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Review

Smart Asphalt Mixtures: A Bibliometric Analysis of the Research Trends

by
Iran Gomes da Rocha Segundo
1,2,*,
Élida Melo Margalho
1,2,
Orlando de Sousa Lima, Jr.
1,2,
Claver Giovanni da Silveira Pinheiro
2,
Elisabete Fraga de Freitas
1 and
Joaquim Alexandre S. A. Oliveira Carneiro
2,*
1
Department of Civil Engineering, Institute for Sustainability and Innovation in Structural Engineering, ARISE, University of Minho, Guimarães 4800-058, Portugal
2
Centre of Physics of Minho and Porto Universities (CF-UM-UP), University of Minho, Azurém Campus, Guimarães 4800-058, Portugal
*
Authors to whom correspondence should be addressed.
Coatings 2023, 13(8), 1396; https://doi.org/10.3390/coatings13081396
Submission received: 1 July 2023 / Revised: 21 July 2023 / Accepted: 2 August 2023 / Published: 8 August 2023
(This article belongs to the Section Surface Characterization, Deposition and Modification)
Browse Figures
Versions Notes

Abstract

:
A smart asphalt mixture holds new capabilities different from the original ones or can react to a stimulus. These capabilities can be categorized based on smartness or function: smartness, mechanical, electrical, optical, energy harvesting, electromagnetic wave/radiation shielding/absorbing, and water related. The most important capabilities applied to asphalt mixtures are the photocatalytic, self-cleaning, self-healing, superhydrophobic, thermochromic, deicing/anti-icing, and latent heat thermal energy storage abilities. This research deals with a bibliometric review of the peer-reviewed journal articles published on the Scopus database, with the strings of terms related to these capabilities and asphalt or bitum in their titles, abstracts, and keywords. The review analysis highlighted the increasing number of accumulated publications, confirming the relevance of this research topic in recent years. The capability most often referred to was self-healing. The study showed that China was the most productive country. Research articles were mostly published in the journal Construction and Building Materials. Several techniques and methods are being developed regarding smart asphalt mixtures; for that reason, this research work aims to evaluate the literature under a bibliometric analysis.

1. Introduction

An asphalt pavement must withstand the high stresses induced by traffic and weather, as well as guarantee safe and comfortable rolling conditions with low cost and a low impact on the environment [1,2,3]. In recent years, the application of new capabilities has become an important subject in Materials Science. More recently, the Civil Engineering Area, particularly Transportation Engineering, has been exploring new capacities to be applied to asphalt pavement [1,4,5].
It is relevant to mention that most road pavements are built with material from the distillation of petroleum crude oils, the asphalt binder. Given that road pavements are close to people and cover a large area, accounting for more than 50% of the urban area comprising roads and parking lots, researchers in Transportation Engineering have started functionalizing asphalt mixtures. This approach provides them with new capabilities, consequently offering several social, environmental, and financial benefits [6,7,8,9].
The incorporation of new functionalities into asphalt mixtures offers an opportunity to enhance road safety. Snow and ice in winter are known to cause many traffic accidents due to the reduced friction between tires and pavement [10,11,12]. Current practices to mitigate this problem rely on deicing agents [13], and conductive materials in asphalt mixtures [12,14]. The first one can melt the ice and snow through a chemical process [15,16], while the second one should be associated with a microwave or an induction-heating machine to melt the ice/snow [17,18]. Superhydrophobic asphalt mixtures can also mitigate this problem [19,20]. By coating the asphalt surface with nano/microparticles, water droplets are repelled, and the ice/snow formation is avoided [20,21,22]. This approach provides safer roads during rain and low temperatures [20,23]. Moreover, the surface coating promotes an additional capability known as self-cleaning, which contributes to the removal of dirt particles over the asphalt surface. Self-cleaning is achieved on three types of surfaces: (i) superhydrophobic: water droplets roll on the surfaces and carry the dirt previously deposited; this phenomenon is known as the lotus flower effect [24]; (ii) superhydrophilic: the water droplets spread on the surface, making it more easily washed by the removal of the adsorbed dirt on its surface during rainy periods; and (iii) photocatalytic effect because the photocatalytic materials can degrade organic pollutants (such as oils and greases) [25,26]. In this way, the self-cleaning property can contribute to the reduction of accidents in oil-spilled areas and dusty areas [1].
In addition to road safety, smart asphalt mixtures promise to mitigate air pollution and reduce the urban heat island (UHI) effect. Air pollution is one of the main environmental problems of large urban centers [27,28,29], and the need for environmentally friendly solutions to address the issue has become increasingly evident. This awareness is reflected by the prevalence of climate change conferences worldwide and substantial media coverage highlighting the effects and repercussions on human health. Among the various sources of air pollution, road traffic is one of the most important contributors that make vehicles [30,31] and roads themselves targets to reduce pollution. In the context of road surfaces, asphalt mixtures, when associated with photocatalysts, can promote air purification by photooxidation of contaminants [1,32,33,34]. Photocatalysts, semiconductor oxides such as TiO2 and ZnO, can oxidize pollutants, including, for instance, NOx and SO2, and degrade organic compounds in the presence of ultraviolet (UV) light and humidity [1,22,33]. To mitigate air pollution levels, researchers are concerned about the application of photocatalytic capability on asphalt mixtures by coating with semiconductor nano/microparticles [6,35]. In the literature, some strategies can be highlighted when it comes to the immobilization of particles. These particles can be fixed over the asphalt surface by applying a stick material, such as resins [36] or even cement [37] before the application of the particle solution. The durability and by-products of this technology are still a gap in the literature and need to be studied.
UHI refers to the phenomenon where an urban is significantly warmer than its adjacent rural areas due to human actions. To minimize this issue, using thermochromic asphalt mixtures offers not only greater mechanical strength and increased resistance to aging for the pavement but also provides the capability to reduce heat absorption by changing their color [38,39,40]. In this sense, it is possible to impart better properties to the material since the black color of conventional asphalt roads absorbs a high percentage of sunlight, increasing its surface temperature and contributing to the warmer conditions in urban areas [39,41].
Another strategy to mitigate the UHI is developing Latent Heat Thermal Energy Storage (LHTS) capability using phase change materials (PCM) [42,43,44,45]. Reducing the magnitude of temperature fluctuations also benefits the mechanical properties of the asphalt mixtures, preventing rutting, and avoiding thermal cracks [1,45]. In general, cracking is one of the main pavement distresses, and it can be reduced by developing a self-healing capability, which acts in the microcracking phase [7,46,47]. Asphalt materials heal their own cracking during rest periods; however, this effect is very limited and is not enough to mitigate the damage processes. Nowadays, researchers are developing innovative methods to improve and optimize this capability by adding conductive materials, nanoparticles, ionomers, and microcapsules containing high content of maltenes [1,47].
In the last 10 years, the number of published scientific articles on the development of new capabilities to asphalt mixtures, i.e., smart asphalt, has been sharply increasing. The main capabilities applied to asphalt mixtures are photocatalytic, self-cleaning, self-healing, superhydrophobic, thermochromic, deicing/anti-icing, and latent heat thermal energy storage [1,6,12,47,48].
However, review works employing a bibliometric approach on asphalt materials are scarce. Within this field, there are only some studies that can be observed that explore trends in different materials, technologies, and processes. These include mapping of publications on asphalt pavement and asphalt bitumen materials [49], research trends in pavement management [50], recent advancements in permeable pavement systems [51], and studies related to pavement distresses [52,53]. Some of them are concerned with smart asphalt mixtures, but only related to one single functionality, including, for example, Abejón [54], who investigated self-healing. The analysis of different bibliometric indicators can evaluate the productivity and quality of publications related to applying new capabilities in asphalt mixture.
Thus, the main objective of this article is to examine the available information in the scientific literature on smart asphalt mixtures based on bibliometric analysis. The articles found were inspected and evaluated according to several classifications (leading countries and institutions, main scientific journals, annual results, among others) to determine the measurable characteristics of smart asphalt worldwide and find the most relevant opportunities. The main novelty related to this work is the fact that it is one of the few (as far as the authors are aware) studies available in the literature on smart asphalt mixtures using a bibliometric approach.
The bibliometric analysis of smart asphalt mixture provides a comprehensive overview of scientific production, helping to identify trends, map knowledge, assess research quality, analyze collaboration networks, and identify relevant sources of information. This paper will be valuable for researchers when planning their own research, identifying collaboration opportunities, and understanding the scientific landscape in their field of study.

