Kharisma Wira Nindhita
| Ahmad Zaki* | Guntur Nugroho
© 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
In recent years, various monitoring methods for concrete structures have been widely researched and applied. However, there are still many research gaps found in related research due to the absence of comprehensive research that maps this research trend. This research presents a comprehensive bibliometric and scientometric analysis regarding "Monitoring Cracks in Concrete Structures" by Scopus over the last decade (2013-2023) with the help of VOS viewer software. From the results of the analysis, it was found that the research trend was increasing with research developments led by China and the USA. The results of the network visualization analysis also show 7 clusters or hotspots for research development, one of which is the concrete damage detection method. In the future, collaboration and integration of monitoring systems based on IoT, deep learning, and CNN can be further developed, so that the data produced is more real-time and accurate.
bibliometric, concrete, crack, monitoring, scientometric, VOS Viewer
Concrete is a popular material used in the field of infrastructure construction, be it buildings, bridges, roads, drainage channels, dams and coastal buildings [1, 2]. The use of concrete materials always increases from year to year globally [3]. In 2019 concrete use increased from 2.8 to 4.08 billion tons [4]. Meanwhile, the type of concrete structure that is most widely used today is the type of reinforced concrete which is more practical, but is able to withstand compression and bending [5]. However, steel reinforcement also has a weakness for corrosion which can reduce the strength and service life of the structure, as well as increase the cost of maintenance and repair of the structure [1, 6-8].
Apart from that, damage to concrete, especially cracks, both small and large, due to overload or weather can also cause further damage to the concrete, such as corrosion [9, 10]. The accumulation of corrosion or rust products in the reinforcement can peel off the concrete cover, causing structural failure [11-14]. Therefore, to mitigate and prevent more severe cracks, it is necessary to carry out a real-time monitoring process. So, concrete can be handled as early as possible to save costs and repair labor [1, 15, 16].
Remembering that the monitoring process in concrete buildings is very important for the engineering aspect. So, in recent years various monitoring methods on concrete structures have been widely researched and applied. Such as research related to the use of Non-Destructive Test (NDT) methods in the form of wave emissions [17], one of which is Acoustic Emission (AE) [6, 16, 18] and Electromechanical Impedance (EMI) [19] for monitoring concrete damage due to cracks. and corrosion. Then AE research was collaborated with Machine Learning which was used to detect crack types [20]. In further research, the process of monitoring crack types was also collaborated with image processing [21], one of which was the Unmanned Aerial Visual (UAV) method [22] and the creation of semi-automatic 3D maps on large structures [23], then processed using Network techniques. Convolutional Neural (CNN) [24]. Not only monitoring research on old structures, but real-time monitoring processes are also applied to the concrete curing process regarding strains and temperatures [25]. Of the various existing studies, most of them still focus on the laboratory scale, so the results obtained are sometimes less significant when applied in the field. Apart from that, most monitoring processes still use conventional methods, namely checking the location directly using tools, then the data is processed first to determine the type or level of damage, as well as appropriate repair methods. From these problems, of course research gaps are still found regarding the topic of monitoring cracks in concrete structures. So, further research is needed regarding the collaboration process between monitoring methods, data processing processes, and suggestions for appropriate types of improvements.
The research mapping process to find or analyze research gaps in a study can easily be carried out using bibliometric and scientometric methods. The bibliometric method can assist the process of analyzing scientific output related to document trends each year, type of document, country of origin, the most relevant publications, the most influential publications, and also the origin of the most productive documents publishing publications in the field of monitoring cracks in concrete structures. This method has been widely applied to analyze trends in various research fields such as health [26-29], environment and disaster [30-32], materials and energy [33-36], education [37-41], and processing engineering. data monitoring [42-45]. Apart from that, in more advanced analysis methods, bibliometric methods are often associated with scientometric methods which are able to analyze and map the evolution of knowledge from large datasets [26]. The scientometric analysis process is an interactive identical visualization of complex analytical data structures for statistics and interactive visual exploration [29, 30], with the help of one of the software tools, namely VOS Viewer. This software can be used as a visualization and mapping facility for research collaboration networks in various scientific literature [46, 47].
