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GPR imaging results of the clay in the four model boxes. (a) water content = 10%; (b) water content = 12%; (c) water content = 14%; (d) water content = 16%; (e) water content = 18%; (f) water content = 20%; (g) water content = 22%; (h) water content = 24%.
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Soil water content is one of the most important factors affecting the safety and stability of buildings or structures, especially in roadbeds, slopes, earth dams and foundations. Accurate assessments of soil water content can ensure the quality of construction, reduce construction costs and prevent accidents, among other benefits. In this study, gr...
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Ground penetrating radar (GPR) technology is widely used in civil engineering projects such as inspection of concrete buildings, pavement road, bridge, tunnel, and underground utilities (water supply pipes, gas pipes, power cables, sewers, etc.). With the application of GPR becoming more and more extensive, in order to further improve its effective...
Citations
... Ali et al. [17] used discrete wavelet transform and principal component analysis to extract features from ground-penetrating radar signals and thus classify the geometry of buried objects. Zhang et al. [18] used wavelet packet energy analysis to analyse groundpenetrating radar signals from soils with different moisture contents. The results showed a highly correlated linear relationship between wavelet packet energy index and soil water content. ...
Deep learning is extensively utilised in transport geotechnical engineering. However, deep architectures have large computational costs and update times, while failing to understand decisions. To this regard, we propose an interpretable dynamic broad network combined with ground-penetrating radar for internal defect identification in roadbeds. The method is more suitable for feature characterisation of two-dimensional data and satisfies incre-mental updates. The test results indicated that the proposed method has an average recognition accuracy of 0.9124 for the four types of internal defects in roadbeds. Compared to the other four classical machine learning methods, it balances training efficiency and recognition accuracy. Robustness analysis results demonstrated that the method is noise-resistant. However, comprehending the recognition results of intelligent algorithms is a key topic. Local interpretation approach is introduced to quantify the feature importance that affects the decision of the model. Based on the feature importance calculation, it is possible to distinguish between positive and negative regions in one sample that influence the decision of the detection model. These interpretative analyses can assist us in better understanding the reasons for decisions generated by the detection model that provide technical support for subsequent enhancements.
... An additional alternative for GPR feature extraction is the use of wavelet methodologies. Wavelet analysis in GPR has been largely applied as a de-noising technique [24,25]; wavelets have also been applied in a diagnostic capacity in civil engineering [26] as well as in evaluation of soil moisture [27]. The fibrous and rhizomatous biomass assessed in this current study are characterized by a lack of a single large, discrete target object and are distributed among three depth layers. ...
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.
... But it also expands to include other techniques such as laser-based scanning and insight into new developments in the field of radar processing. Ten research papers were published in this Special Issue, comprising nine articles [1][2][3][4][5][6][7][8][9] and one technical note [10]. ...
... Two research papers from Zhang et al. [5] and Wang et al. [6] focus on the detection of water content and wet surfaces for use in agricultural settings. The authors of [5] use innovative data processing techniques to improve the assessment of water content in the soil. ...
... Two research papers from Zhang et al. [5] and Wang et al. [6] focus on the detection of water content and wet surfaces for use in agricultural settings. The authors of [5] use innovative data processing techniques to improve the assessment of water content in the soil. On the other hand, the authors of [6] show laboratory experiments and numerical simulations to show wet underground objects and surfaces produce a distinct signal within GPR data that allows for a better interpretation of the presence of water. ...
This Special Issue focuses on the potential of radar-based remote techniques for characterizing and monitoring natural and building structures [...]
... The application of GPR is viable for assessing soil water content with an accurate vertical resolution and an increased spatial resolution [94]. It could also be applied in the future to examine different soil types of different sizes and gradations [95]. Furthermore, radar data may be utilized to detect the presence of liquid organic pollutants in contaminant hydrology applications [96]. ...
