## 5 Conclusions
### Scientometric Analysis  
A scientometric study leveraging VOSviewer ([[https://www.vosviewer.com/](https://www.vosviewer.com/)]([https://www.vosviewer.com/](https://www.vosviewer.com/)) (accessed 25 December 2024)) software offers valuable insights into the core research themes in this field. Keywords serve as indicators of the primary areas of interest within a given research domain [107]. For this analysis, co-occurrence was selected as the method, with all keywords defined as the unit of analysis. The resulting keyword co-occurrence network illustrates the interconnections among research topics and their structural relationships. The co-occurrence analysis is the relatedness of items when determined based on the number of documents in which they occur together. The counting method of full counting was used, which means that each link within the same authorship or occurrence has the same weight. A minimum occurrence threshold of three was applied, and out of 1048 keywords examined, 135 met this criterion, forming seven clusters with 1952 links and a total link strength of 2686.  
As shown in Figure 8, the network demonstrates that all keywords are interconnected. A cluster is a set of items in a network map that are grouped together. Clusters in VOSviewer are non-overlapping, meaning an item can belong to only one cluster. Clusters are often used to represent groups of related items, such as similar keywords or identical authors, and are assigned numerical labels for identification. In Figure 4, seven clusters are colored differently. The red cluster contains terms like \"infrastructure,\" \"artificial intelligence,\" \"condition assessment,\" and \"sustainability\". The green cluster contains terms like \"pipes,\" \"inspection,\" and \"deterioration\". The blue cluster contains terms such as \"environmental,\" \"neural networks,\" and \"machine learning.\" Links are the lines connecting the nodes (terms). Each line represents a relationship between two terms. Link strength indicates the intensity or frequency of the connection between two terms. While specific numerical values are not visible in the image, thicker lines imply stronger links. For example: \"Artificial intelligence\"  
Figure 7: Publication trend associated with articles focusing on cost estimation for sewer systems, highlighting the growing scholarly attention from 1995 to 2025.  
and \"infrastructure\" have a prominent link, suggesting a strong relationship. Figure 9 depicts that when a node (term) is selected, it highlights its linked terms.  
Figure 8: Keyword co-occurrences in VOSviewer. (Encircled node is selected in Figure 9).  
Figure 10 is a network visualization, combined with a gradient bar representing the timeline from 2010 to 2025, illustrating the temporal evolution of research focus and the  
Figure 9: Selecting a node in VOSviewer.  
emergence of new areas in the field. Nodes of colors closer to blue represent earlier research trends (closer to 2010), while green and yellow nodes reflect more recent developments (up to 2025). Key observations include the early prominence of foundational topics such as \"civil engineering,\" \"machine learning,\" and \"environmental\" themes, which are central in blue regions. Over time, as reflected by the transition to green and yellow, newer research areas such as \"artificial intelligence,\" \"infrastructure,\" \"sustainability,\" and \"Internet of Things\" have gained prominence. This evolution indicates a shift towards the integration of advanced technologies and sustainability practices in civil engineering and related fields. The terms in yellow, like \"pipes,\" \"asset management,\" and \"automation,\" reflect cutting-edge focus areas involving real-world applications and monitoring systems studied in recent years. Overall, visualization captures a progression from traditional engineering principles to the adoption of smart, AI-driven, and sustainable approaches in contemporary research and practice.