1. Introduction
In the twenty-first century, mapping existing structures was a big issue for civil engineers. A crucial part of this procedure was extracting relevant information about the position, shape, and type of materials for embedded parts (e.g., sewers and reinforcing rebar). There are varieties of non-destructive testing (NDT) methods available, with ground-penetrating radar (GPR) being the most widely used in the civil engineering field [1,2]. GPR is a relatively new geophysical technology that has made significant progress in the recent decade. It can identify embedded things in structures without being destroyed since it uses electromagnetic waves and radargram processing technologies. Furthermore, it is portable equipment that can manage the full scanning procedure with just one operator [3]. The two key criteria of GPR are depth range and resolution. The signal may propagate farther with a lower center frequency of the GPR antenna, but the resolution in the shallower layers decreases. Higher frequencies do not penetrate deeply but provide higher resolution in shallower layers. Moreover, depth affects the size of the observable item. At shallow depths, little things can be observed, but as the depth range increases, an object’s physical size must be big in order to be detected.
The antenna, storage unit, display unit, control unit, and various auxiliary devices (e.g., battery, car, and global positioning system (GPS)) are all part of the GPR system [4,5]. Figure 1 depicts the structure of a typical GPR system. The antenna is made up of a transmitter that sends electromagnetic waves into structures and monitors for echoes caused by changes in the material characteristics of the structure. The GPR signal has a wide variety of frequency components and commonly operates in the 10–5000 MHz range. The GPR receiver detects these reflected signals, which serve as the foundation for imaging inside the invisible structure [6]. A control unit delivers commands for sample time, repetition time, frequency, and other parameters. A graphical user interface (GUI) is included in the display unit, allowing numerous parameters to be visualized and adjusted. There can be a storage unit that can deliver data onto a PC or other processing units for additional analysis. Finally, depending on the type and technical requirements of the system, accessories such as GPS and wheels may be provided.
One of the most important applications of GPR in civil engineering is scanning buildings and common structural components [7]. GPR is also used to assess roads and pavements since it is one of the most used non-destructive approaches to obtain subsurface information on the structure of roads and pavements [8,9]. It may also be used for underground utility (e.g., pipes, tunnels, and sewers) detection [10,11]. Mineral exploration has major difficulty with complex geological conditions that could be investigated using this emerging technology [12]. In geology, GPR is used to detect permafrost, locate fractures or water-bearing zones, map shallow formations, and so on [13,14]. In archaeology, GPR is commonly employed for mapping buildings in historical sites [15,16].
Notwithstanding the significance of this geophysical technology, there is a dearth of comprehensive literature reviews to analyze the global research topics and future trends of GPR from a statistical standpoint. Bibliometric studies rely on the analysis of journals, authors, publications, author keywords, and collaboration between countries or institutions. The method is gaining popularity as a research tool for examining the knowledge domain or visualizing networks to offer a more comprehensive view of the subject [17]. Furthermore, such investigations aim to evaluate how research has evolved and provide some insights into future research trends [18,19,20].
The primary goal of this study is to perform a comprehensive literature analysis to study current and future global research trends in GPR research. The following sub-objectives are carried out to attain the primary goal: (1) establishing a framework for the reviewed literature; (2) conducting a science mapping analysis to identify time and geographical distribution, authorship, sources, keywords, and citations of publications in the field of GPR; (3) summarizing emerging research themes and determining current research gaps; and (4) proposing future directions in GPR research. The novelty of this study is as follows: (1) conducting objective review-based research in the GPR domain by applying bibliometric and scientometric assessment; (2) providing a more comprehensive analysis of research papers published across 20 years, from 2001 to 2021; (3) performing qualitative analysis that identifies current research status and emerging research trends; and (4) expanding the previous research study related to GPR research [21].
2. Research Methodology
In this review-based study, a holistic analytical approach that integrates quantitative and qualitative evaluations is applied to gain a better understanding of the study area and remove biased findings [22]. The flowchart of this review-based research study is divided into three primary steps, as illustrated in Figure 2. In this study, bibliometric and scientometric assessment has been applied as quantitative tools for examining GPR research from a variety of perspectives, including publication time and citations, author collaboration and productivity, subject categories and journals, relevant countries and institutions, and author keywords [23]. The final element of the study framework is qualitative analysis, which offers a thorough knowledge of the major topics in the GPR research study.
Web of Science (WoS) is the most frequently used scientific literature database platform, with over 12,000 high-impact publications. Furthermore, this database is frequently used by scholars to gather accurate data for bibliometric studies [24,25]. As a result, the literature for this study is retrieved from the WoS database. Various phrases are examined to search for the targeted publications from the database. The utilized search phrases are GPR radar*, ground probing radar*, ground penetration radar*, ground penetrating radar*, GPR micro-wave*, GPR microwave*, geo-radar*, and georadar*. The asterisk indication guarantees that the search includes all relevant keywords. The targeted research and review publications are those published between 2001 and 2021. The conference papers are omitted because they lack the comprehensiveness of scientific content available in journal publications [26]. Science Citation Index Expanded (SCI-EXPANDED) and the Social Sciences Citation Index (SSCI) are chosen as citation indexes, and English is selected as the publishing language. Records related to the authors, article title, source title, author keywords, abstract, cited references, citations count, publisher, publication year, and WoS category of 6880 papers are downloaded as plain text (on 23 December 2021) from the database.
Bibliometric and scientometric assessment can be accomplished using many software applications such as Bibexcel, CiteSpace, VOSviewer, and VantagePoint. In this research, MS Excel is used to do the standard data analysis (such as publication trends, subject categories, journals, authors, nations, institutions, and keywords). The co-occurrence, bibliographic coupling, and co-authorship networks are developed using VOSviewer software (version 1.6.17) because of its aptitude for knowledge mining and visualization of vast networks (
VOSviewer software visualizes three different formats of maps: network, overlay, and density visualization. The elements in the network visualization are represented by nodes where the size of each node reflects its weight and degree of importance. In addition, a node’s color is determined by the cluster to which it belongs. The correlation between nodes is inversely proportional to their distances. The overlay visualization is similar to the network visualization, except that the elements are colored using a scale bar that displays the scores of clusters/items with respect to specific aspects (e.g., publication year). The density visualization shows how dense an object is at a specific point [31]. The density can be presented individually for each cluster to which the items belong (cluster density view) or without taking this distinction into account (item density view). However, network visualization is used in this study for brevity and visualization.
3. Results and Discussion
3.1. Publication Trends
The search yields 6880 publications during the twenty years from 2001 to 2021. The number of annual publications has increased significantly, from 139 in 2001 to 576 in 2021. Between 2001 and 2005, the annual number of articles was fewer than 200, with an average of around 160 each year. The annual number of publications climbed in 2006, reaching 219 compared to 173 in the previous year. There are around 258, 388, and 525 articles each year on average in the years 2007–2011, 2012–2016, and 2017–2021, respectively (Figure 3). In terms of the yearly total number of citations, Figure 3 shows an uneven trend: the three greatest values were 7659, 7548, and 7284 citations in 2007, 2013, and 2014, respectively. However, after 2014, the annual number of citations has steadily fallen, achieving 494 in 2021. This can be attributed to the fact that the more recent papers have had less time to be referenced.
3.2. Sources of Publications and Subject Categories
GPR papers were published in 894 journals between 2001 and 2021, illustrating the many disciplines and areas involved. Annually, the number of active journals increased from 68 in 2001 to 215 in 2021. As depicted in Table 1, the influence of these journals is quantified using four indicators; the number of publications, the average publication year, the average citations, and the average normal citations. The top five active journals in GPR-related research are the Journal of Applied Geophysics, IEEE Transactions on Geoscience and Remote Sensing, Near Surface Geophysics, Remote Sensing, and Geophysics, with 401, 316, 231, 199, and 198 total publications, respectively. These journals publish a total of 19.5% of the total articles, demonstrating the importance of these publications for GPR-related research. Figure 4 illustrates the annual number of publications for the five most productive journals. It demonstrates that each journal experiences yearly fluctuations in the number of published articles, with multiple peaks that tend to converge in the same periods, such as those in 2003, 2005, and 2010.
The top two most productive journals also have the highest number of total citations. For instance, the IEEE Transactions on Geoscience and Remote Sensing journal achieves the highest total citations of 9365, followed by the Journal of Applied Geophysics (8052 citations). However, journals with the highest average citations per article are not among the top three most productive or cited documents: Computer Physics Communication has the most (109.67), followed by Tree Physiology (94.75) and Earth-Science Reviews (80.67). On the other hand, articles published in IEEE Transactions on Geoscience and Remote Sensing have garnered an average of 29.64 citations per article, showing that GPR research papers published in these journals have had a significant impact on this subject.
Based on the average normal citations indicator, IEEE Transactions on Industrial Informatics (10.33) is shown to be the most significant journal in this study subject. This indicator is computed by dividing the total number of citations by the average number of citations published each year. It is used to deduce that older texts are not always cited more frequently than newer releases [32]. Furthermore, while having the largest research output, the Journal of Applied Geophysics is not regarded as the most fruitful journal since it does not have the highest average normal citations. The most recent studies are published in IEEE Access, Water, and Remote Sensing journals. The IEEE Aerospace and Electronic Systems Magazine and IEE Proceedings—Radar, Sonar and, Navigation journals, on the other hand, are no longer active in this study arena.
Figure 5 presents the prominent journals on the topic of GPR. The minimal threshold values are established at three articles and thirty citations. It was found that 251 out of 894 journals matched these criteria. Each journal is distinguished by a circle whose size is proportionate to the number of published articles. The distance between circles indicates the strength of the link with the other journal, such that the shorter distance indicates a stronger connection. Furthermore, the colors of clusters refer to the research topics such that journals belonging to the same cluster cover similar themes. For instance, 77 journals that primarily publish articles on earth sciences, geology, landslide, and sedimentology make up the largest red cluster. A total of 67 Journals that publish articles on applied geophysics, structure, and infrastructure are represented by the green cluster. The blue cluster, comprising 51 journals, includes research on glaciology, atmosphere, hydrology, and planetary and space science in the GPR-related domain.