Ground-penetrating radar (GPR) is an established technology with a wide range of applications for civil engineering, geological research, archaeological studies, and hydrological practices. In this regard, this study applies bibliometric and scientometric assessment to provide a systematic review of the literature on GPR-related research. This study reports the publication trends, sources of publications and subject categories, cooperation of countries, productivity of authors, citations of publications, and clusters of keywords in GPR-related research. The Science Citation Index Expanded (SCI-EXPANDED) and the Social Sciences Citation Index (SSCI), which can be accessed through the Web of Science Core Collection, are used as references. The findings report that the number of publications is 6880 between 2001 and 2021. The number of annual publications has increased significantly, from 139 in 2001 to 576 in 2021. The studies are published in 894 journals, and the annual number of active journals increased from 68 in 2001 to 215 in 2021. Throughout the study, the number of subject categories involved in GPR-related research fluctuated, ranging from 38 in 2001 to 68 in 2021. The research studies originated from 118 countries on 6 continents, where the United States and the People’s Republic of China led the research articles. The top five most common keywords are ground-penetrating radar, non-destructive testing, geophysics, electrical resistivity tomography, and radar. After investigating the clusters of keywords, it is determined that civil engineering, geological research, archaeological studies, and hydrological practices are the four main research fields incorporating GPR utilization. This study offers academics and practitioners an in-depth review of the latest research in GPR research as well as a multidisciplinary reference for future studies.
... In contrast to other signal processing methods, such as Fourier transform, fractal analysis, and Hilbert-Huang transform (Liao Ling et al., 2019), wavelet packet analysis enables the decomposition of the input signal into low-and high-frequency bands (FBs) with the required resolution , and the influence of the FBs of interest can be quantified using the energy ratio. Owing to these advantages, the wavelet packet-based energy analysis method (Zhang et al., 2021) was applied in subsequent data processing to identify the coupling mechanisms of the OWT model for the selected test cases. ...
Owing to the difficulties in the scaled rotor-nacelle assembly (RNA) and support structure design, and alleviation of small scaling effects, the limited dynamic model tests are conducted for the jacket offshore wind turbines (OWTs), which are extensively constructed in the offshore wind farms located in the depth of 40–50 m. To address this limitation, an integrated test method based on aero-hydro-structural elastic similarities is proposed in this study. It comprises a performance-scaled RNA model and a scaled support structure model. A redesigned blade model is adopted in the scaled RNA model to ensure the similarities of aerodynamic thrust loads without modifications of the scaled test winds. Moreover, auxiliary scaled drivetrain and blade pitch control are designed to simulate the operational states of a practical OWT. The scaled model of the OWT support structure is fabricated based on the joint hydro-structural elastic similarity, and the small scaling effects are mitigated by introducing sectional bending stiffness similarities. Subsequently, the dynamic model tests of an ultra-large jacket OWT under wind-only, wave-only, and combined wind and wave conditions are carried out. The accuracy of the fabricated OWT test model is validated based on the recorded responses, and the influence of the dominant frequencies on the dynamic responses of the OWT model is quantitatively evaluated using the wavelet packet-based energy analysis method. Further, the coupling mechanisms of the scaled OWT model under typical wind and wave loads are investigated, and the interactions between the environmental loads and OWT motions are proved.
... Assuming that an initial biorthogonal filter bank is h, g, h, g , the vanishing moments of the initial filter function at the decomposition and reconstruction ends are N and N, respectively. From the definition of vanishing moment, the following equations exist [57][58][59]. ...
Advanced geological prediction of tunnels has become an indispensable task to ensure the safety and effectiveness of tunnel construction before excavation in karst areas. Geological disasters caused by unfavorable geological conditions, such as karst caves, faults, and broken zones ahead of a tunnel face, are highly sudden and destructive. Determining how to predict the spatial location and geometric size of unfavorable geological bodies accurately is a challenging problem. In order to facilitate a three-dimensional quantitative analysis of the filler material ahead of the tunnel face, a biorthogonal wavelet with short support, linear phase, and highly matching waveform of ground penetrating radar (GPR) wavelet is constructed by lifting a simple and general initial filter on the basis of lifting wavelet theory. A method for a time–energy density analysis of wavelet transforms (TEDAWT) is proposed in accordance with the biorthogonal wavelet. Fifteen longitudinal and horizontal survey lines are used to detect void fillers of different heights. Then, static correction, DC bias, gain, band-pass filtering, and offset processing are performed in the original GPR profile to enhance reflected signals and converge diffraction signals. A slice map of GPR profile is generated in accordance with the relative position of longitudinal and horizontal survey lines in space. The wavelet transform analysis of a single-channel signal of each survey line is performed by adopting the TEDAWT method because of the similar rule of the single-channel signal of GPR on the waveform overlay and the ability of the constructed wavelet basis to highlight the time-frequency characteristics of GPR signals. The characteristic value points of the first and second interfaces of the void fillers can be clearly determined, and the three-dimensional spatial position and geometric sizes of different void fillers can be obtained. Therefore, the three-dimensional visualization of GPR data is realized. Results show that the TEDAWT method has a good practical application effect in the quantitative identification of void fillers, which provides a basis for the interpretation of advanced geological prediction data of tunnels and for the construction decision.