Therefore, this research aims to illustrate the visual relationship between publication connections, keywords, researchers, research countries, and study trends related to monitoring cracks in concrete structures using bibliometric [48] and scientometric [38] methods in the last decade (2013-2023). It is hoped that this research can provide a new perspective or research gap for further in-depth and collaborative research on equipment, data processing methods, and crack monitoring systems in concrete structures. So that the existence of a monitoring system for concrete structures can develop following technological trends and have more sophisticated capabilities and accurate results.
In this research, the mapping process will focus on monitoring cracks in concrete structures with the scope of research and research significance including:
3.1 Data mining method
Research data collection was carried out by extracting publication data from the Scopus database. The code entered in the Scopus database is (TITTLE-ABS-KEY (monitoring AND cracks AND on AND concrete AND structure)). With "TITLE-ABS-KEY" which means article title, abstract, and keywords which function as a document search sorter in Scopus [26]. Data searches were limited to publications from the last decade, namely between 2013-2023. The search subject area is also limited to the Engineering field only. Meanwhile, document type, language, country, and document source are not limited. Restrictions on certain time periods and engineering fields are intended to further specify research and adapt to current technological developments. From the results of these restrictions, search results of 1,191 documents were obtained for further analysis regarding publication trends and relationships between documents using bibliometric and scientometric analysis methods.
3.2 Bibliometrics analysis method
Bibliometric analysis is a combination of quantitative and qualitative methods that also adopt statistical and mathematical methods [28, 31]. The bibliometric method is one of the most widely used methods to determine the development of certain sciences with various research objects such as publication trends, authors, topics, and others [34, 35, 49]. In this research, the bibliometric analysis method is used to sort large collections of literature and also analyze research development trends in certain fields [32, 47, 49, 50]. In this step, the Scopus database is analyzed regarding the development of document trends each year, document type, country of origin, the most relevant publications, the most influential publications, and the origin of documents that are most productive in publishing publications in the field of monitoring cracks in concrete structures. The data analysis and visualization process use the Microsoft Excel application as a processing tool.
The process of analyzing document trends each year will provide an overview of the development and number of concrete crack monitoring publications in the last decade, as well as the potential for development in the number in the future. Analysis of the classification of document types will greatly help readers regarding the diversity of types of documents that are often published and the depth of the types of research. The territory network analysis serves to show which countries are currently intensively conducting research related to monitoring concrete cracks. The most relevant publication analysis functions to show search results from the Scopus database that best match the search keywords. The analysis of the most influential publications serves to show which publications are the basic reference for various other studies related to monitoring cracks in concrete. Meanwhile, the analysis of publishers shows which publishers have productivity and play a role in spreading the field of concrete crack monitoring. Apart from that, it can also be known that further research can be developed in the future.
3.3 Scientometric analysis method
The scientometric analysis method is a quantitative analysis method for understanding trends or patterns that emerge in the knowledge structure of a research field [29]. Scientometric analysis is able to measure how big the impact of research is from the results of the relationship between research subjects, and is able to map the evolution of knowledge from large-scale datasets [26]. The process of scientometric analysis is identical to the mapping of data from scientific literature to the interactive visualization of complex data structures for statistical analysis and interactive visual exploration [29, 30].
In this research, VOS viewer software was used to assist the scientometric analysis process [37, 51]. This software is widely used for quantitative analysis to produce results in a way that is well-organized, meaningful, and easy to understand [43]. VOS Viewer software can also be used as a visualization and mapping facility for research collaboration networks in various research fields to analyze the intellectual landscape of various scientific literature [33, 46, 47].
