electromagnetic and acoustic waves. In this study, air- and water-filled cavities are simulated, and nondestructive testing methods such as ground penetrating radar (GPR), time domain reflectometry (TDR), and microphones are used to investigate the thickness and width of a cavity beneath a reinforced concrete plate. The results show that GPR signals converted from temporal to spatial scales, combined with TDR measurements, can be used to accurately examine the cavity thickness. However, the examination of the cavity width based on GPR signals remains challenging because of the strong parabolic patterns caused by steel bars. Therefore, this study employs flexibility, which is the ratio of the particle velocity of the structure to the load signal according to the frequency. The flexibility of the concrete plate calculated using microphone signals increases in the cavity section for both air- and water-filled cavities because the flexural vibration mode prevails at the cavity–concrete plate interface. The characteristics of the microphone signals analyzed in the context of flexibility enabled the prediction of the cavity width beneath the concrete plate in both air- and water-filled cavities. GPR and TDR signatures combined with microphone signals can be used for the detection and risk assessment of cavities beneath underground structures when subjected to variations in the groundwater level.

Imaging tree root systems using ground penetrating radar (GPR) data in Brazil

Trees sequester carbon dioxide from the atmosphere through photosynthesis, storing it in branches, stems, and roots, where the belowground carbon fraction, approximately ¼ of the total amount, exhibits significant interspecies root biomass variability. Estimating the amount of carbon stored in tree roots of different species is key to understanding an important aspect of climate change and exploring how natural forests, urban tree planting policies, and reforestation projects might help to address it. In this context, one of the most prominent Non-Destructive Testing methods capable of estimating the diameter and length of roots at different depths is ground penetrating radar (GPR). It has been widely used for geological, archaeological, and geotechnical studies due to its accuracy in locating buried material in different contexts, although standards for the correct management of datasets related to belowground root systems are still been developed. This paper reports a GPR signal processing flow to estimate the root diameter of three species of tropical forest trees, and to demonstrate the method’s viability, a dataset was collected in a study area with a 900 MHz shielded antenna. A multi-stage data processing flow is then presented, including raw data, file format conversion, zero-time adjustment, background removal, signal gain, Stolt FK migration, and time-to-depth conversion with hyperbolic adjustment velocity. The resulting data were converted from true amplitude data to a trace envelope. High amplitudes on the envelope section, with lateral continuity in parallel sections, were interpreted as roots. However, the interpretation of outcomes encounters notable complexities, primarily attributable to the intricate nature of subsurface root architectures, the soil matrix characterized by significant clay content, and the co-occurrence of buried materials proximate to the arboreal subjects. Consequently, amplitudes discerned within ground penetrating radar (GPR) 2D sections necessitate cautious interpretation, as they are not immediately indicative of subsurface roots. To overcome this difficulty, this study used direct measurements of the roots in the field, to confirm the GPR data. Despite these complexities, the study demonstrates GPR’s efficacy, particularly in the uppermost soil layer-a pivotal carbon reservoir with a 96% correlation (R2) between GPR-derived coarse-root diameter estimates and field measurements.

Multi-Frequency GPR Data Fusion through a Joint Sliding Window and Wavelet Transform-Weighting Method for Top-Coal Structure Detection

Top-coal structure detection is an important basis for realizing effective mining in fully mechanized cave faces. However, the top-coal structure is very complex and often contains multi-layer gangues, which seriously influence the level of effective mining. For these reasons, this paper proposes a novel multi-frequency ground-penetrating radar (GPR) data-fusing method through a joint sliding window and wavelet transform weighting method to accurately detect the top-coal structure. It possesses the advantages of both high resolution and great detection depth, and it can also integrate multi-frequency GPR data into one composite profile to interpret the internal structure information of top coal in detail. The detection procedure is implemented following several steps: First of all, the multi-frequency GPR data are preprocessed and aligned through a band-pass filter and a zero offset elimination method to establish their spatial correspondences. Secondly, the proposed method is used to determine the time-varying weight values of each frequency GPR signal according to the wavelet energy proportion within the sliding window; also, the edge detection algorithm is introduced to improve the fusion efficiency of the wavelet transform so as to realize the effective fusion of the multi-frequency GPR data. Thirdly, a reflection intensity model of multi-frequency GPR signals traveling in the top-coal is established by using the stratified identification method, and then, the detailed top-coal structure can be inversely interpreted. Finally, the quantitative evaluation criteria, information entropy (IE), space–frequency (SF) and Laplacian gradient (LG), are used to evaluate the multi-frequency GPR data fusion’s effectiveness in laboratory and field environments. The experimental results show that, compared with the genetic, time-varying and wavelet transform fusion method, the fusion performance of the presented method possesses higher values in the IE, SF and LG evaluation criteria, and it also has both the merits of high resolution and great detection depth.

