bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhancing GPR-based tunnel lining delamination detection: An unsupervised domain-adaptive YOLO network
Authors: Wang Yuanzheng , Qin Hui , Liu Jinbo , Chen Youjian , Wang Lipeng , Ren Hongyong , Meng Xianbiao , Luo Wenjiang , Gao Lei 
Published: November 2024

Abstract
The ground penetrating radar (GPR) is one of the most recommended methods for tunnel lining inspection, yet the interpreting the GPR data requires significant time and expertise. Recent attempts to automate this procedure using deep learning techniques have made significant progress, yet is still hindered by the need of extensive and accurately annotated data, which is challenging to gather in real scenarios. This paper introduces a domain-adaptive YOLO network, evolving from YOLOv7, which trains on FDTD data for source domain detection and real-world data for target domain adaption. The proposed network enhances the network’s ability to generate generalized features across domains and hence improves the detection performance on real GPR data.
Three-dimensional random medium modelling for GPR based on soil fractals
Authors: Guo Shilong , Lu Qi , Liu Sixin , Liang Wenjing , Liu Kexin 
Published: November 2024

Abstract
Ground penetrating radar (GPR) has a wide range of applications in soil detection. In this article, we model the three-dimensional (3-D) random medium in GPR based on soil fractal characteristics. We use a 3-D Fractal Brownian Motion (FBM) spectral density function to simulate non-uniform media and discuss the effects of Hurst exponent and scale coefficients on the modeling results. The Hurst exponent is an important indicator in FBM, which reflects the degree of disturbance in the medium. Soil water content (SWC) is a key factor affecting the heterogeneity of soil media, and the distribution of soil dielectric properties also depends on SWC. Therefore, we use SWC data to obtain the Hurst exponent through Rescaled-range (R/S) analysis. Finally, a 3-D random medium model is established based on actual SWC data, and GPR forward simulation is performed on the established model by the finite difference time domain (FDTD) method. The simulation results show that the random medium modeling method based on soil fractal characteristics can provide an effective modeling tool for GPR soil detection.
Ground penetrating radar urban road underground target classification algorithm using sequential spectral and time-domain features
Authors: Li F R , Shi W X , Yang F , Xu M X , Fang L , Fang Y J , Wen Y L 
Published: November 2024

Abstract
Ground-penetrating radar (GPR), a highly efficient non-destructive detection method, finds extensive use in urban road underground target detection. Existing GPR data recognition algorithms often rely on singular time-domain spectrogram features, leading to potential misjudgements. To address this, we propose a novel algorithm based on sequence spectra and time-domain features. Serialized radar data, transformed through wavelets, is combined with time-domain images for input, enabling classification through a multi-scale convolutional neural network. Experiments show improved accuracy in underground target classification, offering a fresh perspective on intelligent GPR data recognition.
A GPR layered media parameters inversion method under rough surface
Authors: Zhao S , Yang X , Liao L , Lan T 
Published: November 2024

Abstract
Ground Penetrating Radar (GPR) plays an important role in pavement detection and road exploration due to its efficiency. Traditional media parameter inversion algorithms only consider ideal smooth surfaces and neglect the roughness of interfaces, leading to significant errors in practical applications. Therefore, a GPR rough surface-layered medium parameter inversion method based on genetic algorithm is proposed in this paper. The method uses the generalized reflection coefficient and considers the influence of the surface roughness of the medium. Then, a radar echo signal time-domain model is established. Finally, the cost function is optimized by genetic algorithm. The effectiveness and accuracy are validated and analysed by utilizing simulated data through random rough surfaces models.
Modeling and calculation of ground penetrating radar detection on rough interface of soil media
Authors: Yi Zhu Jia , Liu Ying , Yao Liu Yao , Wang Chen , Jing Li Ya 
Published: November 2024

Abstract
Ground penetrating radar technology is an effective underground physical detection method. In order to better study the ground penetrating radar echo characteristics of complex soil media rough interfaces, a soil rough surface electromagnetic scattering model based on fractal rough surface geometry modeling and soil media dielectric properties modeling is established. The mixed soil characteristics including sand content, water content, clay content, and sand particle density are analyzed, and the radar echo image characteristic of soil rough media interfaces under different fractal dimensions is discussed.
Realistic method for numerical characterization of GPR responses of cracked drainage pipelines in loose-soil areas
Authors: Shi Weihuang , Luo Tess 
Published: November 2024