Scientometric analysis with Vos Viewer software this time will focus on: (1) co-word analysis; (2) co-author analysis; and (3) cluster analysis. Co-word analysis includes an explanation of identifying the frequency of occurrence of keywords and research topics related to monitoring concrete cracks, as well as research developments [40]. Co-Author's analysis includes a collaboration network between authors, so that the writing patterns and partnerships that underlie research related to concrete crack monitoring can be identified [52]. Meanwhile, clustering analysis includes interpretation of keyword networks as well as relationships between keywords related to various concrete crack monitoring research [27, 30]. Meanwhile, the analysis techniques and settings used in the Vos Viewer software are shown in Table 1. From this table, the keyword mapping results produced 292 keywords from the limitation of words appearing at least 10 times. This limitation aims to further specify the level of relationship between keywords in the documents being analyzed. Meanwhile, the author mapping results resulted in 169 authors from the limitation of the appearance of a minimum of 4 documents. This limitation is intended to filter documents published by authors who specialize in that field.
Table 1. VOS viewer limitation method
|
Assessment |
Consideration |
|
|
Keywords |
Authors |
|
|
Analysis type |
Co-occurrence |
Co-authorship |
|
Counting method |
Full counting |
|
|
Units of analysis |
All keywords |
All Authors |
|
Minimum occurrence |
10 times |
4 documents |
|
Result outcomes |
292 keywords |
169 authors |
Research and publications related to monitoring cracks in concrete structures have developed quite a lot from year to year. From the results of 1,191 documents found in the Scopus database search, several scientific mappings analyze can be described based on the number of documents per year, type of document, country of origin of the author, and occurrence of keywords. Co-authorship analysis can determine collaboration patterns between countries and institutions. In this section, the analysis aspects described are: (4.1) Annual publication trend analysis; (4.2) Territory network analysis; (4.3) Co-authorship analysis; (4.4) Most contributing documents and sources; (4.5) Document type distribution; and (4.6) Keyword clustering and hotspots.
4.1 Annual publication trend analysis
From the results of the Scopus database extraction, 1,191 documents were obtained for monitoring cracks in concrete in the Engineering sector in the period 2013-2023. Figure 1 shows the number of published documents indexed by Scopus. From the results of data processing, it can be seen that the number of publications in 2013 tended to be low and stagnant until 2020. However, the number of publications increased quite drastically when entered 2021, but the number of publications fell again in 2022. Meanwhile at the end of 2023 it was recorded that the number of publications related to monitoring cracks in concrete structures reached its highest point compared to previous years with175 documents.
From the analysis results, the trend of publications related to monitoring cracks in concrete structures has tended to increase in the last decade. This increase in the number of publications can be made possible by the increasing development of technology in monitoring building structures [53]. Moreover, concrete structures are the type of building that is most widely used throughout the world and susceptible to damage, even structural failure if they experience cracks [54].
Figure 1. Annual publication trend
From the results of data analysis on related keywords, several publications with the most relevance were obtained with all the keywords entered in the Scopus database search source as shown in Table 2. From the total data, there were 5 publications with the most relevance, with the publication title in first place, namely "Identification and monitoring of concrete cracks based on optical frequency domain reflectometry technique" by Wu et al. [55] with the number of citations being 5.
Table 2. The most relevance publications
|
No. |
Title |
Authors |
Year |
Citations |
|
1 |
Identification and monitoring of concrete cracks based on optical frequency domain reflectometry technique |
Wu et al. [55] |
2019 |
5 |
|
2 |
Experimental study on PPP-BOTDA-based monitoring approach of concrete structure crack |
Su et al. [56] |
2021 |
9 |
|
3 |
The monitoring method for crack damage of concrete structures based on piezoceramics transducers |
Zhang [57] |
2014 |
2 |
|
4 |
Research on performance of smart concrete materials and self-monitoring of cracks in beam members |
Li et al. [58] |
2023 |
1 |
|
5 |
Monitoring methods of crack behavior in hydraulic concrete structure based on crack mouth opening displacement (CMOD) |
Huo et al. [59] |
2014 |
1 |
Meanwhile, from the 5 publications with the most relevance in Table 2, it can be seen that the publication entitled "Experimental study on PPP-BOTDA-based monitoring approach of concrete structure crack" by Su et al. [57] obtained the highest citations, namely 9 citations. From Table 2, the relevance of publications with the keywords entered is more directed at methods and equipment that are often used in monitoring cracks in concrete structures.