Studying GPR's direct and reflected waves

AbstractAs the transmitter and receiver (Tx and Rx, respectively) are located in close proximity during a typical ground‐penetrating radar (GPR) survey, the powerful signal generated by the Tx and which is then recorded by the Rx at various time delays, can be saturated at early times (i.e., this is the direct wave (DW) signal reaching the Rx). This often causes the masking of shallow targets, complicating data interpretation. In this study, our aim is to examine the spatial distribution of the electromagnetic signals around the Tx, attempting to locate areas where the DW becomes minimum, whereas the signal strength from subsurface targets (i.e., reflected wave – RW) remains ideally unchanged. The position of these local minima in the DW signal could give rise to advantageous Tx–Rx configurations, where clear reflections from subsurface targets lying at shallow depths can be obtained with the least possible involvement of the DW. To perform such a study, we carried out static field measurements over a flat lying reflector as well as numerical simulations in a reflection, common‐offset mode around a transmitting antenna. In the field, we also collected wide‐angle reflection–refraction data to determine the GPR wave velocity in the uppermost layer. GPR signals were recorded by the Rx around the Tx in three concentric circles of various radii (i.e., varying the Tx/Rx separation), using a specific angular step and varying the Tx/Rx polarization each time. The synthetic data were produced using a three‐dimensional finite‐difference time‐domain modelling tool. Field and numerically simulated data were analysed and compared to study the behaviour of both the DW and RW events around the Tx when changing the Tx/Rx distance, their respective angular position, as well as their relative polarization/orientation.

Development of a method for identifying cracks in permafrost rocks based on differentiation of GPR data

Relevance. The presence of cracks significantly affects the physical and mechanical properties of rocks, which must be taken into account when planning mining operations and building mining structures. In the conditions of the distribution of permafrost rocks, characteristic of the North-East of Russia, the study of fracturing is possible using the ground penetrating radar method, which is used to assess the structure of rock masses of placer deposits. The criteria for identifying cracks based on the characteristics of GPR wave fields are currently known, and the main problem preventing the full use of the GPR method for studying cracks in subsurface layers of rocks is the significant complexity of processing and interpreting GPR measurement data. The purpose of the work is to develop a method for processing the results of ground penetrating radar measurements to identify cracks in frozen rocks. Methods. Physical and computer modeling of GPR measurements of a rock mass with a fracture, differential calculus methods for analyzing the GPR wave field structure model. Results and their scope. As a result of the research, based on an early developed model of the structure of the GPR wave field, the use of the differentiation operation for identifying cracks in permafrost rocks was justified. Special cases of rectilinear inclined and horizontal axes of in-phase of ground penetrating radar signals are considered and corresponding formulas are obtained for them. The theoretical results obtained were tested on computer and physical modeling data, as well as on field measurement data. An analysis of the features of the signals after differentiation was carried out, and the features of the original radargrams were noted, which may interfere with the reliable interpretation of the results obtained. Conclusions. In the practice of processing data from ground penetrating radar measurements, the results of the research will make it possible to more quickly identify zones of increased fracturing in rocks. In the future, it is planned to develop an algorithm and software that implements it for mapping rock fractures using ground penetrating radar data.

Electromagnetic property selection for GPR modelling in corrosive concrete environments

Understanding the mechanisms that alter the ground penetrating radar (GPR) electromagnetic wave propagation as a result of reinforcement corrosion is pivotal for accurate assessment of the corrosion of reinforced concrete (RC) structures. Nevertheless, the behaviour of the GPR signal during the complex corrosion process is not thoroughly understood. In this study, finite-difference time-domain (FDTD) modelling was used to analyse the effects of corrosion-related parameters, i.e., moisture, chlorides, and corrosion products, on the electric field strength. This study aims to expand the database on numerical simulations of GPR signal behaviour in corrosive environments. It also addresses the knowledge gap in modelling the frequency-dependent properties of concrete and iron oxides. Modelling approach adopted in the study was validated with experimental data obtained on laboratory specimens that correspond to the numerical models in terms of geometry and condition.