Throughout the study, the number of WoS categories involved in GPR-related research fluctuated, ranging from 38 in 2001 to 68 in 2021. A large number of involved categories is dependent on the fact that publishing journals might cover numerous WoS categories. As shown in Figure 6, Geosciences, Multidisciplinary is the most important (2491 articles, or 17.51% of the total), followed by Geochemistry and Geophysics (1393 articles, or 9.79%), Engineering, Electrical, and Electronic (1152 articles, or 8.10%), Remote Sensing (857 articles, or 6.02%), and Imaging Science and Photographic Technology (803 articles, or 5.64%). These five categories account for 45.66% of all GPR-related research articles.
After examining the growth trends of the five most important categories, it is clear that, while the trends show a general increase in the number of articles published, the scientific interest in each category has shifted over time (Figure 7). The Geosciences, Multidisciplinary and Geochemistry, and Geophysics categories have switched places multiple times over the years. The same applies to Geochemistry and Geophysics and Engineering, Electrical, and Electronic categories. In addition, there have been some significant changes in the number of articles published, such as the peaks in the Geochemistry and Geophysics category in 2007 and the sudden increase in the number of articles published in the Geosciences, Multidisciplinary category from 2011 onwards.
3.3. Country and Institution of Publications
Due to the lack of author address information in 24 (0.34%) of the 6880 papers, these data have been removed from the analysis of the publishing country and institution of research outputs. In the period 2001–2021, the GPR research encompasses 118 countries on six continents from all over the world. The six continents are arranged in the following order: Europe (43 nations), Asia (34 nations), Africa (18 nations), South America (10 nations), North America (9 nations), and Oceania (4 nations). It is determined that 59 countries (50.0%), 14 countries (11.86%), and 45 countries (38.14%) produced fewer than 10 articles, between 10 and 20 articles, and more than 20 articles, respectively. With 2002 research outputs (20.16%), the United States is the top productive country (see Table 2). With 959 articles (9.66%), the People’s Republic of China comes in second but is still a long way behind the first. Italy (732, 7.37%), Germany (583, 5.87%), England (540, 5.44%), France (463, 4.66%), and Canada (408, 4.11%) are among the nations with at least 400 publications. Because both developing and developed countries are rated among the top ten countries, academic contributions are not solely dependent on economic progress. Furthermore, the articles have a global reach because they are scattered across three continents: North America, Asia, and Europe.
Regarding the average publication year, the most recent studies are published in Qatar (2018.75), Cyprus (2018.71), and Vietnam (2018.64). The United States (41744), Germany (12825), Italy (12193), England (12054), and the People’s Republic of China (9372) attain the highest total citations among other countries. Despite that, Syria, the country with the highest average citations (42.00), is not among the top-five most productive or cited countries. Concerning the average normal citations indicator, Kyrgyzstan (2.06) is the most significant country in this study subject.
The bibliographic coupling analysis for the active countries involved in GPR research is illustrated in Figure 8. Countries with at least three articles and thirty citations are included in the study. The network comprises 81 countries from a total of 118. It is worth noting that the countries are represented by nodes, where the size of each node indicates the total number of articles produced by each country. Meanwhile, the line thickness represents the strength of the cooperation link between the two countries. Malaysia (1.88), Oman (1.80), and Scotland (1.70) have the greatest influence on the average normal citation indicator, followed by Portugal (1.50), England (1.50), and Northern Ireland (1.45). The United States partners with the majority of nations, including the People’s Republic of China, France, and Switzerland. Italy, which co-authored papers with the top productive countries, worked more closely with the People’s Republic of China, Greece, and Algeria. Similarly, Switzerland, which coordinated with most of the other nations in the network, has Norway, Sweden, and Austria as its primary collaborators. All of these findings suggest that GPR research greatly encourages cross-national collaboration.
The contribution of the institution has been assessed based on the affiliation of the article’s authors. The institutions are determined by counting the total articles ascribed to a certain institution. GPR-related research is supported by 4714 institutions in total. The analysis includes organizations with at least three papers and thirty citations. Out of the total institutions, 889 organizations satisfy these criteria and are incorporated into the network (see Figure 9). It should be noted that the organizations are represented by nodes, with the size of each node indicating the total number of articles published by each organization. Meanwhile, the thickness of the line symbolizes the strength of the two organizations’ cooperation relationship. The Chinese Academy of Sciences works with the vast majority of organizations, including Penn State University, the University of Waterloo, and Ohio State University. Meanwhile, the University of Wisconsin collaborates more closely with Boston University and the University of Copenhagen. All of these data indicate that GPR research promotes cross-national collaboration.
Table 3 depicts the top-five institutions that have been active in the last 20 years. The Chinese Academy of Sciences is the most productive among the main organizations, with 169 papers. The other four organizations came from four different countries (i.e., Italy, Holland, United States, and Germany); National Research Council is the most productive, with 116 papers, followed by Delft University of Technology (91), University of Illinois (88), and Forschungszentrum Jülich (85). With regard to the average publication year, Sun Yat-sen University (2019.92), Chongqing Jiaotong University (2019.80), and Central South University (2019.70) published the most recent studies. Delft University of Technology (2784), University of Leeds (2583), Forschungszentrum Jülich (2427), Chinese Academy of Sciences (2357), and Université Catholique de Louvain (2343) obtain the highest total citations among other organizations. However, organizations with the highest average citations are not among the top five most productive institutions: the University of North Dakota (50.50), the University of California, Berkeley (47.69), and the National Center for Atmospheric Research (46.29). Based on the average normal citations indicator, the Nanjing University of Aeronautics and Astronautics (25.72) is the most significant organization in this study subject. Furthermore, while having the largest research output, the Chinese Academy of Sciences (1.26) is not regarded as the most fruitful organization since it does not have the highest average normal citations.
3.4. Productivity of Authors
Nineteen thousand thirty-seven individual authors are participating in GPR-related research, and the average cooperation index (i.e., number of authors per article) is 4.4. Table 4 depicts the quantitative measures of the most significant scholars. S. Lambot from the Université Catholique de Louvain and F. Soldovieri from the Italian National Research Council are the most productive authors, with 63 articles, followed by H. Vereecken from the Forschungszentrum Jülich, with 47 articles. According to the average publication year, X. Liu and D. Kumlu are among the most recently active researchers. Based on the total citations, S. Lambot, F. Soldovieri, H. Vereecken, J. Van Der Kruk, and Y. Rubin receive a total of 1547, 1271, 1118, 1027, and 1022 citations, respectively. Several scholars in the same cluster receive the same average normal citations. For example, C. Le Bastard and Y. Wang have an average normal citation of 0.74. This shows that these scholars made equal contributions to the scientific field. Furthermore, different groups of researchers differ in terms of the average normal citations. This suggests that these researchers collaborated with other teams or worked alone to develop new collisions.
The bibliographic coupling for the most productive authors is illustrated in Figure 10. 1477 out of 19,037 authors meet the minimal criterion of three publications and thirty citations. The node size and color represent an author’s number of articles and the membership cluster, respectively. Meanwhile, the distance between circles indicates the strength of the relationship between authors. In general, the shorter the distance, the stronger the connection based on bibliographic coupling. In other words, authors who are close to each other tend to cite the same publications and vice versa. The lines connecting between nodes are represented such that the thicker line indicates a greater bibliographic coupling between the two authors. The researchers are classified into seven clusters, and the size of each cluster ranges from 15 to 324. These clusters reflect the research network of academics in GPR research, such as the research group of G. Leucci, X. Comas, and E. Forte.
The co-authorship map for the influential researchers is illustrated in Figure 11. Because not all of these researchers collaborated, the cooperation network has only 721 authors. There exist 38 clusters that range in size from 3 to 77 scholars. There is a clear distinction between co-authorship and bibliographic coupling networks. The researchers in a particular co-authorship cluster may belong to a larger cluster of bibliographic coupling that comprises authors from other co-authorship clusters. For instance, F. Soldovieri is a member of the green cluster in the co-authorship map, with 41 authors. The same author, on the other hand, is a member of a larger green cluster in the author bibliographic coupling, with 266 researchers.
3.5. Citations of Publications
The total citation count is gathered from the Web of Sciences Core Collection (23 December 2021). A total of 470 out of 6880 papers receive at least 50 citations. The top five most-cited publications are listed in Table 5. Giannopoulos [33] is the first most referenced article (409 citations). The paper discussed the foundations of GPR operation, in addition to presenting a software tool for modeling GPR responses from complex targets (GprMax). Yoshikawa and Hinzman [34] is the second most referenced article (334 citations). Lambot et al. [35], which was referenced 299 times, is the third most cited article. Warren et al.’s [36] publication entitled “gprMax: Open source software to simulate electromagnetic wave propagation for ground penetrating radar” came in fourth place with 293 citations. Finally, Gurbuz et al.’s [37] article, which was referenced 234 times, is the fifth most cited article.
3.6. Cited References
When two studies cite one or more documents in common, this is known as bibliographic coupling. The bibliographic coupling strength is higher when there are more common citations in the referring works, detecting the subject similarity between the two studies. On the other side, co-citation analysis overcomes the shortcomings of bibliographic coupling by considering document citations that change over time to assess the similarity between articles. Two documents are said to be co-cited when they acquire a citation from the same third document [38].
Out of 149,276, 1004 cited references are extracted and grouped into seven clusters based on a criterion of at least 20 co-citations. Table 6 presents the top five most co-cited references. Davis and Annan [39] is the most cited reference (741 citations). The article demonstrated the ability to apply radar to map the stratigraphy of soil and rock. Neal [40] is the second most cited reference (491 citations). This research was concerned with studying the principles and problems of applying GPR in sedimentology. Daniels [41], which was referenced 453 times, is the third most cited reference. This article discussed the general system considerations, modeling aspects, applicability in different soil types, modulation techniques, and various technology applications. Jol’s [42] article, which was referenced 423 times, is the fourth most cited reference. This research discussed the fundamental theory and current developments of GPR for different applications. Finally, Huisman et al.’s [43] publication came in fifth place with 334 citations. This article provided a detailed review of GPR technologies for measuring soil water content.
3.7. Author Keywords in Publications
Author keywords can aid in grasping the patterns in a certain subject [44]. As a result, the author’s keywords are investigated to examine the most important themes in the articles. A portion as large as 5117 (74.4%) of the 6880 total articles features one or more keywords, whereas the remaining 1763 (25.6%) articles do not incorporate any keywords. The majority of articles (1574; 22.9%) have five keywords (Figure 12).