2. Bibliometric Analysis

Bibliometric analysis provides a systematic review method, using a bibliometric approach, developed through a database survey of already elaborated and published scientific articles. The technique investigates the formal properties of the knowledge field by utilizing mathematical and statistical methods [55] and has been adopted to scrutinize trends and different topics across science research domains [56].
Bibliometric studies and quantifications of co-citation and co-work networks can serve as a powerful approach for identifying thematic areas, research field clusters and main researchers, being helpful to visualize both current and emerging themes [57]. This tool can also explore the knowledge on a given topic and enhance a literature review approach [58,59].
Figure 1 shows the step-by-step procedure for conducting a bibliometric analysis. Initially, during the research planning phase, research questions and expected outcomes are defined. Subsequently, proper search strings are created based on research keywords, logical operators, and the study’s objective. Thereafter, during the retrieval of the articles from the databases, some filters can be applied according to the purposes of the study. Among the filters are document type, language, journal, category, country, and data range, among others. Some examples of relevant databases are Scopus, Web of Science, and Google Scholar, among others, and at least one of them must be selected. The choice of the retrieved articles that will comprise the bibliometric analysis is carried out by reading the abstracts. From the abstract reading, articles that are not related or that do not answer the research questions should be excluded from the study. In addition, the bibliometric information contained in the documents that will be part of the bibliometric analysis should be carefully checked and treated if necessary. The prior treatment before use consists of cleaning (removing redundant information, formatting errors, unwanted special characters, or any other noise) and standardizing the data used in the respective networks to ensure that inaccurate information is not inferred [58,59].
To assess the indicators and analyze the data, at least one software tool is chosen, such as CiteSpace, HistCite, Excel, BibExcel, and VOSviewer. These tools use statistical and mathematical methods for the analysis. The selection of the network approach generated from the software and the bibliometric information analyzed should be in accordance with the research questions and, consequently, the study’s objective. For instance, keywords such as co-occurrence, co-citation, co-authorship, bibliographic coupling, number of citations per publication, H-index, highly cited articles, and Impact Factor (IF) can be analyzed.
Concerning the network visualization obtained by using VOSviewer software, each color represents a cluster, that is built by following the co-occurrence matrix methodology. The network map construction consists of three steps. In the first step, it is calculated a similarity matrix based on the co-occurrence matrix. In the second step, a map is constructed by applying the VOS mapping technique to the similarity matrix. In the third step, the network map is translated, rotated, and reflected. The items in the network map are represented by nodes (related to objects such as co-authorships or co-occurrences) and edges (pertaining to the link or relationship between nodes). The distance between two nodes represents the estimated relationship between search terms; thus, short distances indicate a high number of co-occurrences. The size of the node label signifies the weight (frequency) of an item within a network; hence, large labels correspond to high frequencies of co-occurrences. Nodes are grouped into clusters based on their similarities and mutually strong mutual correlations [60,61,62].
The Impact Factor (IF) is derived from the Journal Citation Reports (JCR) and serves as a broadly recognized metric for assessing journals worldwide. It considers not just the journals’ display and practicality but also the academic level and quality of the articles within the journal. The Journal Citation Reports from Clarivate, depending on citations and publications, calculates the IF using Equation (1) [63].
IF y = Citations   in   y   to   items   published   in   y − 2 + y − 1 Number   of   citable   items   in   y − 2 + y − 1
where y is the year.
For example, the prestigious and well-known journal Nature presents an IF2021 of 69.504. It is calculated from citations in 2021 to items published in 2019 (54,341) + 2020 (82,165) divided by the number of citable items in 2019 (905) + 2020 (1059), resulting in 136,506/1964 and consequently 69.504 [64].
While the Impact Factor is a comparative metric, the h-index is a combined quantitative measure that evaluates both the productivity and quality of a researcher’s academic publications by the quantity and the citation impact of the author’s publications [65]. Scientific journals, countries, research groups, and departments also present their own H-index. The H-index reflects the maximum number of articles a researcher has published with at least N citations. N represents the number of articles that have received at least N citations. For instance, according to Scopus, Professor Albert Einstein has 132 documents and more than 30,000 citations, resulting in an H-index of 43. This value is due to 43 documents with at least 43 citations each.
The Cite Score (CS) is a metric established by Elsevier that gauges the average number of citations received per document within a specific journal or research field. It serves as an indicator of a journal’s impact and the significance of the research it publishes. The Cite Score is calculated by taking the number of citations received by peer-reviewed documents, such as articles, reviews, conference articles, data articles, and book chapters, published over a four-year period, and dividing it by the number of these types of documents published during the same time frame (Equation (2)). Regarding the journal Nature, its CS2021 is the result of 338,611 citations (2018–2021) divided by 4823 documents (2018–2021), which is 70.2 [66].
CS y = Citations   y − 4   to   y Documents   y − 4   to   y
where y is the year.

3. Methodology

The bibliometric analysis relied on the records obtained from Scopus, one of the most important, extensive, multidisciplinary, and international bibliographic databases. An initial attempt to generate a database using the string “smart” or “intelligent” or “functionalized” and “asphalt” or “bitum*” was unsuccessful. The search yielded various references in Transportation Engineering, including improved asphalt binder modification, advanced compactions using Industry 4.0 knowledge, sustainable pavement management, improved decision making, cost-benefit analysis, and environmental impacts. Additionally, this search provided general results regarding recycled materials, the mechanical performance of asphalt mixtures, and others. In summary, the results of this first search were not aligned with this research’s scope. These results indicate that the terms used in the field, especially ‘smart’, are still undergoing a consolidation phase and may carry diverse meanings depending on the context and specific studies.
A smart material is an intelligent material with new capabilities and, different from the original one, can react upon a stimulus, e.g., temperature and stress. These materials became smart through a functionalization process. The initial attempt to generate a database did not provide the specific results of the different smartness applied to asphalt mixtures, which is the objective of this work. The strings used were the following: (i) photocatalysis: “photocat* and asphalt or bitum*”; (ii) “superhydropho* and asphalt or bitum*”; (iii) self-cleaning: “self-clean* and asphalt or bitum*”; (iv) deicing/anti-icing: “deic* or anti-ic* and asphalt or bitum*”; (v) self-healing: “self-heal* and asphalt or bitum*”; (vi) thermochromic: “thermochrom* and asphalt or bitum*”; and (vii) LHTS: “PCM or phase change or latent heat thermal storage or thermal energy storage or LHTS and asphalt or bitum*”. The results were compiled to generate the global database of this research. The strings collected for each capacity are in accordance with Rocha Segundo et al. [1].
Results were limited to articles or reviews written in English to reduce duplicate publications and minimize false positive results. The search, conducted on 21 January 2022, includes occurrence at the title, abstract or keywords in the range of all years including this date. Initially, 854 references were provided by this search. After reading and assessing each abstract and removing the duplicate documents, the final database comprised 626 documents regarding the different smartness applied to asphalt mixtures without date range. Using VOSviewer software, a visual representation of the data network referring to the clusters was carried out and links were generated between the different parameters (co-authorship, co-occurrence, citation, bibliographic coupling, or co-citation links, among others) to proceed with the analysis of the following research questions:
  • How relevant is each capability in the current literature?
  • Which countries and institutions are most concerned about the functionalization of asphalt mixtures?
  • Who are the most productive authors and co-authors in this research field? In what specific subject/capability?
  • What are the scientific journals publishing the most on smart asphalt mixtures?
  • How have publications on smart mixtures evolved?
  • What is the link between scientific research groups?
  • What are the main knowledge gaps and opportunities related to smart asphalt mixtures?
  • What are the most relevant terms and clusters identified in the co-occurrence network of this research scope?
Thus, the literature was evaluated concerning the most frequent terms and keywords for smart asphalt mixtures, as well as the evolution of each capability and the contribution of each one to the literature when compared to the database were studied, presenting the most innovative and developed capabilities. Statistics of the journals were analyzed, especially the top most productive journals. Analysis of the parameters related to the quality of the journal (IF and H-index) and quantitative indicators (number of articles and citations) were discussed. The most productive and cited authors were assessed. Finally, the most cited articles were also evaluated.