Abstract
A damaged drainage pipeline poses significant subsurface risks, including the potential for ground collapse. This study addresses the unreliability of previous Ground Penetrating Radar reflection patterns in characterizing drainage pipeline defects due to an incomplete understanding of the leakage mechanism and subsurface degradation. We propose an integrated approach using Computational Fluid Dynamics and Discrete Element Method to construct a realistic pipe-soil spatial distribution model. Converting this spatial model into an electromagnetic model enables numerical simulation of radar wave propagation with the Finite Difference Time Domain method. Our findings reveal that the position of pipeline defects influences soil degradation processes, resulting in distinct GPR reflection patterns. This research contributes to the accurate diagnosis of subsurface hazards related to cracked pipelines by offering a more reliable reference for GPR inspections.
Adaptive Variational Mode Decomposition Denoising Scheme for Ground Penetrating Radar Data
Authors: Yang Ding , Persico Raffaele , Guo Cheng , Liu Yajie , Yang Jiamin , Liu Handing , Bai Changjin , Zhao Qing 
Published: November 2024

Abstract
Addressing the impact of noise on target detection performance in Ground Penetrating Radar (GPR), an adaptive Variational Mode Decomposition (VMD) denoising scheme based on the Whale Optimization Algorithm (WOA) is proposed. An improved adaptive VMD method, based on WOA, is employed in this study for modal decomposition of GPR data, with target modes being retained and noise modes removed to obtain denoised GPR data. It is demonstrated by numerical simulation results that the SNR is increased by 20.846 dB through this scheme, surpassing common denoising methods and achieving the best results in terms of RMSE and MSE. Numerical simulations substantiate the scheme’s effectiveness in denoising GPR data.
Signal Fusion of Multi-Frequency Continuous Wave Ground Penetrating Radar
Authors: Xiao Jianping , Liu Zhijing , Qu Rui , Liu Lanbo 
Published: November 2024

Abstract
Carrier-less pulse radar, with its short pulse duration, low average power, and weak anti-interference capability, poses significant limitations to the application of ground penetrating radar (GPR). Fusing continuous wave signals from multiple frequency components forms the basis for the design and data interpretation of continuous wave radar. We firstly introduce two fusion methods: the application of the Inverse Fast Fourier Transform (IFFT) to multi-frequency signals and direct superposition. And we demonstrate these methods by synthesizing three basic sinusoidal signals and analyse their time-frequency relationship. Furthermore, we perform Fast Fourier Transform (FFT) on the transmit waveform of pulse radar to obtain a set of frequency components. By superimposing three sets of frequency components with different numbers of frequency points, we obtain the fused waveform and compare it with the original Ricker wavelet. The results show that while reducing the number of frequency points may increase the error in the synthesized signal slightly, it remains capable of accurately synthesizing the Ricker wavelet. However, this reduction also decreases the time window of the synthesized signal and may result in temporal overlapping. These findings offer insights into selecting parameters for frequency components in multi-frequency continuous wave radar and lay a foundation for signal fusion in such radar systems.
Detection of Termite Nests in Dams Using Ground Penetrating Radar Based on Deep Learning
Authors: Peng Jiao , Xue Wei , Wang Wenkai , Song Junlei , Zhou Feng , Huang Pengchun , Zhang Zhixuan , Zhang Feng , Zhang Xu , Chen Longjia , Sun Li 
Published: November 2024

Abstract
Termites have always been one of the major hidden dangers to the operational safety of dams. Traditional detection methods such as manual inspection and termite baiting have limitations in terms of efficiency and accuracy. To solve the problems, paper proposes a termite nest detection method based on ground penetrating radar (GPR) and deep learning. First, the simulated GPR data are generated using gprMax software, and the real data are collected using a GPR at several dams. Subsequently, the YOLOv8 deep neural network is employed for termite nest recognition in GPR images. The experimental results show that the proposed model can achieve the mean average precision (mAP) of 0.96 and a detection speed of 53.76 frames per second (FPS).
Design of A Linear Array Holographic Subsurface Radar System
Authors: Liu Wei , He Zhihua , Liu Tao , Song Xiaoji 
Published: November 2024