There are a total of 13,626 occurrences of 27,499 unique author keywords. The 50 most common terms (0.37%) are thoroughly examined to obtain a more detailed look. These terms refer to some major “hot” issues, such as geophysics (e.g., electrical resistivity tomography), archaeological investigations (e.g., archaeology and cultural heritage), civil engineering (e.g., landmine detection and concrete), geology (e.g., stratigraphy and geomorphology), and hydrological research (e.g., hydrogeophysics, soil water content, soil, water content, and permafrost).
Keyword co-occurrence networks are one of the most prominent linguistic networks examined in the past. These networks are used to discover the semantic similarity between phrases [45]. In addition, they are used to reveal information about knowledge structures and their temporal evolution in a changing research topic [46]. The author keywords’ and ‘full counting’ approaches are used to display the co-occurrence network. Some parameters in the VOSviewer, such as the minimum number of term occurrences, are adjusted to ensure building the map with several terms that represent the article content. When the frequency of keywords in the network map is adjusted to 10,230, 13,626 of the keywords are chosen. In addition, a thesaurus text file is inserted for combining multiple spellings of the same phrase (e.g., antennas and antenna, electrical resistivity tomography (ERT), and (FDTD) finite-difference time-domain). Furthermore, it could be useful for deleting irrelevant words (e.g., Egypt and Italy) or merging different terms referring to the same concept (e.g., numerical models and numerical modeling).
A co-occurrence map that includes 230 keywords for the whole period (2001–2021) is developed in Figure 13. This map provides an overview of GPR research and subfields with their interdependencies. The diameter of a circle and the size of labels show the occurrences of phrases. Meanwhile, the colors of the nodes reflect the clusters such that the phrases that often co-occur are clustered together on the map. Finally, the distances between nodes show the connections between the keywords. Furthermore, the keywords are organized into twelve clusters. Leading words identify each of these clusters. The keywords in the largest five clusters are described in this sub-section. The largest red category (36 items) includes keywords such as landmine detection, buried object detection, and permittivity. The second green cluster (31 items) includes phrases such as archeological prospection, geoarchaeology, monitoring, and geographic information system (GIS). The third blue cluster (27 items) comprises many keywords such as finite difference time domain, image processing, and tomography. The fourth yellow cluster (24 items) is responsible for topics related to mountain permafrost, rock glacier, soil, and seismic reflection. The fifth purple cluster (24 items) includes terms such as glacier, hydrology, ice, and snow. Within the same cluster, geophysics and electrical resistivity tomography are all significantly connected. On the contrary, there is no relationship between other terms in the same cluster, such as ice and radar signal processing. Furthermore, significant relationships between terms from different clusters, such as non-destructive testing, concrete, and condition assessment, may exist. This demonstrates the capacity of co-occurrence networks to determine the extent of a given area.
With 2960 occurrences, ground-penetrating radar is the most frequent keyword (see Table 7). With 160 occurrences, the non-destructive testing keyword comes in second but is still a long way behind the first. Geophysics, electrical resistivity tomography, and radar are among the top five most frequent keywords. Regarding the average publication year, the most recent keywords include machine learning (2019.59), predictive models (2020.10), data models (2020.25), and deep learning (2020.36). On the other hand, traditional keywords include buried objects (2007.56), borehole radar (2008.80), rough surfaces (2009.25), and subsurface (2009.80). Inverse modeling (52.83), frequency domain (50.60), full-waveform inversion (43.08), seismic refraction (40.88), hydrology and buried objects (40.31) attain the highest average citations, among other keywords. Concerning the average normal citations indicator, the predictive model’s keyword (13.69) is the most significant keyword in this study subject. This indicates the different applications of prediction modeling from GPR in various fields.
4. Qualitative Analysis
The scientometric analysis provides the readers with keyword clusters without identifying the current gaps and future directions in the studied research area [47]. As a result, a qualitative discussion of the papers used in the scientometric review is conducted to provide a comprehensive classification and summarization of GPR research. It also aims at presenting the current gaps and future trends in this research field. In this research, GPR-related studies are classified based on the dedicated application. It is determined that GPR systems could be utilized in many diverse applications, including civil engineering, geological research, archaeological studies, and hydrological applications, as follows:
4.1. Civil Engineering
GPR could be widely utilized in civil engineering applications [48], including buildings [49], foundations [2], roads [50], bridges [51], railways [52], tunnels [53], landmine detection [54], pavements [8,9,55,56,57,58,59,60,61], and underground utilities [62,63,64], as follows:
4.1.1. Buildings
Buildings can be classified into different types: cultural heritage structures and modern structures. GPR is used to assess buildings and common structural components for (1) heritage preservation and building code compliance, (2) deterioration mapping, which can be used as a decision-making tool for preventive maintenance, and (3) determining the extent of structural damage (i.e., detect fractures, voids, moisture, and rebar) caused by natural disasters such as earthquakes, floods, and landslides [6]. The GPR application in building deterioration is extremely useful, especially for occupied structures. This can be attributed to the fact that GPR does not disrupt residents’ and tenants’ everyday activities (i.e., less intrusive), unlike other assessment methods. In addition, because building maintenance and repair are likewise costly, GPR is regarded as a useful approach to identify early problems before damage or failure is visible [65]. Despite that, the GPR application to detect the probable damage causes and support the rehabilitation of buildings after natural disasters is still very limited. Instead, the buildings are demolished or rehabilitated without using non-destructive techniques [6].
4.1.2. Foundations
There are a few studies of GPR application in substructures that examine the interaction between building foundations and the ground. Examples of these applications include: (1) detecting foundations and assessing their structural safety and integrity [66] and (2) identifying water tables and wet ground that may induce settlement [67]. However, because of the difficulties of accessing such structures through an antenna, the number of these applications is currently limited [68].
4.1.3. Road Pavements and Bridges
Another unique application of GPR is providing subsurface information for the transport infrastructure, including roads, pavements, and bridges [69]. For road pavements, GPR surveys are conducted on flexible, rigid, semi-rigid, and composite pavements. Nowadays, the scope of these surveys is not only focused on assessing steel bars or the thickness of layers, but it is extended to conducting a structural assessment, detecting water infiltration, subsidence, voids, cracks, and anomalies [70,71]. On the other side, GPR bridge surveys are conducted either from the bridge deck or from specific bridge parts such as girders and columns [72,73,74,75]. The surveys are mostly used to diagnose bridges for detecting problems related to embedded reinforcement (e.g., bars and post-tensioned or pre-stressed tendons), corrosion and cracks [76,77], and poor compaction [78]. For the shortcomings, it is worth mentioning that GPR is still being used on an ad-hoc basis rather than regularly. Furthermore, the integration between building information modeling (BIM) and pavement management systems (PMS) is still being researched for developing integrated management and decision-making system [79,80].
4.1.4. Underground Utilities
GPR may also be used to detect the invisible and sophisticated network of underground utilities such as water supply pipes, stormwater drainage, sewers, gas pipes, power cables, communications cables, and traffic lights cables [81,82]. The mapping and scanning of underground utilities are one of the most difficult GPR activities of all civil engineering applications. This is due to the following facts: (1) radargram patterns of utility depths, orientations, and material types are frequently non-typical when compared to other infrastructures [6], and (2) the location and status of these underground utilities remain mysterious in most cities in contrast to the evident and visible damage to above-ground assets (e.g., roads and bridges). The condition deterioration of these utilities will be apparent by the occurrence of road collapse or traffic safety hazards from water leaks and seepage from water utilities, soil wash-out, and gas explosions [83,84]. Future research shall focus on GPR interpretation, particularly in extracting the hyperbolas pattern, to forecast unanticipated disturbances [82]. Another research direction incorporates examining combinations of different underground hazards under the utility networks.
4.2. Geological Studies
Identifying geological layers is necessary for locating drinking water supplies and other natural resources, as well as identifying risk zones. Furthermore, various stratigraphic and geological studies have reported significant findings concerning the Earth’s surface [40,85]. Excavations are time-consuming, expensive, and frequently impossible to be conducted because some study sites are protected. As an alternative, GPR, a non-invasive geophysical technology, could be utilized to provide high-resolution subsurface imaging and map shallow formations, detect permafrost, and locate fracture or water-bearing zones [13,14,86]. It is typically used in conjunction with electrical resistivity tomography to offer comprehensive geological information. However, these geophysical technologies have not been generally evaluated and deployed because of the following reasons [6]: (1) the lack of knowledge and understanding of the capabilities of GPR in geophysics by the geotechnical engineers, and (2) the preference of the geological community to believe in the soil and rock that they can see (borehole log) over what they cannot see (radar signal). Therefore, the potential of applying GPR in geotechnical studies needs to be further examined in the future.
4.3. Archeological Applications
In archeology, GPR is capable of mapping historical buildings as well as detecting cracks, fractures, and cavities in historical sites [15,16,87]. Unlike conventional excavation methods that might destroy important archaeological structures, this subsurface imaging approach is very effective [88]. The capacity of GPR to detect the targeted buried objects is influenced by the geometry of the object of interest, subsurface geometric features, and the existence of complicated stratigraphy [89]. It has been demonstrated that soil type and density, sediment mineralogy, and moisture and clay content have a significant impact on data processing parameters and detection accuracy [90]. Other variables include topography, burial depth, and vegetation cover [89]. Therefore, it is critical to investigate and examine the burial conditions when detecting buried archaeological remains.
4.4. Hydrological Research
GPR is a potential technology for the characterization and monitoring of hydrological systems at high resolution and on a broad scale [91,92]. Object detection in the ground is affected by geological elements such as mineralogical clay, saline water, hot water, and soils [93]. 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].
5. New Avenues of GPR Applications
GPR, ERT, shallow seismic refraction (SSR), and very low frequency electromagnetic (VLFEM) are examples of the several available geophysical methods that could be utilized in the fields of civil engineering, geological research, archaeological studies, and hydrological applications. Geotechnical risks are the primary determinants of building decisions in structurally challenging zones. Different techniques, such as geotechnical techniques, geophysical tools, and remote sensing, are used and integrated to study the subsurface structures and locate any geological formations that might obstruct the development of new communities [97,98,99]. In the archaeological field, geophysical methods are commonly used to highlight differences in the physical behavior of the subsurface caused by the presence of buried remains [16,100,101]. Understanding the complexities of the interaction between archaeological features and their geophysical reaction will consume considerable effort. The studies revealed that integrating geophysical approaches can offset these limitations and improve the reported findings. For hydrological applications, geophysical investigations are carried out to demonstrate the capacity of technologies for detecting cavities, sinkholes, and water infiltration pathways [102].