4. Results and Discussion

4.1. Most Frequent Terms of the Smart Asphalt Mixtures

The relationship between the index keywords of each considered document and their co-occurrence frequency is presented in Figure 2. The most relevant terms were repeated at least fifteen times (without duplicate or similar terms, for example, rheological properties and rheology or pavement and pavements). The results were organized into four main clusters. In the first one, the most frequent terms are “temperature”, “bituminous materials”, “phase change materials”, “scanning electron microscopy”, “microstructure”, and “Fourier Transform Infrared Spectroscopy”. The second presents as main terms “asphalt mixtures”, “asphalt pavements”, “pavements”, “titanium dioxide”, “snow and ice removal”, and “efficiency”. The third cluster enclosed the terms “mixtures”, “asphalt concrete”, “concretes”, “induction heating”, and “microwave heating”. The fourth comprised the terms: “asphalt”, “self-healing”, “self-healing materials”, “binders”, and “cracks”. The most central terms are the basic ones for asphalt researchers, such as “asphalt”, “mixtures”, “asphalt pavements”, and “temperature”, which present high relevance for all clusters. In general, it can be observed that there are some capabilities treated in combination or some that present similarities.
It is important to clarify the terminology used in the field of asphalt research due that involves terms such as “bitumen”, “asphalt”, “binder”, and “asphalt mixture”. The use of these terminologies reflects a certain regional preference, where the term “asphalt” is commonly adopted by American researchers, while “bitumen” is chosen by Europeans. In American terminology, bitumen is a solid substance, ranging in color from brown to black, with a semi-solid consistency at typical ambient temperature. They consist of a complex mixture of heavy hydrocarbons and their derivatives [67]. Asphalt is a mixture of hydrocarbons derived from petroleum naturally or by distillation, with bitumen being its main component. It can also contain small amounts of other materials such as oxygen, nitrogen, and sulfur. Therefore, asphalt is a bituminous material because it has bitumen [68].
The term “binder” refers to a material that is used to hold the aggregates together in a mixture. The binder can be made of asphalt, being called asphalt binder. It is typically made up of bitumen or asphalt but can also include additives or modifiers to enhance its performance and properties [69]. Thus, the asphalt binder provides cohesion, strength, and durability to the asphalt pavement [70]. Among the asphalt binders, there are asphalt cement, asphalt cutbacks, foamed asphalt, and asphalt emulsions, among others. Usually, the generic term asphalt binder is used to represent the principal binding agent, the asphalt cement. Finally, the asphalt mixture is an American term used to determine the mix of aggregates with the asphalt binder commonly used for constructing asphalt pavements and roads [71]. However, in some cases, asphalt only can refer to the asphalt mixtures.
The variation in terminologies related to asphalt research can be influenced by factors such as geography, cultural traditions, historical developments, technical differences, and academic influence. For example, sometimes the terminology asphalt can be used to represent the asphalt mixture or the asphalt binder. The term asphalt is commonly adopted by American researchers, while bitumen is used by Europeans. Additionally, industry norms, regulations, and evolving language can also contribute to the diverse terminological landscape within the field.
Photocatalytic and superhydrophobic (referred to by the terms contact angle and hydrophobicity) capabilities are close to each other, probably due to their combination of effects (all included in the second cluster). Moreover, this water-related capability, having as its main functionalization goal the removal of snow and ice that is related to the anti-ice/deicing capability, is usually applied with conductive materials such as steel fibers, wool, and recycled materials, such as slags, which are activated by the microwave of induction heating or even by additives containing salts (included in the third cluster). The anti-ice/deicing capability is also related to self-healing, thus showing the vicinity of the fourth cluster. Regarding self-healing, it can be functionalized by these types of conductive materials, but also by nanomaterials or even by oils (materials with high content of maltenes). All techniques aim to heal the cracks of the asphalt binder (bituminous materials, bitumen, and binders), which means that it presents nearness to the first cluster. The first cluster is more related to chemical, thermal, and microsurface analyses, totally associated with PCM. These tests are essential for nanomaterials, such as oxide minerals (including titanium dioxide). Encapsulation seems to be more related to self-healing than PCM, which can be an alternative for PCM leakage into the asphalt mixture. Slags are associated with anti-icing aspects but are highly recommended for self-healing as well.
The 45 most used keywords, considering the 626 documents in the database, were listed in Figure 3. The word that appeared the most was “asphalt”, followed by “asphalt mixture”. They appeared in 348 (56% of the total) and 216 (35% of the total) articles, respectively. In addition, considering the analysis of the top ten keywords, it is possible to highlight the interest of asphalt researchers in the self-healing capacity applied to asphalt mixtures. This is due to the presence of the words “self-healing materials”, “self-healing”, and “self-healing properties” occupying the third, fifth, and tenth positions.
Terms such as “phase change materials” (related to LHTS), “snow and ice removal” and “ice” (usually related to anti-ice/deicing), and “titanium dioxide” and “nitrogen oxides” (usually related to photocatalysis) also appear, indicating their significant contribution to the literature. Characterization tests and processes, such as Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), microwave heating, induction heating, and dynamic shear rheometer, are also present. Properties like mechanical properties (specifically “fatigue of materials” and “tensile strength”) and thermal conductivity are emphasized. These occurrences may be associated with the current focus on the design and optimization of these capabilities. Future research may involve real context applications, environmental analysis, and modeling.
The practical application of smart asphalt mixtures may face barriers due to limited development and a lack of comprehensive studies that evaluate their environmental impacts, durability, economic benefits, and overall performance in real field conditions. Transitioning from laboratory environments to practical implementation requires robust evidence demonstrating their effectiveness in applied contexts. In the literature, only self-healing and anti-ice appear as the most developed capabilities in the field implementation. Self-healing focuses on the mechanical part of the and gains the attention of researchers. For the other capacities less developed, a Life Cycle Assessment (LCA) and Life Cycle Cost Analysis (LCCA) can contribute to information about the environmental impact and application and maintenance costs by incorporating new capabilities into the asphalt mixture. Some studies related to photocatalytic capacities show that this approach, nevertheless, is still incipient.
In addition, future research opportunities also are related to developing mixtures with multiple capacities. Few studies have explored the synergistic effects and integration of different functionalities to create asphalt mixtures with enhanced performance.

4.2. Overview of the Smartness/Capability

To assess the evolution of these capabilities over time and also assess their level of innovation, Figure 4 is presented. It can be observed that the concern about ice as pavements is not new since there are a few references related to this subject before the 1990s. Nevertheless, publications on the functionalization of asphalt mixtures from different points of view start significantly growing around 2012. The documents related to self-healing exponentially increased while the other ones did not follow this scientific interest. Anti-ice/deicing, LHTS, and photocatalysis seem to increase with similar scientific interest seen by equivalent evolution in recent years. It is observed that thermochromism, superhydrophobicity, and self-cleaning capabilities are the most innovative ones, as there are just a few numbers of publications. It is possible to conclude that self-healing is the most relevant and developed capability applied to asphalt mixtures with a sharp increase in interest from 2017.
For a more accurate analysis, the relative contribution of each capability (i.e., each capability’s significance for the database) is displayed in Figure 5. Self-healing has the highest contribution to the database at 55.5%, while the others have less than 20%. Anti-ice/deicing is the second most targeted capability (14.9%), followed by photocatalysis and LHTS, each with 11.2%. Self-cleaning, superhydrophobicity, and thermochromism have only 1.7%, 2.6%, and 3.2% contributions, respectively, confirming that these functionalization processes are indeed innovative. Compared to self-healing, this may also indicate that researchers have no or do not yet have such interest in these topics.