If the most relevant publications with keywords that have a span of more than 10 years refer to equipment and research methods for monitoring concrete cracks. So, in Table 3 which shows the latest publications, which were published in 2023, are more directed towards the integration of real-time monitoring systems by collaborating using the development of digital Convolutional Neural Network (CNN) technology. This is reflected in the first ranking of the latest publication entitled "Analysis of Geometric Characteristics of Cracks and Delamination in Aerated Concrete Products Using Convolutional Neural Networks" by Razveeva et al. [60].
Table 3. The newest publication
|
No. |
Title |
Authors |
|
1 |
Analysis of Geometric Characteristics of Cracks and Delamination in Aerated Concrete Products Using Convolutional Neural Networks |
Razveeva et al. [60] |
|
2 |
Image-based Concrete Cracks Identification under Complex Background with Lightweight Convolutional Neural Network |
Meng et al. [24] |
|
3 |
Damage localization using acoustic emission sensors via convolutional neural network and continuous wavelet transform |
Vy et al. [61] |
|
4 |
Structural assessment of the pedestrian bridge accessing Civita di Bagnoregio, Italy |
Buffarini et al. [62] |
|
5 |
In-situ nonlinear ultrasonic technique for monitoring damage in ultra-high performance fibre reinforced concrete (UHPFRC) during direct tensile test |
Yin et al. [63] |
4.2 Territory network analysis
In the last decade, China has been the country that has published the most research in the field of monitoring cracks in concrete structures. Based in Figure 2, the distribution of the number of research publication documents for the top 15 countries in related fields, China contributed the largest number of publications with 330 documents. Then followed by the USA and India with 184 and 78 documents respectively, as the largest contributors of documents in related fields. Meanwhile, Germany, Canada, UK, Australia, Italy, Japan, Belgium, France, Spain, South Korea, Malaysia, and Poland are also developing research in the field of monitoring cracks in concrete structures. Meanwhile, Figure 3 shows the distribution network and linkages between countries in the development of knowledge related to monitoring cracks in concrete structures developed by 84 countries. From this figure the strongest cross-country author collaboration relationships are owned by China (17 links) and the USA (29 links) which is also reflected in the number of documents produced by these two countries. China and the USA also collaborate with many other countries, so they have wide and large network coverage. Meanwhile, the United Kingdom (UK) (27 links) which contributed 54 documents, has a higher level of international collaboration than India (12 links) which is ranked 3rd in contributing the most documents. However, from the visualization results in Figure 3, there are still countries that are not yet connected in international collaboration but have started to develop research in related fields.
So, from the results of this analysis and visualization of countries that have participated in the development of research on monitoring cracks in concrete structures, there are still large opportunities to establish international research collaborations and exchange new ideas between countries on a broader scale. Thus, research related to monitoring cracks in concrete structures can be more developed and accurate.
Figure 2. Territory participant
Figure 3. Country distribution and network
4.3 Co-authorship analysis
The results of the analysis of the authorship network related to monitoring cracks in concrete structures in the last decade are shown in Figure 4. From the visualization results of the authorship mapping, the names of the authors are dominated by Chinese people in the red cluster. This is of course in line with the existence of the largest number of publications coming from China. However, the distribution of the network from the red cluster which is dominated by Chinese people has not yet widely reached all clusters, because collaboration between researchers is still limited to national collaboration or in the same country, as is also the case in other clusters.
Figure 4. Co-authorship network
Meanwhile, Figure 5 shows a visualization of the affiliate network or organization from the author mapping results. From the visualization results, the network linkages between affiliates are still not evenly connected. Only few affiliates are connected and form their own cluster. This shows that the level of collaboration between researchers is still relatively low, because it is possible that researchers have still collaborated with the same researchers in recent years. So, from the results of this visualization, it is hoped that in future research, researchers can further expand international connections and collaboration in related research fields which can encourage the development of concrete structure crack monitoring system technology to become more sophisticated and accurate.