It is demonstrated that satellite radar remote sensing systems can be applied to complement non-destructive ground-based techniques (e.g., GPR), paving the way for the smart monitoring of infrastructure assets. The combination of these approaches enables the high resolution, flexibility, and capacity of GPR to detect the sources of shallow defects to be paired with the ability of satellite remote sensing to simulate the evolution trend of distresses on a broader scale. Indeed, the increased precision can help to increase a facility’s resistance to both external catastrophes and internal degradation, thus leading to infrastructure resilience [103,104,105]. The groundwater potential of complex areas characterized by moderate to steep slopes of topography, strong heterogeneity, multiple intrusions, and repetitive deformations could be assessed using a joint venture of satellite remote sensing, geoelectrical resistivity, and GPR techniques [106]. The integrated approach of remote sensing, sedimentological, and geophysical approaches has been proven to be accurate and successful in the mapping of paleochannels and accomplishing sustainable groundwater development goals [107]. Another important application has been reported by the combination of field observations, geophysical tools, and satellite remote sensing for landslide characterization [108]. The integration of various remote sensing techniques could be utilized for detecting and inspecting buried archaeological remains as well as assessing their preservation degrees [109,110].
The classic GPR system suffers from low detection efficiency and high labor costs when the detection field is vast. Furthermore, the GPR application could be dangerous for field investigations in harsh weather and terrain conditions. In an attempt to overcome these shortcomings, an integrated system of the unmanned aerial vehicle (UAV)-mounted GPR is developed to examine regions without being in direct contact with the Earth. This system is beneficial for spotting and detecting destructive objects such as landmines or archaeological surveys [111,112,113,114].
Future studies shall focus on automating GPR scanning operations. At the current time, several parts of the scanning process are performed manually, being labor-intensive and consuming considerable time. Furthermore, the output formats of different scanners are not uniform, and the data cannot be simply imported into a general-purpose 3D modeling software such as Autodesk Revit. This calls for the necessity of exploring other options for solving this problem [3]. In civil applications, future studies might explore using GPR in pavement design and maintenance procedures. The focus shall be given to examining the drawbacks, including the method’s accuracy and the difficulty in interpreting recorded signals [115]. In the archaeological field, it is suggested to integrate geophysical data to acquire volumetric and planimetric structures in the subsurface, necessitating the use of advanced algorithms (i.e., machine learning algorithms and image data fusion) [100].
In the recent few years, satellite-based interferometry has been leveraged for analyzing and monitoring structural deformation in bridges. Wang et al. [116] used persistent scatters interferometric synthetic aperture radar (PS-InSAR) technology to scrutinize the collected time series data and detect differential deformation between piers. In this regard, they built a three-dimensional deformation model using green’s function-based interpolation method. In another study, Schlögl et al. [117] experimented with the use of airborne laser scanning (ALS), vehicle-mounted mobile laser scanning (MLS), and satellite radar interferometry (InSAR) for identifying structural deformation trends. They elucidated that three non-invasive technologies were able to monitor deformation, with ALS offering a more flexible and cost-effective approach than MLS. In addition, InSAR was found to stand out as a more efficacious technology for long-term deformation assessment of bridge structures. Impact echo was lately exploited by Hu et al. [118] for defect detection in ballastless tracks. They utilized the finite-difference time-domain (FDTD) technique to emulate the propagation of elastic waves in ballastless tracks, and an improved synthetic aperture focusing technique (SAFT) was presented for the visualization of defects. In a study by Stüwe et al. [119], impact echo and ultrasonic contact testing were implemented to investigate scaling growth in geothermal pipelines. They evinced that both tests are applicable, while impact echo offered a rapid and more cost-efficient scaling monitoring alternative.
Electrical resistivity tomography (ERT) is another non-destructive technique that was newly deployed by Abudeif et al. [120] to find groundwater pathways and observe their level rise. In this context, ERT was able to create 3D profiles and 3D voxel interpretations for subsurface geoelectrical zones. As well, Guo et al. [121] jointly employed self-potential and electrical resistivity tomography for the sake of seepage detection in earth-filled dams. They managed to reestablish pseudo-3D seepage pathways by combining the measurements of electrical resistivity alongside inversion outcomes of self-potential data. An infrared thermography camera is a contactless non-destructive technique that was mounted by unmanned aerial vehicles (UAV) and used by Zhou et al. [122] for the sake of automated detection of earth embankment leakage. In their study, an AlexNet-based transfer learning framework was created for the classification of infrared images into either cold slope leakage, warm slope leakage, normal slope, normal ponding, cold piping, and warm piping. Moreover, Loiotine et al. [123] utilized airborne infrared thermography for characterization of rock mass in complex conditions. By analyzing thermograms, they succeeded in mapping the correlation exhibited between rock mass properties and temperature profiles.
6. Conclusions
The research study applied a holistic approach of bibliometric and scientometric assessment to present a global overview of ground penetrating radar (GPR) research from 2001 to 2021. The Web of Science database produced 6880 publications that were examined with respect to the publication trends, sources of publications and subject categories, cooperation of countries, the productivity of authors, citations of publications, and clusters of keywords. According to the findings, there has been a shift in the development and promotion of the field of GPR. The number of annual publications had climbed from 139 in 2001 to 576 in 2021. Specifically, the publishing output has risen rapidly since 2006, with a multidisciplinary and multi-regional approach distinguishing it. The research studies were published in 894 journals, with the number of active journals rising from 68 in 2001 to 215 in 2021. The number of subject categories included in GPR-related research fluctuated during the study, ranging from 38 in 2001 to 68 in 2021. The GPR research studies involved 118 countries from all around the world. The United States and the People’s Republic of China made the most significant contributions to the research community. The Chinese Academy of Sciences, China, was the most prolific institution, followed by the National Research Council, Italy. “Modelling ground penetrating radar by GprMax” article that was authored by Giannopoulos [33] in the Construction and Building Materials journal and was the top most cited article (409 citations). Ground-penetrating radar, non-destructive testing, geophysics, electrical resistivity tomography, and radar ranked first through fifth in terms of emerging keywords. GPR was widely applied in four different fields; civil engineering (landmine detection, bridge deck, and asphalt pavement), geological research (sedimentology, stratigraphy, and Holocene), archaeological studies (archeology, cultural heritage, and geoarchaeology), and hydrological practices (soil moisture, soil, and moisture content). All of these findings and conclusions have been interpreted in light of the Web of Science database. This review article could assist academics in identifying the most prestigious journals and researchers with whom to collaborate or publish in the future. It also aided in recognizing current hotspots in order to gain a comprehensive understanding of the subject at hand.
Conceptualization, N.E., A.A.-S. and E.M.A.; methodology, N.E. and E.M.A.; formal analysis, N.E., A.A.-S. and E.M.A.; data curation, N.E., A.A.-S. and E.M.A.; investigation, N.E., A.A.-S., E.M.A. and T.Z.; resources, N.E., A.A.-S., E.M.A. and T.Z.; writing—original draft preparation, N.E., A.A.-S. and E.M.A.; writing—review and editing, N.E., A.A.-S., E.M.A. and T.Z. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Informed consent was obtained from all subjects involved in the study.
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
The authors declare no conflict of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 6. Distribution of top thirty WoS categories involved in GPR-related research.
Enlarge this image.Figure 12. Statistical distribution of the keywords in GPR research publications.
Top 5 most productive journals in GPR research between 2001 and 2021.
| Journal | Publisher | Impact Factor (2021) | Number of Publications | Average Publication Year | Total Citations | Average Citations | Average Normal Citations |
|---|---|---|---|---|---|---|---|
| Journal of Applied Geophysics | Elsevier | 2.121 | 401 | 2012.66 | 8052 | 20.08 | 1.04 |
| IEEE Transactions on Geoscience and Remote Sensing | IEEE | 5.600 | 316 | 2011.79 | 9365 | 29.64 | 1.42 |
| Near Surface Geophysics | Wiley | 2.033 | 231 | 2011.80 | 2525 | 10.93 | 0.54 |
| Remote Sensing | MDPI | 4.848 | 199 | 2019.11 | 1276 | 6.41 | 0.98 |
| Geophysics | SEG Library | 2.928 | 198 | 2011.19 | 4421 | 22.33 | 0.97 |
Top 5 most active countries in GPR research between 2001 and 2021.
| Country | Number of Publications | Average Publication Year | Total Citations | Average Citations | Average Normal Citations |
|---|---|---|---|---|---|
| United States | 2002 | 2012.58 | 41,744 | 20.85 | 1.11 |
| People’s Republic of China | 959 | 2016.75 | 9372 | 9.77 | 1.08 |
| Italy | 732 | 2013.69 | 12,193 | 16.66 | 1.02 |
| Germany | 583 | 2013.47 | 12,825 | 22.00 | 1.24 |
| England | 540 | 2013.49 | 12,054 | 22.32 | 1.50 |
Top 5 most active institutions in GPR research between 2001 and 2021.
| Institution | Country | Number of Publications | Average Publication Year | Total |
Average Citations | Average Normal Citations |
|---|---|---|---|---|---|---|
| Chinese Academy of Sciences | China | 169 | 2016.35 | 2357 | 13.95 | 1.26 |
| National Research Council | Italy | 116 | 2012.61 | 2339 | 20.16 | 1.05 |
| Delft University of Technology | Holland | 91 | 2010.84 | 2784 | 30.59 | 1.29 |
| University of Illinois Urbana-Champaign | United States | 88 | 2012.77 | 2207 | 25.08 | 1.43 |
| Forschungszentrum Jülich | Germany | 85 | 2013.94 | 2427 | 28.55 | 1.57 |
Top 5 most productive authors in GPR research between 2001 and 2021.