4.3. Statistics of Journals

Figure 6 shows the bibliographic coupling network by sources (journals) appearing at least twice. Nine clusters were presented, with Construction and Building Materials (CBM) as the central journal in the network. Other noteworthy journals include the Journal of Cleaner Production (JCP), Materials (M), Road Materials and Pavement Design (RMPD), and Transportation Research Record (TRR), among others.
Table 1 presents the distribution of the top 10 most published journals. The Impact Factor (IF) value is taken from JCR Clarivate, and the value Cite Score from Elsevier (Scopus), both of the year 2021. CBM is the leading journal accounting for 219 of the total number of articles, or 35% of the overall publication ranking. The Journal of Materials in Civil Engineering is the second, contributing 49 articles (7.8% of the total), although still significantly behind the leading journal. However, considering the IF, JCP is the most relevant journal. CBM ranks as the second most relevant journal, with the other ones presenting IF in a range from 1.333 to 4.178.
Regarding Cite Score, JCP and CBM are the most significant ones with a score of 15.8 and 10.6, respectively. This suggests that these journals are highly influential and that researchers frequently cite articles published within them when discussing smart asphalt mixtures.
The publication trends of the ten journals in the 2000s are illustrated in Figure 7. RMPD was the first journal to publish an article in 2005, followed by the IJPRT in 2009. Since 2010, other journals have emerged, with CBM showing a remarkable growth trend. CBM has been maintaining its lead since 2015, surpassing other journals significantly in terms of publication numbers. It should be noted that the graph showed a decrease in publications on the topic in 2022, but this is expected because the research was conducted in January 2022 and included publications up to that period.
Figure 8 shows a radar chart with four quantitative variables (published articles, citations, Impact Factor (IF), and H-index for the ten most productive journals. The influence of an article is linked to the journal in which it was published [72]. CBM and JCP stand out the most, each scoring the highest in the two indicators. CMB has the highest score for articles and citations and the second highest for the H-Index and Impact Factor. JCP scores best for H-index and Impact Factor but has one of the lowest scores for citations and published articles in the field. High H-Index scores from these journals indicate they publish many high-quality articles. A high Impact Factor reflects the journal’s citation potential, visibility, and importance.
The cumulative score of indicators for the top ten most productive journals analyzed is presented in Figure 9. CMB remains to be prominent with the highest cumulative score. The JCP occupies the second position, despite having a small number of published articles, as it has the highest Impact Factor and H-index compared to the others. These data demonstrate the influence and quality of the articles published in JCP, highlighting the relevance of this journal. It can be concluded that the most relevant journal for the smart asphalt mixtures is CBM with the highest number of articles and citations and the second-best regarding H-index and IF.

4.4. Statistics of Authors

Figure 10 shows the co-authorship network from different perspectives: authors (Figure 10a), institutions (Figure 10b), and countries (Figure 10c). The co-authorship database (publishing at least seven articles) generated twelve clusters. As seen in Figure 10a, Chinese researchers (Wang, Ma, Wang, and Zhang, among others) represent the most connected authors, forming a larger cluster; nevertheless, the most relevant researcher to this network is Wu S., who has been working on the anti-icing, self-healing, and LHTS capabilities. State Key Laboratory of Silicate Materials for Architectures is the core of smart asphalt materials.
The top ten authors in this research topic on capacities applied to asphalt mixtures, who published at least 15 articles, are presented in Table 2. The table shows the author’s ranking, the number of documents they have published, their institution, and the specific capabilities they have worked on. Among these authors, nine of ten works with self-healing, while less than half focus on other capabilities: LHTS, anti-ice/deicing, and photocatalysis, listed in order of frequency. The Department of Construction Management and Industrial Engineering, Engineering and Geosciences, Micromechanics Laboratory, and Key Laboratory of Road and Traffic Engineering of the Ministry of Education are the institutions to which at least two of the ten most productive authors are affiliated.
Table 3 displays the ranking of the top ten most productive authors based on the total number of citations received in their documents. Garcia A. has the highest total citation count, followed by Schlangen E. and Liu Quantao. Interestingly, the most cited work for Schlangen E. and Liu Quantao is the same, demonstrating collaboration between the authors and overlapping research interests.

4.5. Most-Cited Articles

The number of citations is an indicator for assessing the importance of a document. Highly cited papers tend to reflect the research topics that have captured the interest of the academic community in a particular subject area [72]. Table 4 shows the articles that received the highest number of citations out of the 626 articles in the database and the capacities that are studied in them. Nine of ten articles were published after 2010, implying a relatively recent and emerging trend in the field. It is notable that all of the articles investigate the topic of self-healing, indicating a significant development of this capacity. The CBM journal stands out by presenting more than half (60%) of the most cited articles, reinforcing its relevance and influence in the field of study. All documents are article-type publications.
The analysis of the highly cited articles was realized to evaluate the focus among researchers. Álvaro [46] examined the self-healing nature of asphalt mastic by incorporating steel wool fibers of varying diameters. He investigated the mechanisms of thermal curing, including the influence of temperature and rest periods. Guangji and Hao [77] used molecular dynamics simulation and explored the impact of aging on self-healing potential. For comparative purposes, molecular models of virgin and aged asphalt binders with percentages of saturates, aromatics, resins, and asphaltenes (SARA) were created. Amit, Rammohan, Michael, and Dallas [78] also use molecular simulations to understand the relationship between the molecular properties of asphalt binders and the self-healing mechanism. They studied two types of molecular structures chosen based on a literature review. Álvaro, Erik, Martin, and Quantao [14] evaluated the increase in the rate of self-heling with the addition of graphite (filler) and steel wool fibers. Juan, Miguel, Verónica, and Antonio [80] studied asphalt samples with different percentages of steel wool subjected to microwave to aid self-healing. Jose and Alvaro [73] also used steel wool fibers to evaluate the effect of microwave heating and considered the effect of induction on self-healing. The effects of induction heating on self-healing were studied by Quantao, Alvaro, Erik, and Martin [79]; Alvaro, Moises, Jose, and Manfred [81]; Alessandro, Alvaro, Manfred, Gabriele, and Philipp [82]; and Quantao, Erik, Martin, Gerbert, and Jo [83].

5. Conclusions

This work aimed to investigate the information available in the scientific literature on smart asphalt mixtures through a bibliometric analysis. The focus is primarily on publications from 2012 onwards. Key terms and their relevance, the evolution of the concern in new asphalt capabilities, prominent authors, organizations, countries, and journals related to the research topic, and the relationships between these variables were identified. The main conclusions were:
  • The scientific production of smart asphalt started significantly growing around 2012. The documents related to self-healing exponentially increased.
  • Self-healing is the most developed capability with higher occurrence and growth and has seen a sharp increase in interest since 2017. The most innovative ones are thermochromism, superhydrophobicity, and self-cleaning. Anti-ice/deicing, LHTS, and photocatalysis seem to increase with similar scientific interest seen by equivalent evolution in recent years.
  • Self-healing has received more attention due to its direct correlation with the mechanical properties of mixtures.
  • The practical application of technology for certain capabilities, from the laboratory to real scale, is still in its early stages. However, self-healing and anti-ice capacities are the ones that have made the most progress in field applications.
  • In the domain of self-healing, there are several techniques with the same objectives, covering nanomaterials, oil-filled capsules, ionomers, and conductive materials like steel slag, carbon fibers, and steel wool. The other capacities present applications with fewer techniques and materials.
  • Developing these new capabilities requires a multidisciplinary approach that encompasses knowledge of optics, chemistry, and climate science, among others. This interdisciplinary nature can present obstacles to advancing some capabilities.
  • Various specific and often unconventional tests are employed to investigate the properties of the new capabilities, enabling a deeper understanding of the behavior of smart asphalt mixtures. These tests include gas degradation tests, contact angle measurements, Fourier transform infrared spectroscopy, and fluorescence microscopy, among others, depending on the specific capacities being studied.
  • China leads in research on smart asphalt mixtures, with a significant and well-established group of researchers. The institution that stands out as the leading research group in this area is from China.
  • Regarding the authors, three stand out due to their relevant articles considering scientific production and quality. They have accumulated citations between 1000 and 2018 and placed in the ranking of the most productive authors in the field. Their main contributions are related to self-healing and anti-ice/deicing.
  • The most important journal in this scope is Construction and Building Materials (with the highest number of articles and citations) followed by the Journal of Cleaner Production (with the highest H-index and IF).
  • Concerning the frequently used keywords, terms related to self-healing (“self-healing materials”, “self-healing”, and “self-healing properties”) are highlighted. With an intermediate number of occurrences, terms related to LHTS (“phase change materials”), anti-ice/deicing (“snow and ice removal”), photocatalysis (“titanium dioxide” and “nitrogen oxides”) confirm the relevance of these capabilities applied to asphalt mixtures.
  • In the literature, the term “smart asphalt” is related to the new capabilities and the processes of both management and optimization of its production and application. Thus, this shows that this term is still going through a consolidation phase and can present meanings completely different depending on the studies.
  • Future research may involve real-world applications, environmental analysis, modeling, and application of some of these new capabilities in other elements of road engineering, not just in asphalt pavements.
  • The limited number of studies focusing on the life cycle of smart and multifunctional asphalt mixtures highlights their importance. However, a significant gap exists in cost analysis for all these capabilities. Although a few economic evaluations are available, such as for photocatalytic coatings, it is crucial to conduct thorough economic studies to ensure the feasibility of these technologies.