Figure 5. Affiliation distribution and network
4.4 Most contributing documents and sources
Based on the results of analysis of 1,191 documents in the field of monitoring cracks in concrete structures, there are the top 10 most productive journals in the last decade related to this field. It can be seen in Figure 6 that the distribution of documents from the top 10 journals is, Construction and Building Materials (52 documents), Proceedings of SPIE (50 documents), Lecture Notes in Civil Engineering (47 documents), Engineering Structures (37 documents), Sensors Switzerland (36 documents), Applied Mechanics and Materials (31 documents), Structural Health Monitoring (31 documents), Advanced Materials Research (28 documents), Sensors (20 documents), and Smart Materials and Structures (18 documents). Of the top 10 journals, the “Construction and Building Materials” journal occupies the highest position as the document source that contributes most to the research field of crack monitoring in concrete structures. Moreover, the “Construction and Building Materials” journal is one of the well-known journals with the publisher Elsevier which is the owner of the Scopus database.
However, the results of the analysis of the highest number of citations, the first place with 687 citations was obtained by Dung and Anh [64] with the publication title "Autonomous concrete crack detection using deep fully convolutional neural network" from the journal "Automation in Construction". Even though the highest number of publications was obtained by the journal "Construction and Building Materials", one of its publications is still in third place with a total of 439 citations and the publication title "Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete” by Dorafshan et al. [65].
From the results of the analysis in Table 4, the trend in research development in the field of monitoring cracks in concrete structures is more towards developing damage detection, especially cracks in real time and automatically. Apart from that, the use of sensors and Unmanned Aerial Vehicles (UAV) combined with deep learning systems has also been increasingly developed in recent years, considering the increasing development of the digital world and the enormous number of citations in related research.
Figure 6. Most contributing document sources
Table 4. The most impactful document
|
No. |
Title |
Authors |
Source |
Year |
Citations |
|
1 |
Autonomous concrete crack detection using deep fully convolutional neural network |
Dung and Anh [64] |
Automation in Construction |
2019 |
687 |
|
2 |
Intrinsic self-sensing concrete and structures: A review |
Han et al. [66] |
Journal of the International Measurement Confederation |
2015 |
513 |
|
3 |
Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete |
Dorafshan et al. [65] |
Construction and Building Materials |
2018 |
439 |
|
4 |
Automated crack detection on concrete bridges |
Prasanna et al. [67] |
IEEE Transactions on Automation Science and Engineering |
2016 |
320 |
|
5 |
Investigations on scalable fabrication procedures for self-sensing carbon nanotube cement-matrix composites for SHM applications |
D'Alessandro et al. [68] |
Cement and Concrete Composites |
2016 |
256 |
4.5 Document type distribution
Of all the types of documents contained in the Scopus database related to monitoring cracks in concrete structures, there are 5 types of documents that discuss related topics. Document types are divided into articles, conference papers, conference reviews, book chapters, and reviews. It can be seen in Figure 7 that the largest distribution is in the article document type which occupies 60% or 711 documents. Furthermore, the conference paper document type occupies 29% or 344 documents, conference review accounts for 6% or 71 documents, book chapters account for 4% or 44 documents, and reviews account for 1% or 18 documents.
From the various types of documents, it can be seen that articles occupy the highest distribution of document types, this is possible because most of the research related to monitoring cracks in concrete structures is the result of field tests using tools and then the data is processed, thereby obtaining a conclusion regarding the condition of the structure being studied. Likewise, in the conference paper document type, it is possible for research to focus more on the results of field studies using new methods or real case studies that produce conclusions about the condition of a structure.
Meanwhile, conference review, book chapter and review document types are more likely to discuss theory, elaboration of new formulas, or even a summary of developments in methods and technology for monitoring cracks in concrete structures. Thus, the research trend related to monitoring cracks in concrete structures is dominated by the type of research articles that are more detailed regarding the actual results of experiments and assessments in the field.