Abstract
This work introduces a new holographic subsurface radar (HSR) system designed with a cross-arranged linear array antenna. In non-destructive applications, holographic subsurface radar utilizes continuous wave, high-density spatial sampling, and a wide synthetic aperture to achieve high azimuth resolution. However, this results in a low data acquisition speed, which limits the detection efficiency. In this work, we designed a cross-arranged linear array antenna, where the transmitting- and receiving-antennas are layered on two parallel baselines. In this way, the space between the neighboring sampling points can still meet the spatial sampling criterion. The use of the electric-switching antenna array reduces 2-dimensional mechanical scanning to a 1-dimensional scan, significantly improving data acquisition speed while maintaining sufficient spatial sampling density. The prototype of the system was also detailed in the paper. The experiments show that the newly designed HSR system can improve detection efficiency by more than 10 times while maintaining imaging resolution.
An identification method of bonding state in ETIS based on GPR
Authors: Wang C , Wang J , Liu M , Li X , Gong J , Yang X , Lan T 
Published: November 2024

Abstract
The external thermal insulation system (ETIS) often has safety quality problems caused without debonding, and the classical debonding detection methods can not achieve high precision and high efficiency safety detection at the same time. Ground penetrating radar (GPR) can be one of the most efficient tools to identify the bonding state. In this paper, a methodology for bonding state detection is presented. Firstly, a modeling and simulation method for ETIS is designed, which is closer to the real form of the adhesive shape. Secondly, a 3D frequency wave-number domain filter is used to remove the low-rank direct-coupled waves and the low-frequency components, then a matched filter is used to estimate the time delay arrival and identify the adhesive shape in ETIS. Finally, the effectiveness of the algorithm for the recognition of bonding state is verified by numerical results and experiments.
Application of Ground Penetrating Radar in Rapid and Non destructive Detection of Urban Tunnels
Authors: Zhao Beiqiang , He Zhihua , Liu Tao , Song Xiaoji , Zheng Hongxing 
Published: November 2024

Abstract
Due to the rapid advancement of modern transportation, tunnels have emerged as an important component of transportation infrastructure. A large number of pipelines and tunnels makes the urban underground environment extremely complex. Detecting underground tunnels and pipelines can provide guidance for urban construction. Ground penetrating radar (GPR) possesses attributes such as swift detection speed, no loss, convenient and flexible operation, thereby playing a crucial role in numerous domains. The aim of this research is to verify the feasibility of detecting tunnels with depths on the order of 10 meters, by evaluating tunnel dimensions and depth parameters through a combined approach of simulation and experimental measurements. In this investigation, gprMax was utilized to model and simulate the underground tunnel to establish the correlation between echo data and target position. Additionally, this research employed an 80MHz GPR system to detecting a undergound tunnel and acquiring data which facilitated successful determination of the depth of the underground tunnel based on empirical evidence
Adding the outer pipe to a thin borehole radar probe in a thick water-filled borehole in rock to reduce direct coupling
Authors: Ebihara S , Masui R , Koyama K , Tsujikawa Y , Nishida Y 
Published: November 2024

Abstract
We show the experimental evidence that the additional outer pipe attached to a borehole radar probe reduces the direct wave’s power in single-hole measurement and emphasizes the reflected wave from subsurface objects in hard rock. This pipe replaces the water layer with the air one when the difference between the diameter of the radar probe and that of the borehole is large, for example, by 4 cm. In the field experiment, the radar probe is scanned in the water-filled borehole in the rock in the two cases, with and without the additional outer pipe. The comparison between these two cases confirmed that the direct wave’s power difference increases as the frequency gets higher and reaches 20 dB at 300 MHz. The radar probe with the additional outer pipe detected the reflected wave from the objects in the time domain. This contrasted with the fact that the radar probe without the additional outer pipe observed only the strong direct waves rather than the reflected waves.
The GPR and ERT detection for road hazard in loess region
Authors: Yan Jiahe , Zeng Zhaofa , An Baizhou , Sun Boyang , Li Zhilian , Wang Haoyu , Zhao Jianwei 
Published: November 2024