... Singular spectrum analysis, fuzzy threshold and correlation analysis are carried out on the selection of these three different types of coefficients to preserve the dynamic performance of chaotic signals to the greatest extent. An energy analysis method based on wavelet packet is proposed in [98]. This method is used to calculate the wavelet packet energy index of the ground-penetrating radar signal of clay samples with water content. ...
As a general and rigid mathematical tool, wavelet theory has found many applications and is constantly developing. This article reviews the development history of wavelet theory, from the construction method to the discussion of wavelet properties. Then it focuses on the design and expansion of wavelet transform. The main models and algorithms of wavelet transform are discussed. The construction of rational wavelet transform (RWT) is provided by examples emphasizing the advantages of RWT over traditional wavelet transform through a review of the literature. The combination of wavelet theory and neural networks is one of the key points of the review. The review covers the evolution of Wavelet Neural Network (WNN), the system architecture and algorithm implementation. The review of the literature indicates the advantages and a clear trend of fast development in WNN that can be combined with existing neural network algorithms. This article also introduces the categories of wavelet-based applications. The advantages of wavelet analysis are summarized in terms of application scenarios with a comparison of results. Through the review, new research challenges and gaps have been clarified, which will serve as a guide for potential wavelet-based applications and new system designs.
Collapses of urban roads caused by defects in shallow underground spaces are extremely dangerous, and this imposes high requirements for the detection of potential defect areas. Although the characteristics of defects, including voids, water-bearing voids, hyperbola defects and loose defects, can be effectively detected using ground penetrating radar (GPR), amounts of GPR data are currently limited. In this work, a deep convolutional generative adversarial network (DCGAN) with an improved loss function is applied to augment existing GPR data to give a total of 3,256 images. More importantly, a recognition model for underground defects is proposed based on GPR B-scans and strengthened with attention modules using YOLO v5. The mean average precision (mAP) of the model is 85.4 %, a value 3.26 % higher than that of YOLO v5. The values of the average precision (AP) for voids, water-bearing voids, hyperbolae, and loose defects are 87.5 %, 86.6 %, 96.2 %, and 71.1 %, respectively. Finally, the locations and span sizes of the defects are obtained by estimating the velocity of the electromagnetic wave, and this approach is verified through a field test. The absolute error in the burial depth is less than 0.16 m, and the average error ratio is less than 15 %. The absolute error in the horizontal span is found to be lower than 0.28 m, with an average error ratio of smaller than 22 %. Consequently, the proposed method provides reasonable support for more accurate and effective detection of underground defects.
The analysis of time-frequency variation and energy refinement characteristics of blasting vibration signals has contributed to understanding the propagation law of blasting vibration wave and reducing the possible losses. Combined with the measured data of tunnel blasting excavation and based on the newly constructed wavelet function, the spectrum distribution and energy refinement characteristics of tunnel blasting vibration signals are deeply explored and studied. The results demonstrated that compared to the Fourier spectrum, the innovative method of scale energy spectrum can not only acquire the dominant frequency of the blasting vibration signals, but also the obtained spectrum curve is smoother and can clearly reflect the change trend of the signal spectrum. The newly constructed biorthogonal wavelet has the characteristics of high vanishing moment, high regularity and matching with the waveform variation of the measured blasting vibration signals, and can describe the subtle variation characteristics of blasting vibration signal frequency. The continuous wavelet transform energy spectrum can reflect the three-dimensional energy distribution of blasting vibration signal in the time-scale domain, and the occurrence time of frequency, the frequency duration interval and time range of blasting vibration signal can also be acquired. Wavelet packet algorithm can precisely calculate the energy distribution of each frequency component in the signal, the tunnel blasting vibration signals (YBJ1, YBJ2) generated near the power tower presents low frequency, while the signals (YBF3,YBF4) far away from the power tower presents relatively high frequency. Measures should be taken to control the vibration and resonance of power tower caused by tunnel blasting. This research is of great significance for recognizing the propagation law of vibration waves, reducing the impact of blasting on surrounding buildings, and ensuring the safety of tunnel construction and surrounding buildings.