Figure 7. Document type distribution
4.6 Keyword clustering and hotspots
In various research fields, the main essence of each discussion is illustrated by the keywords used. Figure 8 shows a visualization of the distribution and connections between keywords in research in the field of monitoring cracks in concrete structures. From 1,191 documents analyzed using VOS Viewer software, overall visualization results were obtained regarding keywords that often appear or are used by researchers. Various dots have different sizes and colors. The larger the dot size, the greater the frequency of appearance of the word in each document. Meanwhile, the difference in color at each point better describes the division of research types based on clusters formed from the VOS viewer software.
It can be seen in Figure 8 that several words have a point size that is larger than other points, such as cracks, reinforced concrete, concrete beams and girders, structural health monitoring, concretes, crack detection, acoustic emissions, damage detection, prestressed concrete, and corrosion. All of these keywords form a cluster and network that is interconnected with each other. There are 7 clusters with 7 different colors which are described in points 4.5.1 to 4.5.7 below.
4.6.1 Cluster 1 (red cluster)
Based on the visualization in Figure 8, the first cluster is marked in red and the keyword “cracks “is a word that is often used in research on monitoring cracks in concrete structures. The keyword "cracks" is the main keyword in this field of research because the focus of this research is on cracks in the concrete structure that will be studied. The keyword "cracks" has a frequency of appearance of 404 times with connections to other words of 259 links. The keyword "cracks" also has a connection with big keywords from each cluster such as "reinforced concrete", "structural health monitoring", "concretes", "damage detection", "prestressed concrete", "acoustic emissions", and "corrosion".
From the results of the analysis of the mapping in Figure 8, the results obtained are that cluster 1 or cluster red which has the big keyword "Cracks", better describes the mechanical properties of the structure studied. This relationship is illustrated between the keyword "Cracks" and keywords that indicate factors in the occurrence of a crack such as "ductility", "deformation", "fracture", "fatigue", "failure", "tensile", and others. So, red cluster or cluster 1 describes the mechanical properties factors that cause cracks in concrete structures.
4.6.2 Cluster 2 (green cluster)
Figure 8 shows the clustering network for the main keyword reinforced concrete which is marked in green. The second cluster is dominated by the keyword "reinforced concrete" with a total of 343 occurrences and 253 links, followed by the keyword "concrete beams and girders" with 200 occurrences and 239 links. This cluster is also connected to other large clusters such as the keywords "cracks", "structural health monitoring", and others.
The results of the analysis of the green cluster in Figure 8 show that this cluster is more directed towards the use of fiber as an additional ingredient in concrete mixtures which is known to increase the ability of concrete to withstand loads and help reduce damage to concrete, especially cracks. In this cluster, it can also be seen that the crack monitoring tool that is often used in concrete structures is in the form of a sensor or fiber optic sensor. Meanwhile, the main keyword, namely "reinforced concrete", shows that most of the research focuses on reinforced concrete structures which are the type of structure most widely used in this era. So, in this cluster, the type of keyword that is highlighted is the use of additional materials in the form of fiber to increase the ability of concrete to resist damage, one of which is cracking.
4.6.3 Cluster 3 (blue cluster)
Figure 8 shows the clustering network marked in blue. The third cluster is dominated by the keywords "Structural health monitoring", "Concretes", "Concrete construction", "Concrete building", and "Crack detection". In this cluster, there is a strong connection between keywords, so that the number of word occurrences and total links for several large keywords in this cluster are not too different. Table 5 shows the number of keywords with the highest occurrences and links in this cluster.
Table 5. Blue cluster links and occurrences
|
Keyword |
Occurrences |
Links |
|
Structural health monitoring |
456 |
257 |
|
Concretes |
434 |
258 |
|
Concrete construction |
272 |
258 |
|
Concrete building |
202 |
243 |
|
Crack detection |
213 |
235 |
From the results of the analysis of the blue cluster in Figure 8, it shows that the keyword "structural health monitoring" is the main keyword in this cluster. In this cluster, the relationship between keywords is more directed at processing and data retrieval methods for detecting damage to concrete structures. Data collection methods for damage detection include "visual inspection" and "Unmanned Aerial Vehicle (UAV)". Then the data processing method resulting from structural monitoring can use machine learning, deep learning, automation, deep neural networks, and image processing. So, in this cluster it can be seen that the type of keyword that will be highlighted is the method of collecting and processing data for detecting damage to concrete structures.