Abstract
The road hazards have frequently happened in the loess region in Ningxia autonomous region. The reason for this phenomenon is that the loess under the roads has always changed because of its properties, such as collapsibility, and leads to the instability of the geological structure. Detection of the loess under the roads can obtain the information about the geological structure and identify potential road hazard regions, which has great significance. Therefore, in this paper, we conducted a field experiment about road hazard in loess region using ground penetrating radar (GPR) and electrical resistivity tomography (ERT). The field experiment demonstrated that each method can be used in the detection and identification of potential road hazard but limited in their different detection capabilities, but better results can be obtained by using both methods comprehensively.
Polarimetric Attributes of Martian Regolith at the Tianwen-1 Landing Site
Authors: Yang Jiarun , Feng Xuan , Khuut Tseeulam , Lu Qi , Zhou Haoqiu , Dong Zejun , Zhang Minghe , An Yafei 
Published: November 2024

Abstract
We designed a data processing procedure for polarization analysis of the Martian regolith and applied it to the RoPeR CH2 channel data of the Zhurong rover, obtaining the structure distribution and weathering degree distribution of the Martian regolith of about 1900 m (325 sols). Three typical subsurface structures are studied in detail, and the results show that the structures in the Martian regolith all have relatively rough surfaces. This indicates that the structures within the regolith have suffered a high degree of weathering.
Estimation of soil hydraulic properties based on full waveform inversion of ground-penetrating radar data with Grey Wolf optimization algorithm
Authors: Zhang M. H. , Bano M. , Feng X. , Khuut T. , Lu Q. 
Published: November 2024

Abstract
In this paper, we propose an inversion scheme to directly and non-invasively determine soil hydraulic properties. The scheme is based on hydrogeophysical inversion full waveform ground penetrating radar (GPR) data with Grey Wolf Optimizer algorithm. This inversion scheme includes a soil hydraulic model, a petrophysical relationship, a ground penetrating radar forward modelling and a Grey Wolf Optimizer algorithm. In order to evaluate its performance, a numerical experiment was carried out and the proposed scheme was applied to the experimental data. The results indicate that the proposed inversion scheme is promising for determining soil hydraulic properties with GPR.
Reverse time migration of GPR data based on GPU parallel Symplectic Euler Algorithm and Total Variation Regularization
Authors: Gao Xin , Lei Jianwei , Chen Zhiqing , Fang Hongyuan 
Published: November 2024

Abstract
Reverse time migration is important in GPR image processing, but its efficiency and accuracy need to be improved. In terms of efficiency, reverse time migration uses the Finite Difference Time Domain (FDTD) algorithm for parallel CPU computation, which is inefficient. In terms of accuracy, it usually uses cross-correlation conditions for imaging, which reduces the imaging accuracy. To solve the above problems, this paper proposes the solution: using symplectic algorithm parallel GPU acceleration technology to improve the migration efficiency and using TV regularization technology to improve imaging accuracy. Finally, the proposed algorithm is verified by the road cavity model and the underground pipeline model.
Subsurface characterization of Removable Urban Pavements (RUP) using Ground Penetrating Radar (GPR)
Authors: Andreoli G , Schmidt F , Ihamouten A , Cothenet A , Dezert T , Nguyen M L , Sedran T 
Published: November 2024

Abstract
In the pursuit of improving transport infrastructure as part of the overall sustainable development policy, new coatings and emerging structures are coming to the forefront. This is the case with Removable Urban Pavements (RUP). This new type of structure, thanks to lightweight equipment, is removable in parts and allows quick access to underground networks. To assess the design and potential degradation of RUP, the use of multi-technique tools for Non-Destructive Techniques (NDT) is implemented as part of the European HERON project. These methods are increasingly sought after by transport infrastructure managers, both for corrective and preventive/predictive maintenance. Among them, Ground Penetrating Radar (GPR) is gaining popularity among pavement professionals for multi-object detection/characterization in the subsurface, as well as for assessing the geometry of structures. In this context, a RUP structure, installed on the fatigue carousel at Gustave Eiffel University (Nantes, France) and subjected to various mechanical and hydraulic solicitations, has been studied using GPR.
An adaptive event-based state estimation for coupled network with multiple fading measurement via partial nodes information
Authors: Lin Na , Chen Dongyan , Zhao Hairui 
Published: November 2024