Author Contributions

Conceptualization, I.G.d.R.S. and O.d.S.L.J.; methodology, I.G.d.R.S. and O.d.S.L.J.; software, I.G.d.R.S. and É.M.M.; validation, C.G.d.S.P., E.F.d.F. and J.A.S.A.O.C.; formal analysis, I.G.d.R.S., É.M.M. and O.d.S.L.J.; investigation, I.G.d.R.S., J.A.S.A.O.C. and E.F.d.F.; resources, J.A.S.A.O.C. and E.F.d.F.; data curation, C.G.d.S.P.; writing—original draft preparation, I.G.d.R.S., É.M.M. and O.d.S.L.J.; writing—review and editing, C.G.d.S.P.; visualization, I.G.d.R.S., O.d.S.L.J. and J.A.S.A.O.C.; supervision, E.F.d.F. and J.A.S.A.O.C.; project administration, J.A.S.A.O.C. and E.F.d.F.; funding acquisition, J.A.S.A.O.C. and E.F.d.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Portuguese Foundation for Science and Technology (FCT), NanoAir PTDC/FISMAC/6606/2020, MicroCoolPav EXPL/EQU-EQU/1110/2021, UIDB/04650/2020, and UIDB/04029/2020. This research was also supported by the doctoral grant 2023.02795.BD, funded by FCT, as well as and bydoctoral grant PRT/BD/154269/2022 financed by the FCT and with funds from POR Norte-Portugal 2020 and State Budget, under MIT Portugal Program. The first author would like to acknowledge the FCT for funding (2022.00763.CEECIND).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Segundo, I.R.; Freitas, E.; Branco, V.T.F.C.; Landi, S.; Costa, M.F.; Carneiro, J.O. Review and analysis of advances in functionalized, smart, and multifunctional asphalt mixtures. Renew. Sustain. Energy Rev. 2021, 151, 111552. [Google Scholar] [CrossRef]
  2. Liu, Y.; Su, P.; Li, M.; You, Z.; Zhao, M. Review on evolution and evaluation of asphalt pavement structures and materials. J. Traffic Transp. Eng. 2020, 7, 573–599. [Google Scholar] [CrossRef]
  3. Wei, H.; Zhang, H.; Li, J.; Zheng, J.; Ren, J. Effect of loading rate on failure characteristics of asphalt mixtures using acoustic emission technique. Constr. Build. Mater. 2023, 364, 129835. [Google Scholar] [CrossRef]
  4. Han, B.; Wang, Y.; Dong, S.; Zhang, L.; Ding, S. Smart concretes and structures: A review. J. Intell. Mater. Syst. Struct. 2015, 26, 1303–1345. [Google Scholar] [CrossRef]
  5. Han, B.; Zhang, L.; Ou, J. Smart and Multifunctional Concrete toward Sustainable Infrastructures; Springer: Berlin/Heidelberg, Germany, 2017; ISBN 978-981-10-4348-2. [Google Scholar]
  6. Segundo, I.R.; Freitas, E.; Landi, S.; Costa, M.F.M.; Carneiro, J.O. Smart, photocatalytic and self-cleaning asphalt mixtures: A literature review. Coatings 2019, 9, 696. [Google Scholar] [CrossRef] [Green Version]
  7. Sun, Y.; Liu, Q.; Wu, S.; Shang, F. Microwave heating of steel slag asphalt mixture. Key Eng. Mater. 2014, 599, 193–197. [Google Scholar] [CrossRef]
  8. Mehta, S.; Kushwaha, A.; Kisannagar, R.R.; Gupta, D. Fabrication of a reversible thermochromism based temperature sensor using an organic–inorganic composite system. RSC Adv. 2020, 10, 21270–21276. [Google Scholar] [CrossRef]
  9. Kalnæs, S.E.; Jelle, B.P. Phase change materials and products for building applications: A state-of-the-art review and future research opportunities. Energy Build. 2015, 94, 150–176. [Google Scholar] [CrossRef] [Green Version]
  10. Peng, C.; Yu, J.; Zhao, Z.; Fu, J.; Zhao, M.; Wang, W.; Dai, J. Preparation and properties of a layered double hydroxide deicing additive for asphalt mixture. Cold Reg. Sci. Technol. 2015, 110, 70–76. [Google Scholar] [CrossRef]
  11. Baheri, F.T.; Poulikakos, L.D.; Poulikakos, D.; Schutzius, T.M. Ice adhesion behavior of heterogeneous bituminous surfaces. Cold Reg. Sci. Technol. 2021, 192, 103405. [Google Scholar] [CrossRef]
  12. Pan, P.; Wu, S.; Xiao, F.; Pang, L.; Xiao, Y. Conductive asphalt concrete: A review on structure design, performance, and practical applications. J. Intell. Mater. Syst. Struct. 2015, 26, 755–769. [Google Scholar] [CrossRef]
  13. Luo, S.; Yang, X. Performance evaluation of high-elastic asphalt mixture containing deicing agent Mafilon. Constr. Build. Mater. 2015, 94, 494–501. [Google Scholar] [CrossRef]
  14. García, Á.; Schlangen, E.; van de Ven, M.; Liu, Q. Electrical conductivity of asphalt mortar containing conductive fibers and fillers. Constr. Build. Mater. 2009, 23, 3175–3181. [Google Scholar] [CrossRef]
  15. Giuliani, F.; Merusi, F.; Polacco, G.; Filippi, S.; Paci, M. Effectiveness of sodium chloride-based anti-icing filler in asphalt mixtures. Constr. Build. Mater. 2012, 30, 174–179. [Google Scholar] [CrossRef]
  16. Ma, T.; Geng, L.; Ding, X.; Zhang, D.; Huang, X. Experimental study of deicing asphalt mixture with anti-icing additives. Constr. Build. Mater. 2016, 127, 653–662. [Google Scholar] [CrossRef]
  17. Gao, J.; Sha, A.; Wang, Z.; Tong, Z.; Liu, Z. Utilization of steel slag as aggregate in asphalt mixtures for microwave deicing. J. Clean. Prod. 2017, 152, 429–442. [Google Scholar] [CrossRef]
  18. Sun, Y.; Wu, S.; Liu, Q.; Hu, J.; Yuan, Y.; Ye, Q. Snow and Ice Melting Properties of Self-healing Asphalt Mixtures with Induction Heating and Microwave Heating. Appl. Therm. Eng. 2017, 129, 871–883. [Google Scholar] [CrossRef]
  19. Peng, C.; Zhang, H.; You, Z.; Xu, F.; Jiang, G.; Lv, S.; Zhang, R. Preparation and anti-icing properties of a superhydrophobic silicone coating on asphalt mixture. Constr. Build. Mater. 2018, 189, 227–235. [Google Scholar] [CrossRef]
  20. Arabzadeh, A.; Ceylan, H.; Kim, S.; Gopalakrishnan, K.; Sassani, A. Superhydrophobic coatings on asphalt concrete surfaces: Toward smart solutions for winter pavement maintenance. Transp. Res. Rec. 2016, 2551, 10–17. [Google Scholar] [CrossRef]
  21. Nahvi, A.; Sadoughi, M.; Arabzadeh, A.; Sassani, A. Multi-objective Bayesian optimization of super hydrophobic coatings on asphalt concrete surfaces. J. Comput. Des. Eng. 2018, 6, 693–704. [Google Scholar] [CrossRef]
  22. Rocha Segundo, I.; Ferreira, C.; Freitas, E.F.; Carneiro, J.O.; Fernandes, F.; Landi Júnior, S.; Costa, M.F. Assessment of photocatalytic, superhydrophobic and self-cleaning properties on hot mix asphalts coated with TiO2 and/or ZnO aqueous solutions. Constr. Build. Mater. 2018, 166, 36–44. [Google Scholar] [CrossRef] [Green Version]
  23. Han, S.; Yao, T.; Yang, X. Preparation and anti-icing properties of a hydrophobic emulsified asphalt coating. Constr. Build. Mater. 2019, 220, 214–227. [Google Scholar] [CrossRef]
  24. Peng, C.; Hu, X.; You, Z.; Xu, F.; Jiang, G.; Ouyang, H.; Guo, C.; Ma, H.; Lu, L.; Dai, J. Investigation of anti-icing, anti-skid, and water impermeability performances of an acrylic superhydrophobic coating on asphalt pavement. Constr. Build. Mater. 2020, 264, 120702. [Google Scholar] [CrossRef]