Figure 8. Visualization of keyword network and distribution
4.6.4 Cluster 4 (yellow cluster)
The 4th cluster is marked in yellow as shown in Figure 8. This cluster shows the dominance of the keyword by the word "Acoustic emission" which is a type of Non-Destructive Test (NDT) that has been very popular in the last few years until now. The keyword "acoustic emissions" has a frequency of 123 occurrences and a total of 189 links. Meanwhile, the keyword "Acoustic emissions testing" has 146 occurrences and 204 links.
In this cluster, the relationship between keywords refers more to the method that is often used in crack testing of concrete structures, namely the use of acoustic emission. Acoustic emissions will be closely related to research that leads to the detection of damage to structures, especially reinforced concrete, and also corrosion. Thus, this cluster emphasizes the acoustic emission method as one of the popular methods used to detect damage to concrete structures.
4.6.5 Cluster 5 (purple cluster)
Based on Figure 8, this 5th cluster is shown in purple which is dominated by the words "Damage detection" and "Nondestructive examinations". With the frequency of appearance of the keyword "damage detection" 240 occurrences and 247 links. Meanwhile, the keyword, "Nondestructive examination" has 90 occurrences and 191 links. Where the keyword "damage detection" is very closely related to the keyword "structural health monitoring" (cluster 2) which refers to methods for detecting and processing data on damage to concrete building structures. Meanwhile, the keyword "Nondestructive examination" is more closely related to the keyword "Cracks" (cluster 1), which means the purpose of non-destructive testing is to check for damage, especially cracks in building structures.
The results of the analysis of the internal relationships between keywords in the purple cluster are as shown in Figure 8, which tends to lead to the type of damage detection tool for concrete structures. This type of detection tool refers more to Non-Destructive Test (NDT) equipment such as the use of ultrasonic waves, piezoelectricity, transducers, elastic waves, and coda waves. So, in this cluster, it can be seen that the keyword highlighted is the type of damage detection tool in concrete structures.
4.6.6 Cluster 6 (ocean blue cluster)
The color distribution in the 6th cluster is shown in ocean blue as shown in Figure 8. The most dominant keyword is the word "Prestressed concrete" with 62 occurrences and 158 links. The keyword "prestressed concrete" has a close relationship with the keyword "concrete beams and girders" (cluster 2). This is possible because most concrete beam and girder construction uses prestressed concrete structures.
Apart from that, the internal relationship between keywords in cluster 6 focuses more on the properties and behaviour of prestressed concrete structures such as stiffness, prestressing, vibration and post-tension concrete. The application of prestressed concrete is closely related to the structure of concrete bridges, also composite beams and girders. So, in this cluster the keywords are more prominent in field structures which are often used as research objects in monitoring cracks or damage to concrete structures.
4.6.7 Cluster 7 (orange cluster)
In the 7th cluster, the grouping is shown in orange as can be seen in Figure 8. The most dominant keyword is the word "Corrosion" with 86 occurrences and 169 links. This keyword is very closely related to the keywords "Reinforced concrete" (cluster 2), "concretes" (cluster 3), and "cracks" (cluster 1). This is possible because the main factor of corrosion in reinforced concrete is caused by cracks which can accelerate the rate of corrosion in concrete reinforcement.
Apart from that, the internal relationship in this cluster also shows that corrosion has several other factors that cause damage to reinforced concrete to become more severe, namely the presence of chloride diffusion which can increase the corrosion rate of reinforced concrete structures. In this cluster, there is also the keyword "repair" which indicates that research related to monitoring cracks in concrete structures does not only focus on structural behaviour, detection tools, and processing methods but can also be linked to further treatment of structural damage that may have occurred. So, in this cluster, the keyword is more prominent in one type of damage that arises due to cracks in reinforced concrete structures, namely corrosion.