Abstract
The state estimation (SE) problem is explored for coupled complex networks (CNs) subjected to general nonlinearity and coupled nonlinearity, as well as multiple fading measurement phenomena. The phenomenon of multiple fading measurement means that the measurement fading occurs in a random manner and each sensor has a separate fading probability in the information transmission. An adaptive event triggering mechanism (ETM) with a time-varying threshold is introduced for adjusting the frequency of information transmission. A two-stage partial-nodes-based (PNB) state estimator is designed using partial nodes’ information to estimate all true states. The PNB estimator gain matrix is parameterized by optimizing the trace of the upper bound matrix of the estimation error covariance (UBMEEC). The monotonicity of the trace of the UBMEEC with respect to the fading probability is clarified. Finally, a numerical example demonstrates the effectiveness of the proposed two-stage PNBSE method.
Optimal dispatching method for low-carbon park comprehensive energy system based on IFWA-SFLA algorithm
Authors: Ma Rui , Wan Peng , Wang Liang , Shi Yuanyuan , Liu Jia , Liu Chaochao , Xiao Zhenghai 
Published: November 2024

Abstract
Due to the instability of the output and load of the integrated energy system, it is difficult to effectively control the carbon emissions during the load phase. Therefore, this paper proposes a study on the optimal dispatching method of the low-carbon park integrated energy system based on the IFWA-SFLA algorithm. Combining load demand and renewable energy output fluctuations and analyzing the conversion and transfer relationships between different energy forms in the system, an objective function based on minimizing carbon emissions was constructed and will meet the actual load demand of the park. The energy conversion equipment capacity limit is used as a constraint; in the specific scheduling stage, the IFWA-SFLA algorithm is introduced, using carbon emissions as a fitness function to solve the optimal scheduling plan in the global scope. In the test results, the design method corresponds to the lowest carbon emissions. Compared with the control group, the total carbon emissions during the test period were reduced by 1002.88kg and 1058.80kg, respectively, and the average carbon emissions during the test period were reduced by 143.27kg and 151.26kg, respectively.
Enhanced chitosan fibres for skin regeneration: solution blow spinning and incorporation with platelet lysate and tannic acid
Authors: Haugen Håvard J , Coelho David , Tien Nguyen D , Geng Tianxiang , Blaker Jonny J 
Published: November 2024

Abstract
In this study, we developed and characterised enhanced chitosan/polyethylene oxide (PEO) nanofibre scaffolds using solution blow spinning (SBS) for potential application in skin tissue engineering. SBS enabled the efficient and scalable production of fibre matrices with precise morphology control, facilitating the integration of PEO to improve spinnability, 100X the speed of electron spinning. Following fabrication, fibres were subjected to potassium carbonate neutralisation to reduce PEO content, improving chitosan stability in aqueous environments. Characterisation by scanning electron microscopy (SEM) and attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR) confirmed structural integrity post-neutralisation and the successful incorporation with bioactive additives. Platelet lysate (PL) was incorporated to introduce growth factors, and tannic acid (TA) was added for antibacterial properties and enhanced mechanical stability through potential crosslinking. Mechanical testing showed that the optimised PL- and TA-enriched scaffolds exhibited the highest mechanical performance, with Young’s modulus of 7.0 ± 0.6 MPa, an ultimate tensile strength of 26.4 ± 2.3 MPa, elongation at break of 16.5 ± 1.7%, and toughness of 3.0 ± 0.3 MJ m−3 which is within the range of human skin. At the same time, SEM and ATR-FTIR analyses confirmed the stability and distribution of these functional agents within the fibre network. Biocompatibility tests with normal human dermal fibroblasts (NHDF) indicated low cytotoxicity, appropriate cell adhesion and proliferation over 14 days in culture, suggesting these scaffolds as promising candidates for wound healing and skin regeneration applications.