| Scholar | Affiliation | Number of Publications | Average Publication Year | Total |
Average Citations | Average Normal Citations |
|---|---|---|---|---|---|---|
| S. Lambot | Université catholique de Louvain | 63 | 2013.97 | 1547 | 24.56 | 1.37 |
| F. Soldovieri | Italian National Research Council | 63 | 2013.06 | 1271 | 20.17 | 1.18 |
| H. Vereecken | Forschungszentrum Jülich | 47 | 2014.64 | 1118 | 23.79 | 1.33 |
| R. Persico | Institute for Archaeological and Monumental Heritage | 43 | 2012.55 | 747 | 17.37 | 0.85 |
| J. Van Der Kruk | Forschungszentrum Jülich | 42 | 2014.29 | 1027 | 24.25 | 1.39 |
Top 5 most influential publications in GPR research between 2001 and 2021.
| Scholars | Title | Journal | Impact |
Publication Year | Total |
Normal Citations |
|---|---|---|---|---|---|---|
| Giannopoulos [ |
“Modelling ground penetrating radar by GprMax” | Construction and Building Materials | 6.141 | 2005 | 409 | 11.32 |
| Yoshikawa and Hinzman [ |
“Shrinking thermokarst ponds and groundwater dynamics in discontinuous permafrost near Council, Alaska” | Permafrost and Periglacial Processes | 4.368 | 2003 | 334 | 10.29 |
| Lambot et al. [ |
“Modeling of ground-penetrating radar for accurate characterization of subsurface electric properties” | IEEE Transactions on Geoscience and Remote Sensing | 5.600 | 2004 | 299 | 8.74 |
| Warren et al. [ |
“gprMax: Open source software to simulate electromagnetic wave propagation for ground penetrating radar” | Computer Physics Communications | 4.390 | 2016 | 293 | 20.97 |
| Gurbuz et al. [ |
“Compressive sensing for subsurface imaging using ground penetrating radar” | Remote Sensing | 4.662 | 2009 | 234 | 3.93 |
Top 5 most cited references in GPR research between 2001 and 2021.
| Scholars | Title | Publication Year | Total |
|---|---|---|---|
| Davis and Annan [ |
“Ground-penetrating radar for high-resolution mapping of soil and rock stratigraphy” | 1989 | 741 |
| Neal [ |
“Ground-penetrating radar and its use in sedimentology: Principles, problems and progress” | 2004 | 491 |
| Daniels [ |
“Ground penetrating radar” | 2004 | 453 |
| Jol [ |
“Ground penetrating radar: Theory and applications” | 2009 | 423 |
| Huisman et al. [ |
“Measuring soil water content with ground penetrating radar: A review” | 2003 | 334 |
Top 5 most cited keywords in GPR research between 2001 and 2021.
| Keywords | Total Occurrences | Average Publication Year | Average Citations | Average Normal Citations |
|---|---|---|---|---|
| Ground penetrating radar | 2960 | 2014.25 | 15.53 | 1.00 |
| Non-destructive testing | 160 | 2014.80 | 18.69 | 1.32 |
| Geophysics | 128 | 2013.50 | 18.34 | 1.02 |
| Electrical resistivity tomography | 122 | 2014.31 | 11.81 | 0.63 |
| Radar | 110 | 2010.65 | 21.04 | 0.87 |
References
1. Rhee, J.Y.; Park, K.T.; Cho, J.W.; Lee, S.Y. A study of the application and the limitations of GPR investigation on underground survey of the Korean expressways. Remote Sens.; 2021; 13, 1805. [DOI: https://dx.doi.org/10.3390/rs13091805]
2. Zhou, D.; Zhu, H. Application of ground penetrating radar in detecting deeply embedded reinforcing bars in pile foundation. Adv. Civ. Eng.; 2021; 2021, 4813415. [DOI: https://dx.doi.org/10.1155/2021/4813415]
3. Xiang, Z.; Rashidi, A.; Ou, G.G. States of practice and research on applying GPR technology for labeling and scanning constructed facilities. J. Perform. Constr. Facil.; 2019; 33, 03119001. [DOI: https://dx.doi.org/10.1061/(ASCE)CF.1943-5509.0001313]
4. Dabous, S.A.; Yaghi, S.; Alkass, S.; Moselhi, O. Concrete bridge deck condition assessment using IR thermography and ground penetrating radar technologies. Autom. Constr.; 2017; 81, pp. 340-354. [DOI: https://dx.doi.org/10.1016/j.autcon.2017.04.006]
5. Teoh, Y.J.; Bruka, M.A.; Idris, N.M.; Ismail, N.A.; Muztaza, N.M. Introduction of a ground penetrating radar system for subsurface investigation in Balik Pulau, Penang Island. J. Phys. Conf. Ser.; 2018; 995, 012098. [DOI: https://dx.doi.org/10.1088/1742-6596/995/1/012098]
6. Lai, W.W.L.; Derobert, X.; Annan, P. A review of ground penetrating radar application in civil engineering: A 30-year journey from locating and testing to imaging and diagnosis. NDT E Int.; 2018; 96, pp. 58-78.
7. Rathod, H.; Debeck, S.; Gupta, R.; Chow, B. Applicability of GPR and a rebar detector to obtain rebar information of existing concrete structures. Case Stud. Constr. Mater.; 2019; 11, e00240. [DOI: https://dx.doi.org/10.1016/j.cscm.2019.e00240]
8. Wang, S.; Zhao, S.; Al-Qadi, I.L. Real-time monitoring of asphalt concrete pavement density during construction using ground penetrating radar: Theory to practice. Transp. Res. Rec.; 2019; 2673, pp. 329-338. [DOI: https://dx.doi.org/10.1177/0361198119841038]
9. Joshaghani, A.; Shokrabadi, M. Ground penetrating radar (GPR) applications in concrete pavements. Int. J. Pavement Eng.; 2022; 23, pp. 4504-4531. [DOI: https://dx.doi.org/10.1080/10298436.2021.1954182]
10. Tabarro, P.G.; Pouliot, J.; Losier, L.M.; Fortier, R. Detection and location of buried infrastructures using ground penetrating radar: A new approach based on GIS and data integration. Int. J. 3D Inf. Model. IJ3DIM; 2018; 7, pp. 57-77. [DOI: https://dx.doi.org/10.4018/IJ3DIM.2018040103]
11. Li, S.; Zhang, Y.; Han, S. Safety inspection system and comprehensive evaluation method for concrete structure of gas pipeline tunnel based on fuzzy mathematics. Adv. Mech. Eng.; 2021; 13, 16878140211046098. [DOI: https://dx.doi.org/10.1177/16878140211046098]
12. Kgarume, T.; Van Schoor, M.; Nontso, Z. The use of 3D ground penetrating radar to mitigate the risk associated with falls of ground in Bushveld Complex platinum mines. J. S. Afr. Inst. Min. Metall.; 2019; 119, pp. 973-982. [DOI: https://dx.doi.org/10.17159/2411-9717/603/2019]
13. Fedorova, L.; Lejzerowicz, A.; Kulyandin, G.; Savvin, D.; Fedorov, M. Ground penetrating radar investigations of the geological structure of loose sediments at solid mineral deposits. E3S Web Conf.; 2020; 192, 04005. [DOI: https://dx.doi.org/10.1051/e3sconf/202019204005]
14. Zheng, L.; Li, X.; Liu, Z.; Huang, D.; Tang, Z. Accuracy evaluation of advanced geological prediction based on improved AHP and GPR. Math. Probl. Eng.; 2020; 2020, 8617165. [DOI: https://dx.doi.org/10.1155/2020/8617165]
15. Ebraheem, M.O.; Ibrahim, H.A. Contributions of ground-penetrating radar in research of some predynastic and dynastic archaeological sites at the eastern and western banks of the River Nile, Assiut, Egypt. Archaeol. Prospect.; 2022; 29, pp. 177-189.
16. El Emam, A.E.; Lethy, A.; Radwan, A.M.; Awad, A. Archaeological investigation and hazard assessment using magnetic, ground-penetrating radar, and GPS tools at Dahshour area, Giza, Egypt. Front. Earth Sci.; 2021; 9, 437. [DOI: https://dx.doi.org/10.3389/feart.2021.674953]
17. Huang, Y.; Ding, X.H.; Liu, R.; He, Y.; Wu, S. Reviewing the domain of technology and innovation management: A visualizing bibliometric analysis. SAGE Open; 2019; 9, 2158244019854644. [DOI: https://dx.doi.org/10.1177/2158244019854644]
18. de Oliveira, O.J.; da Silva, F.F.; Juliani, F.; Barbosa, L.C.F.M.; Nunhes, T.V. Bibliometric Method for Mapping the State-of-the-Art and Identifying Research Gaps and Trends in Literature: An Essential Instrument to Support the Development of Scientific Projects. Scientometrics Recent Advances; IntechOpen: London, UK, 2019.
19. Donthu, N.; Kumar, S.; Mukherjee, D.; Pandey, N.; Lim, W.M. How to conduct a bibliometric analysis: An overview and guidelines. J. Bus. Res.; 2021; 133, pp. 285-296. [DOI: https://dx.doi.org/10.1016/j.jbusres.2021.04.070]
20. Mejia, C.; Wu, M.; Zhang, Y.; Kajikawa, Y. Exploring topics in bibliometric research through citation networks and semantic analysis. Front. Res. Metr.; 2021; 6, 742311. [DOI: https://dx.doi.org/10.3389/frma.2021.742311] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34632257]
21. Gizzi, F.T.; Leucci, G. Global research patterns on ground penetrating radar (GPR). Surv. Geophys.; 2018; 39, pp. 1039-1068. [DOI: https://dx.doi.org/10.1007/s10712-018-9475-1]
22. Snyder, H. Literature review as a research methodology: An overview and guidelines. J. Bus. Res.; 2019; 104, pp. 333-339. [DOI: https://dx.doi.org/10.1016/j.jbusres.2019.07.039]
23. Moral Muñoz, J.A.; Herrera Viedma, E.; Santisteban Espejo, A.; Cobo, M.J. Software tools for conducting bibliometric analysis in science: An up-to-date review. Prof. Inf.; 2020; 29, e290103. [DOI: https://dx.doi.org/10.3145/epi.2020.ene.03]
24. Birkle, C.; Pendlebury, D.A.; Schnell, J.; Adams, J. Web of Science as a data source for research on scientific and scholarly activity. Quant. Sci. Stud.; 2020; 1, pp. 363-376. [DOI: https://dx.doi.org/10.1162/qss_a_00018]
25. Butt, N.S.; Malik, A.A.; Shahbaz, M.Q. Bibliometric analysis of statistics journals indexed in web of science under emerging source citation index. SAGE Open; 2021; 11, 2158244020988870. [DOI: https://dx.doi.org/10.1177/2158244020988870]
26. Butler, L.; Visser, M.S. Extending citation analysis to non-source items. Scientometrics; 2006; 66, pp. 327-343. [DOI: https://dx.doi.org/10.1007/s11192-006-0024-1]
27. Waltman, L.; Van Eck, N.J.; Noyons, E.C. A unified approach to mapping and clustering of bibliometric networks. J. Informetr.; 2010; 4, pp. 629-635. [DOI: https://dx.doi.org/10.1016/j.joi.2010.07.002]
28. Kessler, M.M. Bibliographic coupling between scientific papers. Am. Doc.; 1963; 14, pp. 10-25. [DOI: https://dx.doi.org/10.1002/asi.5090140103]
29. Rehman, S.U.; Al-Almaie, S.M.; Haq, I.U.; Ahmad, S.; Ahmad, S.; Al-Shammari, M.A.; Darwish, M.; Mustafa, T. Journal of family and community medicine: A scientometric analysis 1994–2020. J. Fam. Community Med.; 2021; 28, 164.