Various methods for monitoring concrete cracks have developed greatly in the last decade. It can be seen from the related research trend that it is towards increasing every year. In its development, the process of monitoring concrete cracks has been widely applied both on a laboratory and field scale. So, the limitations, potential applications and further research related to monitoring cracks in concrete can be summarized as follows:
5.1 Non-destructive test method
Various methods for monitoring concrete cracks initially used visual assessment methods, which were later developed using Non-Destructive Test (NDT) methods. The NDT method has been widely researched and used in the process of monitoring concrete cracks [69]. These various studies include NDT using Acoustic Emission (AE) [70-75], ultrasonic wave [63, 76, 77], electromechanical impedance [19], Impact Echo (IE) [78], Ground Penetrating Radar (GPR) [79], and others. The monitoring method used is not limited to normal concrete, but also begins to research the characteristics of geopolymer concrete, the influence of environmental conditions, corrosion and self-healing concrete. Apart from that, in the monitoring process on a larger scale, monitoring processes have begun to be developed using Unmanned Aerial Vehicle (UAV) or drone technology [21-23, 80]. So the monitoring process can be carried out on a large scale with image-based output. However, the monitoring process still requires scheduled maintenance which is less efficient in time and energy. So there is still a need for integration with other technologies to improve the automation system and the accuracy of monitoring concrete cracks.
5.2 Automation and efficiency of concrete crack monitoring
Apart from the development of conventional NDT methods, the use of sensors as a concrete monitoring development is also starting to be widely developed [81]. Various monitoring of concrete cracks such as the use of fiber optic sensors [44, 82, 83], piezosensor [84], and wireless sensors [85, 86]. Apart from that, the use of sensors in the process of monitoring concrete cracks also supports the real-time detection process, so that damage to concrete can be immediately detected. Apart from that, there are technological developments such as the Internet of Things (IoT) [45] and Artificial Intelligence (AI) such as Machine Learning [87, 20], Deep Learning (DL) [80, 88], Arificial Neural Network (ANN) [42], and Convolutional Neural Network (CNN) [53] can be integrated into a concrete monitoring system to process input data from the field. This integration process is also intended to improve the damage detection automation process, data processing efficiency, and increase detection accuracy.
5.3 Automatic labeling and repair optimization
Currently, the process of monitoring damage to concrete is still done manually, so it takes a lot of time and energy. Meanwhile, automatic crack monitoring and analysis processes are still rarely implemented. Therefore, in the future there is a need to develop technology related to automatic detection processes accompanied by automatic labeling processes for various types of concrete cracks based on AI. The automatic labeling process will be very useful in making decisions about the type of repair for concrete damage. In further research, database technology can also be developed that can provide recommendations for repairing concrete damage automatically from the results of processing types of concrete damage labeling. However, this technology still needs to be studied by relating various factors, such as environmental conditions, weather, structural materials, ease of access to repairs, level of damage, time, repair costs, and various other factors that greatly influence the strength of the structure.
5.4 Central database monitoring detection
In the development of advanced research, when all damage monitoring components have been tested in the laboratory and field, a central database can be created regarding various types of cracks and suggestions for appropriate repairs. This database will be very useful for the process of monitoring infrastructure that is considered vital or important, thereby minimizing excessive damage to infrastructure. However, the creation of this database still requires in-depth study and collaboration of various components both across fields and countries. This is also related to the need for input of large amounts of data with various environmental conditions and effects that can have fatal consequences on infrastructure. Apart from that, various track records of previous damage can be used as a reference for repairing other infrastructure that has experienced similar types of damage.
This research presents a comprehensive bibliometric and scientometric analysis of the development of "Monitoring Cracks in Concrete Structures" published by Scopus over the last decade (2013-2023). Research includes analysis of document trends each year, keyword networks, author networks, affiliate networks, country networks, sources, types of documents, the most impactful documents, and mapping of emerging technologies. The scientometric method with the help of VOS viewer software is applied to analyze and visualize the relationship between keywords, authors, countries, and institutions/organizations affiliated with the author. From the 1,191 documents analyzed, the following conclusions can be drawn:
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