  25. Carneiro, J.O.; Azevedo, S.; Teixeira, V.; Fernandes, F.; Freitas, E.; Silva, H.; Oliveira, J. Development of photocatalytic asphalt mixtures by the deposition and volumetric incorporation of TiO2 nanoparticles. Constr. Build. Mater. 2013, 38, 594–601. [Google Scholar] [CrossRef]
  26. Barthlott, W.; Mail, M.; Bhushan, B.; Koch, K. Plant surfaces: Structures and functions for biomimetic innovations. Nano-Micro Lett. 2017, 9, 1–40. [Google Scholar] [CrossRef] [Green Version]
  27. Schraufnagel, D.E.; Balmes, J.R.; Cowl, C.T.; De Matteis, S.; Jung, S.-H.; Mortimer, K.; Perez-Padilla, R.; Rice, M.B.; Riojas-Rodriguez, H.; Sood, A.; et al. Air Pollution and Noncommunicable Diseases. Chest 2019, 155, 417–426. [Google Scholar] [CrossRef]
  28. Fan, W.; Chan, K.Y.; Zhang, C.; Zhang, K.; Ning, Z.; Leung, M.K.H. Solar Photocatalytic Asphalt for Removal of Vehicular NOx: A Feasibility Study. Appl. Energy 2018, 225, 535–541. [Google Scholar] [CrossRef]
  29. Jensen, H.; Pedersen, P.D. Real-life Field Studies of the NOx Removing Properties of Photocatalytic Surfaces in Roskilde and Copenhagen Airport, Denmark. J. Photocatal. 2020, 2, 71–81. [Google Scholar] [CrossRef]
  30. European Environment Agency. Emissions from Road Traffic and Domestic Heating behind Breaches of EU Air Quality Standards across Europe; European Environment Agency: Copenhagen, Denmark, 2022. [Google Scholar]
  31. Environmental Protection Agency. Smog, Soot, and Other Air Pollution from Transportation; Environmental Protection Agency: Washington, DC, USA, 2023. [Google Scholar]
  32. Chouhan, J.; Chandrappa, A.K. A systematic review on photocatalytic concrete for pavement applications: An innovative solution to reduce air pollution. Innov. Infrastruct. Solut. 2023, 8, 90. [Google Scholar] [CrossRef]
  33. Hassan, M.M.; Dylla, H.; Asadi, S.; Mohammad, L.N.; Cooper, S. Laboratory Evaluation of Environmental Performance of Photocatalytic Titanium Dioxide Warm-Mix Asphalt Pavements. J. Mater. Civ. Eng. 2012, 24, 599–605. [Google Scholar] [CrossRef]
  34. Hassan, M.; Mohammad, L.N.; Asadi, S.; Dylla, H.; Cooper, S. Sustainable Photocatalytic Asphalt Pavements for Mitigation of Nitrogen Oxide and Sulfur Dioxide Vehicle Emissions. J. Mater. Civ. Eng. 2013, 25, 365–371. [Google Scholar] [CrossRef]
  35. Fernández-Pampillón, J.; Palacios, M.; Núñez, L.; Pujadas, M.; Sanchez, B.; Santiago, J.L.; Martilli, A. NOx depolluting performance of photocatalytic materials in an urban area—Part I: Monitoring ambient impact. Atmos. Environ. 2021, 251, 118190. [Google Scholar] [CrossRef]
  36. Segundo, I.R.; Zahabizadeh, B.; Landi, S.; Lima, O.; Afonso, C.; Borinelli, J.; Freitas, E.; Cunha, V.M.C.F.; Teixeira, V.; Costa, M.F.M.; et al. Functionalization of Smart Recycled Asphalt Mixtures: A Sustainability Scientific and Pedagogical Approach. Sustainability 2022, 14, 573. [Google Scholar] [CrossRef]
  37. Wang, D.; Leng, Z.; Hüben, M.; Oeser, M.; Steinauer, B.; Hüben, M.; Oeser, M.; Steinauer, B. Photocatalytic pavements with epoxy-bonded TiO2-containing spreading material. Constr. Build. Mater. 2016, 107, 44–51. [Google Scholar] [CrossRef]
  38. Hu, J.; Asce, S.M.; Yu, X.B.; Asce, M. Reflectance Spectra of Thermochromic Asphalt Binder: Characterization and Optical Mixing Model. J. Mater. Civ. Eng. 2015, 28, 1–10. [Google Scholar] [CrossRef]
  39. Lima, O., Jr.; Freitas, E.; Cardoso, P.; Margalho, É.; Moreira, L.; Nascimento, J.; Landi, S., Jr.; Carneiro, J. Mitigation of Urban Heat Island Effects by Thermochromic As- phalt Pavement Thermosensitive Asphalt Pavements. Coatings 2022, 13, 35. [Google Scholar] [CrossRef]
  40. Hu, J.; Yu, X. Performance evaluation of solar-responsive asphalt mixture with thermochromic materials and nano-TiO2 scatterers. Constr. Build. Mater. 2020, 247, 118605. [Google Scholar] [CrossRef]
  41. Chen, Z.; Zhang, H.; Duan, H.; Shi, C. Improvement of thermal and optical responses of short-term aged thermochromic asphalt binder by warm-mix asphalt technology. J. Clean. Prod. 2021, 279, 123675. [Google Scholar] [CrossRef]
  42. Anupam, B.R.; Sahoo, U.C.; Rath, P. Phase change materials for pavement applications: A review. Constr. Build. Mater. 2020, 247, 118553. [Google Scholar] [CrossRef]
  43. Ren, J.; Ma, B.; Si, W.; Zhou, X.; Li, C. Preparation and analysis of composite phase change material used in asphalt mixture by sol-gel method. Constr. Build. Mater. 2014, 71, 53–62. [Google Scholar] [CrossRef]
  44. Ma, B.; Wang, S.S.; Li, J. Study on Application of PCM in Asphalt Mixture. Adv. Mater. Res. 2010, 168–170, 2625–2630. [Google Scholar] [CrossRef]
  45. Chen, M.Z.; Hong, J.; Wu, S.P.; Lu, W.; Xu, G.J. Optimization of Phase Change Materials Used in Asphalt Pavement to Prevent Rutting. Adv. Mater. Res. 2011, 219–220, 1375–1378. [Google Scholar] [CrossRef]
  46. García, Á. Self-healing of open cracks in asphalt mastic. Fuel 2012, 93, 264–272. [Google Scholar] [CrossRef]
  47. Xu, S.; García, A.; Su, J.; Liu, Q.; Tabaković, A.; Schlangen, E. Self-Healing Asphalt Review: From Idea to Practice. Adv. Mater. Interfaces 2018, 1800536, 1800536. [Google Scholar] [CrossRef] [Green Version]
  48. Lima, O., Jr.; Cardoso, P.; Rocha Segundo, I.; Freitas, E.; Costa, M.F.M.; do Nascimento, J.H.O.; Afonso, C.; Landi, S., Jr.; Teixeira, V.; Carneiro, J.O. Thermochromism Applied to Transportation Engineering: Asphalt roads, color, and road markings. In Proceedings of the 5th International Conference on Application of Optics and Photonics (AOP), Guimarães, Portugal, 18–22 July 2022. [Google Scholar]
  49. Chang, X.; Zhang, R.; Xiao, Y.; Chen, X.; Zhang, X.; Liu, G. Mapping of publications on asphalt pavement and bitumen materials: A bibliometric review. Constr. Build. Mater. 2020, 234, 117370. [Google Scholar] [CrossRef]
  50. Pérez-Acebo, H.; Linares-Unamunzaga, A.; Abejón, R.; Rojí, E. Research trends in pavement management during the first years of the 21st century: A bibliometric analysis during the 2000–2013 Period. Appl. Sci. 2018, 8, 1041. [Google Scholar] [CrossRef] [Green Version]
  51. Singer, M.N.; Hamouda, M.A.; El-Hassan, H.; Hinge, G. Permeable Pavement Systems for Effective Management of Stormwater Quantity and Quality: A Bibliometric Analysis and Highlights of Recent Advancements. Sustainability 2022, 14, 13061. [Google Scholar] [CrossRef]
  52. Ali, L.; Alnajjar, F.; Khan, W.; Serhani, M.A.; Al Jassmi, H. Bibliometric Analysis and Review of Deep Learning-Based Crack Detection Literature Published between 2010 and 2022. Buildings 2022, 12, 432. [Google Scholar] [CrossRef]