30. Yanhui, S.; Lijuan, W.; Shiji, C. An exploratory study of the all-author bibliographic coupling analysis: Taking scientometrics for example. J. Inf. Sci.; 2022; 48, pp. 767-782. [DOI: https://dx.doi.org/10.1177/0165551520981293]
31. Biresselioglu, M.E.; Demir, M.H.; Solak, B.; Kayacan, A.; Altinci, S. Investigating the trends in arctic research: The increasing role of social sciences and humanities. Sci. Total Environ.; 2020; 729, 139027. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2020.139027]
32. Van Eck, N.J.; Waltman, L. Visualizing bibliometric networks. Measuring Scholarly Impact; Springer: Cham, Switzerland, 2014; pp. 285-320.
33. Giannopoulos, A. Modelling ground penetrating radar by GprMax. Constr. Build. Mater.; 2005; 19, pp. 755-762. [DOI: https://dx.doi.org/10.1016/j.conbuildmat.2005.06.007]
34. Yoshikawa, K.; Hinzman, L.D. Shrinking thermokarst ponds and groundwater dynamics in discontinuous permafrost near Council, Alaska. Permafr. Periglac. Process.; 2003; 14, pp. 151-160. [DOI: https://dx.doi.org/10.1002/ppp.451]
35. Lambot, S.; Slob, E.C.; van den Bosch, I.; Stockbroeckx, B.; Vanclooster, M. Modeling of ground-penetrating radar for accurate characterization of subsurface electric properties. IEEE Trans. Geosci. Remote Sens.; 2004; 42, pp. 2555-2568. [DOI: https://dx.doi.org/10.1109/TGRS.2004.834800]
36. Warren, C.; Giannopoulos, A.; Giannakis, I. gprMax: Open source software to simulate electromagnetic wave propagation for ground penetrating radar. Comput. Phys. Commun.; 2016; 209, pp. 163-170. [DOI: https://dx.doi.org/10.1016/j.cpc.2016.08.020]
37. Gurbuz, A.C.; McClellan, J.H.; Scott, W.R., Jr. Compressive sensing for subsurface imaging using ground penetrating radar. Signal Process.; 2009; 89, pp. 1959-1972. [DOI: https://dx.doi.org/10.1016/j.sigpro.2009.03.030]
38. Mas-Tur, A.; Roig-Tierno, N.; Sarin, S.; Haon, C.; Sego, T.; Belkhouja, M.; Porter, A.; Merigó, J.M. Co-citation, bibliographic coupling and leading authors, institutions and countries in the 50 years of technological forecasting and social change. Technol. Forecast Soc. Chang.; 2021; 165, 120487. [DOI: https://dx.doi.org/10.1016/j.techfore.2020.120487]
39. Davis, J.L.; Annan, A.P. Ground-penetrating radar for high-resolution mapping of soil and rock stratigraphy. Geophys. Prospect.; 1989; 37, pp. 531-551. [DOI: https://dx.doi.org/10.1111/j.1365-2478.1989.tb02221.x]
40. Neal, A. Ground-penetrating radar and its use in sedimentology: Principles, problems and progress. Earth Sci. Rev.; 2004; 66, pp. 261-330. [DOI: https://dx.doi.org/10.1016/j.earscirev.2004.01.004]
41. Daniels, D.J. Ground Penetrating Radar; 2nd ed. IEE Radar, Sonar and Navigation Series; The Institution of Electrical Engineers: London, UK, 2004.
42. Jol, H.M. Ground Penetrating Radar: Theory and Applications; Elsevier: Amsterdam, The Netherlands, 2009; Volume 509.
43. Huisman, J.A.; Hubbard, S.S.; Redman, J.D.; Annan, A.P. Measuring soil water content with ground penetrating radar: A review. Vadose Zone J.; 2003; 2, pp. 476-491. [DOI: https://dx.doi.org/10.2136/vzj2003.4760]
44. Tripathi, M.; Kumar, S.; Sonker, S.K.; Babbar, P. Occurrence of author keywords and keywords plus in social sciences and humanities research: A preliminary study. CJSIM; 2018; 12, pp. 215-232. [DOI: https://dx.doi.org/10.1080/09737766.2018.1436951]
45. Bordag, S. A comparison of co-occurrence and similarity measures as simulations of context. International Conference on Intelligent Text Processing and Computational Linguistics; Springer: Berlin/Heidelberg, Germany, 2008; pp. 52-63.
46. Radhakrishnan, S.; Erbis, S.; Isaacs, J.A.; Kamarthi, S. Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature. PLoS ONE; 2017; 12, e0172778.
47. Hussein, M.; Zayed, T. Crane operations and planning in modular integrated construction: Mixed review of literature. Autom. Constr.; 2021; 122, 103466. [DOI: https://dx.doi.org/10.1016/j.autcon.2020.103466]
48. Benedetto, A.; Pajewski, L. Civil Engineering Applications of Ground Penetrating Radar; Springer: Cham, Switzerland, 2015; 371.
49. Pérez-Gracia, V.; Caselles, O.; Clapés, J.; Santos-Assunçao, S. GPR building inspection: Examples of building structures assessed with ground penetrating radar. Proceedings of the 2017 9th International Workshop on Advanced Ground Penetrating Radar (IWAGPR); Edinburgh, UK, 28–30 June 2017.
50. Benedetto, A.; Tosti, F.; Ciampoli, L.B.; D’amico, F. An overview of ground-penetrating radar signal processing techniques for road inspections. Signal Process.; 2017; 132, pp. 201-209. [DOI: https://dx.doi.org/10.1016/j.sigpro.2016.05.016]
51. Alani, A.M.; Aboutalebi, M.; Kilic, G. Applications of ground penetrating radar (GPR) in bridge deck monitoring and assessment. J. Appl. Geophy.; 2013; 97, pp. 45-54. [DOI: https://dx.doi.org/10.1016/j.jappgeo.2013.04.009]
52. Benedetto, A.; Tosti, F.; Ciampoli, L.B.; Calvi, A.; Brancadoro, M.G.; Alani, A.M. Railway ballast condition assessment using ground-penetrating radar–An experimental, numerical simulation and modelling development. Constr. Build. Mater.; 2017; 140, pp. 508-520. [DOI: https://dx.doi.org/10.1016/j.conbuildmat.2017.02.110]
53. Alani, A.M.; Tosti, F. GPR applications in structural detailing of a major tunnel using different frequency antenna systems. Constr. Build. Mater.; 2018; 158, pp. 1111-1122. [DOI: https://dx.doi.org/10.1016/j.conbuildmat.2017.09.100]
54. Lopera, O.; Slob, E.C.; Milisavljevic, N.; Lambot, S. Filtering soil surface and antenna effects from GPR data to enhance landmine detection. IEEE Trans. Geosci. Remote Sens.; 2007; 45, pp. 707-717. [DOI: https://dx.doi.org/10.1109/TGRS.2006.888136]
55. Al-Qadi, I.L.; Lahouar, S. Measuring layer thicknesses with GPR–Theory to practice. Constr. Build. Mater.; 2005; 19, pp. 763-772. [DOI: https://dx.doi.org/10.1016/j.conbuildmat.2005.06.005]
56. Benedetto, A.; Pensa, S. Indirect diagnosis of pavement structural damages using surface GPR reflection techniques. J. Appl. Geophy.; 2007; 62, pp. 107-123. [DOI: https://dx.doi.org/10.1016/j.jappgeo.2006.09.001]
57. Loizos, A.; Plati, C. Accuracy of pavement thicknesses estimation using different ground penetrating radar analysis approaches. NDT E Int.; 2007; 40, pp. 147-157. [DOI: https://dx.doi.org/10.1016/j.ndteint.2006.09.001]
58. Loizos, A.; Plati, C. Accuracy of ground penetrating radar horn-antenna technique for sensing pavement subsurface. IEEE Sens. J.; 2007; 7, pp. 842-850. [DOI: https://dx.doi.org/10.1109/JSEN.2007.894152]
59. Lahouar, S.; Al-Qadi, I.L. Automatic detection of multiple pavement layers from GPR data. NDT E Int.; 2008; 41, pp. 69-81. [DOI: https://dx.doi.org/10.1016/j.ndteint.2007.09.001]
60. Al-Qadi, I.L.; Leng, Z.; Lahouar, S.; Baek, J. In-place hot-mix asphalt density estimation using ground-penetrating radar. Transp. Res. Rec.; 2010; 2152, pp. 19-27. [DOI: https://dx.doi.org/10.3141/2152-03]
61. Plati, C.; Loizos, A. Estimation of in-situ density and moisture content in HMA pavements based on GPR trace reflection amplitude using different frequencies. J. Appl. Geophy.; 2013; 97, pp. 3-10. [DOI: https://dx.doi.org/10.1016/j.jappgeo.2013.04.007]
62. Yan, J.; Jaw, S.W.; Soon, K.H.; Wieser, A.; Schrotter, G. Towards an underground utilities 3D data model for land administration. Remote Sens.; 2019; 11, 1957. [DOI: https://dx.doi.org/10.3390/rs11171957]
63. Iftimie, N.; Savin, A.; Steigmann, R.; Dobrescu, G.S. Underground pipeline identification into a non-destructive case study based on ground-penetrating radar imaging. Remote Sens.; 2021; 13, 3494. [DOI: https://dx.doi.org/10.3390/rs13173494]
64. Bernatek-Jakiel, A.; Kondracka, M. Detection of soil pipes using ground penetrating radar. Remote Sens.; 2019; 11, 1864. [DOI: https://dx.doi.org/10.3390/rs11161864]
65. Pérez-Gracia, V.; García García, F.; Rodriguez Abad, I. GPR evaluation of the damage found in the reinforced concrete base of a block of flats: A case study. NDT E Int.; 2008; 41, pp. 341-353. [DOI: https://dx.doi.org/10.1016/j.ndteint.2008.01.001]
66. Ortega-Ramírez, J.; Bano, M.; Cordero-Arce, M.T.; Villa-Alvarado, L.A.; Fraga, C.C. Application of non-invasive geophysical methods (GPR and ERT) to locate the ancient foundations of the first cathedral of Puebla, Mexico. A case study. J. Appl. Geophys.; 2020; 174, 103958. [DOI: https://dx.doi.org/10.1016/j.jappgeo.2020.103958]
67. Salako, A.O.; Osotuyi, A.G.; Adepelumi, A.A. Seepage investigations of heterogeneous soils beneath some buildings using geophysical approaches: Example from southwestern Nigeria. Int. J. Geo-Eng.; 2019; 10, 11. [DOI: https://dx.doi.org/10.1186/s40703-019-0107-5]
68. Pérez-Gracia, V.; Solla, M. Inspection procedures for effective GPR surveying of buildings. Civil Engineering Applications of Ground Penetrating Radar; Benedetto, A.; Pajewski, L. Springer: Berlin/Heidelberg, Germany, 2015; pp. 97-123.
69. Solla, M.; Pérez-Gracia, V.; Fontul, S. A review of GPR application on transport infrastructures: Troubleshooting and best practices. Remote Sens.; 2021; 13, 672. [DOI: https://dx.doi.org/10.3390/rs13040672]
70. Pedret Rodés, J.; Martínez Reguero, A.; Pérez-Gracia, V. GPR spectra for monitoring asphalt pavements. Remote Sens.; 2020; 12, 1749. [DOI: https://dx.doi.org/10.3390/rs12111749]
71. Liu, X.; Hao, P.; Wang, A.; Zhang, L.; Gu, B.; Lu, X. Non-destructive detection of highway hidden layer defects using a ground-penetrating radar and adaptive particle swarm support vector machine. PeerJ Comput. Sci.; 2021; 7, e417. [DOI: https://dx.doi.org/10.7717/peerj-cs.417] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33834102]
72. Alsharqawi, M.; Zayed, T.; Dabous, S.A. Integrated condition rating and forecasting method for bridge decks using visual inspection and ground penetrating radar. Autom. Constr.; 2018; 89, pp. 135-145. [DOI: https://dx.doi.org/10.1016/j.autcon.2018.01.016]
73. Dinh, K.; Gucunski, N.; Duong, T.H. An algorithm for automatic localization and detection of rebars from GPR data of concrete bridge decks. Autom. Constr.; 2018; 89, pp. 292-298. [DOI: https://dx.doi.org/10.1016/j.autcon.2018.02.017]
74. Abdelkader, E.M.; Marzouk, M.; Zayed, T. An optimization-based methodology for the definition of amplitude thresholds of the ground penetrating radar. Soft Comput.; 2019; 23, pp. 12063-12086. [DOI: https://dx.doi.org/10.1007/s00500-019-03764-3]
75. Dinh, K.; Gucunski, N.; Zayed, T. Automated visualization of concrete bridge deck condition from GPR data. NDT E Int.; 2019; 102, pp. 120-128. [DOI: https://dx.doi.org/10.1016/j.ndteint.2018.11.015]
76. Bachiri, T.; Khamlichi, A.; Bezzazi, M. Bridge deck condition assessment by using GPR: A review. MATEC Web Conf.; 2018; 191, 00004. [DOI: https://dx.doi.org/10.1051/matecconf/201819100004]
77. Zaki, A.; Megat Johari, M.A.; Wan Hussin, W.M.A.; Jusman, Y. Experimental assessment of rebar corrosion in concrete slab using ground penetrating radar (GPR). Int. J. Corros.; 2018; 2018, 5389829. [DOI: https://dx.doi.org/10.1155/2018/5389829]
78. Plati, C.; Loizos, A.; Gkyrtis, K. Integration of non-destructive testing methods to assess asphalt pavement thickness. NDT E Int.; 2020; 115, 102292. [DOI: https://dx.doi.org/10.1016/j.ndteint.2020.102292]
79. Oreto, C.; Massotti, L.; Biancardo, S.A.; Veropalumbo, R.; Viscione, N.; Russo, F. BIM-based pavement management tool for scheduling urban road maintenance. Infrastructures; 2021; 6, 148. [DOI: https://dx.doi.org/10.3390/infrastructures6110148]
80. Oreto, C.; Biancardo, S.A.; Viscione, N.; Veropalumbo, R.; Russo, F. Road pavement information modeling through maintenance scenario evaluation. J. Adv. Transp.; 2021; 2021, 8823117. [DOI: https://dx.doi.org/10.1155/2021/8823117]
81. Gabryś, M.; Ortyl, Ł. Georeferencing of multi-channel GPR—Accuracy and efficiency of mapping of underground utility networks. Remote Sens.; 2020; 12, 2945. [DOI: https://dx.doi.org/10.3390/rs12182945]
82. Ali, H.; Ideris, N.S.M.; Zaidi, A.A.; Azalan, M.Z.; Amran, T.T.; Ahmad, M.R.; Rahim, N.A.; Shukor, S.A. Ground penetrating radar for buried utilities detection and mapping: A review. J. Phys. Conf. Ser.; 2021; 2107, 012056. [DOI: https://dx.doi.org/10.1088/1742-6596/2107/1/012056]
83. Kuliczkowska, E. An analysis of road pavement collapses and traffic safety hazards resulting from leaky sewers. Balt. J. Road Bridge Eng.; 2016; 11, pp. 251-258. [DOI: https://dx.doi.org/10.3846/bjrbe.2016.29]
84. Chen, S.Y.; Lin, M.S.; Hsiao, G.L.K.; Wang, T.C.; Kao, C.S. Underground pipeline leakage risk assessment in an urban city. Int. J. Environ. Res. Public Health; 2020; 17, 3929. [DOI: https://dx.doi.org/10.3390/ijerph17113929]
85. Slob, E.; Sato, M.; Olhoeft, G. Surface and borehole ground-penetrating-radar developments. Geophysics; 2010; 75, pp. A103-A175. [DOI: https://dx.doi.org/10.1190/1.3480619]
86. Proulx-McInnis, S.; St-Hilaire, A.; Rousseau, A.N.; Jutras, S. A review of ground-penetrating radar studies related to peatland stratigraphy with a case study on the determination of peat thickness in a northern boreal fen in Quebec, Canada. Prog. Phys. Geogr.; 2013; 37, pp. 767-786. [DOI: https://dx.doi.org/10.1177/0309133313501106]
87. Corradini, E.; Wilken, D.; Zanon, M.; Groß, D.; Lübke, H.; Panning, D.; Dörfler, W.; Rusch, K.; Mecking, R.; Erkul, E. et al. Reconstructing the palaeoenvironment at the early Mesolithic site of Lake Duvensee: Ground-penetrating radar and geoarchaeology for 3D facies mapping. Holocene; 2020; 30, pp. 820-833. [DOI: https://dx.doi.org/10.1177/0959683620902234]
88. Conyers, L.B. Ground-Penetrating Radar for Geoarchaeology; John Wiley & Sons: Hoboken, NJ, USA, 2016.
89. Conyers, L.B.; Cameron, C.M. Ground-penetrating radar techniques and three-dimensional computer mapping in the American Southwest. J. Field Archaeol.; 1998; 25, pp. 417-430.
90. Annan, A.P.; Cosway, S.W.; Redman, J.D. Water table detection with ground-penetrating radar. SEG Technical Program Expanded Abstracts; Society of Exploration Geophysicists: Tulsa, OK, USA, 1991; pp. 494-496.