  53. El Hakea, A.H.; Fakhr, M.W. Recent computer vision applications for pavement distress and condition assessment. Autom. Constr. 2023, 146, 104664. [Google Scholar] [CrossRef]
  54. Abejón, R. Self-healing asphalt: A systematic bibliometric analysis for identification of hot research topics during the 2003–2018 period. Materials 2021, 14, 565. [Google Scholar] [CrossRef]
  55. Pritchard, A. Statistical bibliography or bibliometrics. J. Doc. 1969, 25, 348. [Google Scholar]
  56. Qu, Z.; Zhang, S.; Zhang, C. Patent research in the field of library and information science: Less useful or difficult to explore? Scientometrics 2017, 111, 205–217. [Google Scholar] [CrossRef]
  57. Zhao, D.; Strotmann, A. Analysis and Visualization of Citation Networks; Springer: Berlin/Heidelberg, Germany, 2015. [Google Scholar]
  58. Ellegaard, O.; Wallin, J.A. The bibliometric analysis of scholarly production: How great is the impact? Scientometrics 2015, 105, 1809–1831. [Google Scholar] [CrossRef]
  59. 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, 285–296. [Google Scholar] [CrossRef]
  60. Van Eck, N.; Waltman, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 2014, 84, 523–538. [Google Scholar] [CrossRef] [Green Version]
  61. van Eck, N.J.; Waltman, L.; Noyons, E.C.M.; Buter, R.K. Automatic term identification for bibliometric mapping. Scientometrics 2010, 82, 581–596. [Google Scholar] [CrossRef] [Green Version]
  62. Jan van Eck, N.; Waltman, L. VOSviewer Manual Manual for VOSviewer Version 1.6.17; Centre for Science and Technology Studies, Leiden University: Leiden, The Netherlands, 2021; pp. 1–53. [Google Scholar]
  63. Dong, P.; Loh, M.; Mondry, A. The “impact factor” revisited. Biomed. Digit. Libr. 2005, 2, 1–8. [Google Scholar] [CrossRef] [Green Version]
  64. Clarivate, J. Nature—Journal’s Performance. Available online: https://jcr.clarivate.com/jcr-jp/journal-profile?journal=NATURE&year=2021&fromPage=%2Fjcr%2Fhome (accessed on 15 April 2023).
  65. Gaster, N.; Gaster, M. A critical assessment of the h-index. BioEssays 2012, 34, 830–832. [Google Scholar] [CrossRef]
  66. Scopus Scopus Source. Available online: https://www.scopus.com/sourceid/21206. (accessed on 15 April 2023).
  67. Yadykova, A.Y.; Strelets, L.A.; Ilyin, S.O. Infrared Spectral Classification of Natural Bitumens for Their Rheological and Thermophysical Characterization. Molecules 2023, 28, 2065. [Google Scholar] [CrossRef]
  68. Bernucci, L.B.; da Motta, L.M.G.; Ceratti, J.A.P.; Soares, J.B. Pavimentação Asfáltica—Formação Básica para Engenheiros; PETROBRAS ABEDA: Rio de Janeiro, Brazil, 2008. [Google Scholar]
  69. Mersha, D.A.; Sendekie, Z.B. High-Temperature Performance Enhancement of Bitumen by Waste PET-Derived Polyurethane. Adv. Mater. Sci. Eng. 2022, 2022, 1–15. [Google Scholar] [CrossRef]
  70. Branco, F.; Pereira, P.; Santos, L.P. Pavimentos Rodoviários; Almedina Publishing: Coimbra, Portugal, 2006. [Google Scholar]
  71. Chang, J.; Li, J.; Hu, H.; Qian, J.; Yu, M. Numerical Investigation of Aggregate Segregation of Superpave Gyratory Compaction and Its Influence on Mechanical Properties of Asphalt Mixtures. J. Mater. Civ. Eng. 2023, 35. [Google Scholar] [CrossRef]
  72. Rahman, T.; Zudhy Irawan, M.; Noor Tajudin, A.; Rizka Fahmi Amrozi, M.; Widyatmoko, I. Knowledge mapping of cool pavement technologies for urban heat island Mitigation: A Systematic bibliometric analysis. Energy Build. 2023, 291, 113133. [Google Scholar] [CrossRef]
  73. Norambuena-Contreras, J.; Garcia, A. Self-healing of asphalt mixture by microwave and induction heating. Mater. Des. 2016, 106, 404–414. [Google Scholar] [CrossRef]
  74. Qiu, J.; van de Ven, M.F.C.; Wu, S.P.; Yu, J.Y.; Molenaar, A.A.A. Investigating self healing behaviour of pure bitumen using Dynamic Shear Rheometer. Fuel 2011, 90, 2710–2720. [Google Scholar] [CrossRef]
  75. Sun, D.; Lin, T.; Zhu, X.; Tian, Y.; Liu, F. Indices for self-healing performance assessments based on molecular dynamics simulation of asphalt binders. Comput. Mater. Sci. 2016, 114, 86–93. [Google Scholar] [CrossRef]
  76. Su, J.-F.; Qiu, J.; Schlangen, E. Stability investigation of self-healing microcapsules containing rejuvenator for bitumen. Polym. Degrad. Stab. 2013, 98, 1205–1215. [Google Scholar] [CrossRef]
  77. Xu, G.; Wang, H. Molecular dynamics study of oxidative aging effect on asphalt binder properties. Fuel 2017, 188, 1–10. [Google Scholar] [CrossRef]
  78. Bhasin, A.; Bommavaram, R.; Greenfield, M.L.; Little, D.N. Use of Molecular Dynamics to Investigate Self-Healing Mechanisms in Asphalt Binders. J. Mater. Civ. Eng. 2011, 23, 485–492. [Google Scholar] [CrossRef]
  79. Liu, Q.; García, Á.; Schlangen, E.; Ven, M. Van De Induction healing of asphalt mastic and porous asphalt concrete. Constr. Build. Mater. 2011, 25, 3746–3752. [Google Scholar] [CrossRef]
  80. Gallego, J.; del Val, M.A.; Contreras, V.; Páez, A. Heating asphalt mixtures with microwaves to promote self-healing. Constr. Build. Mater. 2013, 42, 1–4. [Google Scholar] [CrossRef]
  81. García, A.; Bueno, M.; Norambuena-Contreras, J.; Partl, M.N. Induction healing of dense asphalt concrete. Constr. Build. Mater. 2013, 49, 1–7. [Google Scholar] [CrossRef]
  82. Menozzi, A.; Garcia, A.; Partl, M.N.; Tebaldi, G.; Schuetz, P. Induction healing of fatigue damage in asphalt test samples. Constr. Build. Mater. 2015, 74, 162–168. [Google Scholar] [CrossRef]
  83. Liu, Q.; Schlangen, E.; van de Ven, M.; van Bochove, G.; van Montfort, J. Evaluation of the induction healing effect of porous asphalt concrete through four point bending fatigue test. Constr. Build. Mater. 2012, 29, 403–409. [Google Scholar] [CrossRef]
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Figure 1. Methodology of bibliometric analysis.
Figure 1. Methodology of bibliometric analysis.
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Figure 2. Co-occurence network of the most frequent terms of the smart asphalt mixtures.
Figure 2. Co-occurence network of the most frequent terms of the smart asphalt mixtures.
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Figure 3. The top 45 most frequently used keywords.
Figure 3. The top 45 most frequently used keywords.
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Figure 4. Evolution of smartness/capability with time.
Figure 4. Evolution of smartness/capability with time.
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Figure 5. Percentage of each smartness/capability.
Figure 5. Percentage of each smartness/capability.
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Figure 6. Bibliographic coupling by sources.
Figure 6. Bibliographic coupling by sources.
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Figure 7. The trend graphs of the articles published in ten productive journals.
Figure 7. The trend graphs of the articles published in ten productive journals.
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Figure 8. Four aspects analysis of ten productive journals.
Figure 8. Four aspects analysis of ten productive journals.
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Coatings 13 01396 g009
Figure 9. Cumulative score of the top ten journals.
Figure 9. Cumulative score of the top ten journals.
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Coatings 13 01396 g010
Figure 10. Co-authorship analysis by: (a) authors, (b) institutions, and (c) countries.
Figure 10. Co-authorship analysis by: (a) authors, (b) institutions, and (c) countries.
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Table 1. The top most productive journals.
Table 1. The top most productive journals.
RankingJournalDocumentsPercentage (%)IFCite Score
1Construction and Building Materials (CBM)21935.07.69310.6
2Journal of Materials in Civil Engineering (JMCE)497.83.6515.5
3Materials (M)304.83.7484.7
4Journal of Cleaner Production (JCP)264.211.07215.8
5Road Materials and Pavement Design (RMPD)243.83.8056.3
6Transportation Research Record (TRR)193.02.0193.0
7Applied Sciences (Switzerland) (AS)121.92.8382.6
8International Journal of Pavement Research and Technology (IJPRT)121.92.6694.5
9Journal of Testing and Evaluation (JTE)121.91.3333.7
10International Journal of Pavement Engineering (IJPE)111.84.1786.5
Table 2. The top most productive authors.
Table 2. The top most productive authors.
RankingAuthorDocumentsInstitutionCapabilities
1Wu Shaopeng49Department of Construction Management and Industrial Engineering, Louisiana State University.Self-healing, LHTS, and anti-ice/deicing
2Garcia A.37Delft University of Technology, Faculty of Civil Engineering and Geosciences, Micromechanics Laboratory (MICROLAB).Self-healing
3Schlangen E.34Civil Engineering and Geosciences, Delft University of Technology.Self-healing
4Liu Quantao33Delft University of Technology, Faculty of Civil Engineering and Geosciences, Micromechanics Laboratory (MICROLAB).Self-healing and LHTS
5Norambuena-Contreras J.32Department of Civil and Environmental Engineering, University of Bío-Bío.Self-healing
6Hassan M.20Dept. of Construction Management and Industrial Engineering, Louisiana State University.Self-healing and Photocatalysis
7Su Jun-Feng20Institute of Materials Science and Chemical Engineering, Tianjin University of CommerceSelf-healing and Anti-ice/deicing
8Ma B.19Highway and Railway Engineering, Chang’An UniversityLHTS
9Zhu Xingyi19Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji UniversitySelf-healing, LHTS, and anti-ice/deicing
10Sun D.17Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji UniversitySelf-healing and anti-ice/deicing
Table 3. The most cited authors among the most productive.
Table 3. The most cited authors among the most productive.
RankingAuthorTotal CitationsMost Cited ArticlesRef.
1Garcia A.2018Self-healing of open cracks in asphalt mastic[46]
2Schlangen E.1514Self-healing of asphalt mortars with conductive fibers and fillers[14]
3Liu Quantao1141Self-healing of asphalt mortars with conductive fibers and fillers[14]
4Norambuena-Contreras J.1069Self-healing of asphalt mixture by microwave and induction heating using steel wool fibers[73]
5Wu Shaopeng1004Investigating self-healing behavior of pure bitumen using dynamic shear rheometer[74]
6Zhu Xingyi655Indices for self-healing performance assessments based on molecular dynamics simulation of asphalt binders[75]
7Sun D.516Indices for self-healing performance assessments based on molecular dynamics simulation of asphalt binders[75]
8Su Jun-Feng508Stability investigation of self-healing microcapsules containing rejuvenator for bitumen[76]
9Hassan M.349Sustainable photocatalytic asphalt pavements for mitigation of nitrogen oxide and sulfur dioxide vehicle emissions[34]
10Ma B.260Preparation and analysis of composite phase change material used in asphalt mixture by sol–gel method[43]
Table 4. Ten most cited articles.
Table 4. Ten most cited articles.
Ref.Source Title, YearTitleDocument TypeCitationsCapability
[46]Fuel, 2012Self-healing of open cracks in asphalt masticArticle249Self-healing
[77]Fuel, 2017Molecular dynamics study of oxidative aging effect on asphalt binder propertiesArticle178Self-healing
[14]Construction and Building Materials, 2009Electrical conductivity of asphalt mortar containing conductive fibers and fillersArticle172Self-healing
[73]Materials and Design, 2016Self-healing of asphalt mixture by microwave and induction heatingArticle150Self-healing
[78]Journal of Materials in Civil Engineering, 2011Use of molecular dynamics to investigate self-healing mechanisms in asphalt bindersArticle139Self-healing
[79]Construction and Building Materials, 2011Induction healing of asphalt mastic and porous asphalt concreteArticle137Self-healing
[80]Construction and Building Materials, 2013Heating asphalt mixtures with microwaves to promote self-healingArticle129Self-healing
[81]Construction and Building Materials, 2013Induction healing of dense asphalt concreteArticle129Self-healing
[82]Construction and Building Materials, 2015Induction healing of fatigue damage in asphalt test samplesArticle115Self-healing
[83]Construction and Building Materials, 2012Evaluation of the induction healing effect of porous asphalt concrete through four-point bending fatigue testArticle107Self-healing
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da Rocha Segundo, I.G.; Margalho, É.M.; Lima, O.d.S., Jr.; Pinheiro, C.G.d.S.; de Freitas, E.F.; Carneiro, J.A.S.A.O. Smart Asphalt Mixtures: A Bibliometric Analysis of the Research Trends. Coatings 2023, 13, 1396. https://doi.org/10.3390/coatings13081396

AMA Style

da Rocha Segundo IG, Margalho ÉM, Lima OdS Jr., Pinheiro CGdS, de Freitas EF, Carneiro JASAO. Smart Asphalt Mixtures: A Bibliometric Analysis of the Research Trends. Coatings. 2023; 13(8):1396. https://doi.org/10.3390/coatings13081396

Chicago/Turabian Style

da Rocha Segundo, Iran Gomes, Élida Melo Margalho, Orlando de Sousa Lima, Jr., Claver Giovanni da Silveira Pinheiro, Elisabete Fraga de Freitas, and Joaquim Alexandre S. A. Oliveira Carneiro. 2023. "Smart Asphalt Mixtures: A Bibliometric Analysis of the Research Trends" Coatings 13, no. 8: 1396. https://doi.org/10.3390/coatings13081396

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