91. Mahmoudzadeh, M.R.; Francés, A.P.; Lubczynski, M.; Lambot, S. Using ground penetrating radar to investigate the water table depth in weathered granites—Sardon case study, Spain. J. Appl. Geophys.; 2012; 79, pp. 17-26. [DOI: https://dx.doi.org/10.1016/j.jappgeo.2011.12.009]
92. Lambot, S.; Weihermüller, L.; Huisman, J.A.; Vereecken, H.; Vanclooster, M.; Slob, E.C. Analysis of air-launched ground-penetrating radar techniques to measure the soil surface water content. Water Resour. Res.; 2006; 42, pp. 1-12. [DOI: https://dx.doi.org/10.1029/2006WR005097]
93. Maryanto, S.; Suciningtyas, I.K.L.N.; Dewi, C.N.; Rachmansyah, A. Integrated resistivity and ground penetrating radar observations of underground seepage of hot water at Blawan-Ijen geothermal field. Int. J. Geophys.; 2016; 2016, 6034740. [DOI: https://dx.doi.org/10.1155/2016/6034740]
94. Liu, X.; Chen, J.; Cui, X.; Liu, Q.; Cao, X.; Chen, X. Measurement of soil water content using ground-penetrating radar: A review of current methods. Int. J. Digit. Earth; 2019; 12, pp. 95-118. [DOI: https://dx.doi.org/10.1080/17538947.2017.1412520]
95. Zhang, S.; Zhang, L.; Ling, T.; Fu, G.; Guo, Y. Experimental research on evaluation of soil water content using ground penetrating radar and wavelet packet-based energy analysis. Remote Sens.; 2021; 13, 5047. [DOI: https://dx.doi.org/10.3390/rs13245047]
96. Knight, R. Ground penetrating radar for environmental applications. Annu. Rev. Earth Planet Sci.; 2001; 29, pp. 229-255. [DOI: https://dx.doi.org/10.1146/annurev.earth.29.1.229]
97. Mesbah, H.; Shokry, M.; Soliman, M.; Atya, M. Integrated geophysical investigations to detect the shallow subsurface settings at new Sohag city, Egypt. Int. J. Geosci.; 2017; 8, pp. 364-377. [DOI: https://dx.doi.org/10.4236/ijg.2017.83019]
98. Sulaiman, N.; Nordiana, M.M.; Azwin, I.N.; Taqiuddin, Z.M.; Maslinda, U.; Hisham, H.; Amalina, M.N.; Saharudin, M.A.; Nordiana, A.N. Integration of ground penetrating radar (GPR) and 2-D resistivity imaging methods for soil investigation. IOP Conf. Ser. Earth Environ. Sci.; 2017; 62, 012007. [DOI: https://dx.doi.org/10.1088/1755-1315/62/1/012007]
99. Araffa, S.A.S.; Gobashy, M.M.; Khalil, M.H.; Abdelaal, A. Integration of geophysical techniques to detect geotechnical hazards: A case study in Mokattam, Cairo, Egypt. Bull. Eng. Geol. Environ.; 2021; 80, pp. 8021-8041. [DOI: https://dx.doi.org/10.1007/s10064-021-02388-y]
100. Capozzoli, L.; Giampaolo, V.; De Martino, G.; Perciante, F.; Lapenna, V.; Rizzo, E. ERT and GPR prospecting applied to unsaturated and subwater analogue archaeological site in a full scale laboratory. Appl. Sci.; 2022; 12, 1126. [DOI: https://dx.doi.org/10.3390/app12031126]
101. Murín, I.; Neumann, M.; Brady, C.; Bátora, J.; Čapo, M.; Drozd, D. Application of magnetometry, georadar (GPR) and geoelectrical methods in archaeo-geophysical investigation of a Napoleonic battlefield with fortification at Pressburg (Bratislava, Slovakia). J. Appl. Geophys.; 2022; 196, 104493. [DOI: https://dx.doi.org/10.1016/j.jappgeo.2021.104493]
102. Hussain, Y.; Uagoda, R.; Borges, W.; Nunes, J.; Hamza, O.; Condori, C.; Aslam, K.; Dou, J.; Cárdenas-Soto, M. The potential use of geophysical methods to identify cavities, sinkholes and pathways for water infiltration. Water; 2020; 12, 2289. [DOI: https://dx.doi.org/10.3390/w12082289]
103. Clementini, C.; Latini, D.; Gagliardi, V.; Ciampoli, L.B.; D’Amico, F.; Del Frate, F. Synergistic Monitoring of Transport Infrastructures by Multi-Temporal InSAR and GPR Technologies: A Case Study in Salerno, Italy. Proceedings of the Earth Resources and Environmental Remote Sensing/GIS Applications XII; Online, 13–18 September 2021.
104. D’Amico, F.; Gagliardi, V.; Clementini, C.; Latini, D.; Del Frate, F.; Bianchini Ciampoli, L.; Di Benedetto, A.; Fiani, M.; Benedetto, A. Integrated Health Monitoring of Transport Assets by Ground-Based Non-Destructive Technologies (NDTs) and Satellite Remote Sensing Analysis. Proceedings of the EGU General Assembly Conference Abstracts; Online, 19–30 April 2021.
105. Tosti, F.; Gagliardi, V.; Ciampoli, L.B.; Benedetto, A.; Threader, S.; Alani, A.M. Integration of Remote Sensing and Ground-Based Non-Destructive Methods in Transport Infrastructure Monitoring: Advances, Challenges and Perspectives. Proceedings of the 2021 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS); Jakarta Pusat, Indonesia, 29–30 September 2021.
106. Maury, S.; Tiwari, R.K.; Balaji, S. Joint application of satellite remote sensing, ground penetrating radar (GPR) and resistivity techniques for targeting ground water in fractured Ophiolites of South Andaman Island, India. Environ. Earth Sci.; 2016; 75, 237. [DOI: https://dx.doi.org/10.1007/s12665-015-5007-1]
107. Upadhyay, R.K.; Kishore, N.; Sharma, M. Delineation and mapping of palaeochannels using remote sensing, geophysical, and sedimentological techniques: A comprehensive approach. Water Sci.; 2021; 35, pp. 100-108. [DOI: https://dx.doi.org/10.1080/23570008.2021.1941691]
108. Kannaujiya, S.; Chattoraj, S.L.; Jayalath, D.; Bajaj, K.; Podali, S.; Bisht, M.P.S. Integration of satellite remote sensing and geophysical techniques (electrical resistivity tomography and ground penetrating radar) for landslide characterization at Kunjethi (Kalimath), Garhwal Himalaya, India. Nat. Hazards; 2019; 97, pp. 1191-1208. [DOI: https://dx.doi.org/10.1007/s11069-019-03695-0]
109. Agapiou, A.; Lysandrou, V.; Sarris, A.; Papadopoulos, N.; Hadjimitsis, D.G. Fusion of satellite multispectral images based on ground-penetrating radar (GPR) data for the investigation of buried concealed archaeological remains. Geosciences; 2017; 7, 40. [DOI: https://dx.doi.org/10.3390/geosciences7020040]
110. Zong, X.; Wang, X.Y.; Luo, L. The integration of VHR satellite imagery, GPR survey and boring for archaeological prospection at the Longcheng Site in Anhui Province, China. Archaeometry; 2018; 60, pp. 1088-1105. [DOI: https://dx.doi.org/10.1111/arcm.12373]
111. Fernández, M.G.; López, Y.Á.; Arboleya, A.A.; Valdés, B.G.; Vaqueiro, Y.R.; Andrés, F.L.H.; García, A.P. Synthetic aperture radar imaging system for landmine detection using a ground penetrating radar on board a unmanned aerial vehicle. IEEE Access; 2018; 6, pp. 45100-45112. [DOI: https://dx.doi.org/10.1109/ACCESS.2018.2863572]
112. Garcia-Fernandez, M.; Alvarez-Lopez, Y.; Las Heras, F.; Gonzalez-Valdes, B.; Rodriguez-Vaqueiro, Y.; Pino, A.; Arboleya-Arboleya, A. GPR system onboard a UAV for non-invasive detection of buried objects. Proceedings of the 2018 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting; Boston, MA, USA, 8–13 July 2018.
113. Šipoš, D.; Gleich, D. A lightweight and low-power UAV-borne ground penetrating radar design for landmine detection. Sensors; 2020; 20, 2234. [DOI: https://dx.doi.org/10.3390/s20082234] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32326550]
114. Hou, J.; Yan, Y.; Cong, P. Application of technology of UAV-mounted ground penetrating radar in the study of the thickness of soil plow layer. IOP Conf. Ser. Earth Environ. Sci.; 2021; 719, 042074. [DOI: https://dx.doi.org/10.1088/1755-1315/719/4/042074]
115. Li, Y.; Liu, C.; Yue, G.; Gao, Q.; Du, Y. Deep learning-based pavement subsurface distress detection via ground penetrating radar data. Autom. Constr.; 2022; 142, pp. 1-11. [DOI: https://dx.doi.org/10.1016/j.autcon.2022.104516]
116. Wang, R.; Zhang, J.; Liu, X. A most-unfavorable-condition method for bridge-damage detection and analysis using PSP-inSAR. Remote Sens.; 2022; 14, 137. [DOI: https://dx.doi.org/10.3390/rs14010137]
117. Schlögl, M.; Dorninger, P.; Kwapisz, M.; Ralbovsky, M.; Spielhofer, R. Remote sensing techniques for bridge deformation monitoring at millimetric scale: Investigating the potential of satellite radar interferometry, airborne laser scanning and ground-based mobile laser scanning. PFG-J. Photogramm. Remote Sens. Geoinf. Sci.; 2022; 90, pp. 391-411. [DOI: https://dx.doi.org/10.1007/s41064-022-00210-2]
118. Hu, M.; Xu, Y.; Li, S.; Lu, H. Detection of defect in ballastless track based on impact echo method combined with improved SAFT algorithm. Eng. Struct.; 2022; 269, 114779. [DOI: https://dx.doi.org/10.1016/j.engstruct.2022.114779]
119. Stüwe, I.; Zacherl, L.; Grosse, C.U. Ultrasonic and impact-echo testing for the detection of scaling in geothermal pipelines. J. Nondestruct. Eval.; 2023; 42, 18. [DOI: https://dx.doi.org/10.1007/s10921-023-00926-0]
120. Abudeif, A.M.; Abdel Aal, G.Z.; Masoud, A.M.; Mohammed, M.A. Detection of groundwater pathways to monitor their level rise in Osirion at Abydos archaeological site for reducing deterioration hazards, Sohag, Egypt using electrical resistivity tomography technique. Appl. Sci.; 2022; 12, 10417. [DOI: https://dx.doi.org/10.3390/app122010417]
121. Guo, Y.; Cui, Y.; Xie, J.; Luo, Y.; Zhang, P.; Liu, H.; Liu, J. Seepage detection in earth-filled dam from self-potential and electrical resistivity tomography. Eng. Geol.; 2022; 306, 106750. [DOI: https://dx.doi.org/10.1016/j.enggeo.2022.106750]
122. Zhou, R.; Wen, Z.; Su, H. Automatic recognition of earth rock embankment leakage based on UAV passive infrared thermography and deep learning. ISPRS J. Photogramm. Remote Sens.; 2022; 191, pp. 85-104. [DOI: https://dx.doi.org/10.1016/j.isprsjprs.2022.07.009]
123. Loiotine, L.; Andriani, G.F.; Derron, M.H.; Parise, M.; Jaboyedoff, M. Evaluation of infraRed thermography supported by UAV and field surveys for rock mass characterization in complex settings. Geosciences; 2022; 12, 116. [DOI: https://dx.doi.org/10.3390/geosciences12030116]
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; Zayed, Tarek 4
1 Department of Building and Real Estate, Faculty of Construction and Environment, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong; Construction and Project Management Research Institute, Housing and Building National Research Centre, Giza 12311, Egypt
2 Department of Building and Real Estate, Faculty of Construction and Environment, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong; Structural Engineering Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt
3 Department of Buildings, Civil and Environmental Engineering, Concordia University, Montreal, QC H3G 1M8, Canada; Department of Architecture and Environmental Planning, College of Engineering and Petroleum, Hadhramout University, Mukalla 50512, Yemen
4 Department of Building and Real Estate, Faculty of Construction and Environment, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong