ICT‐Driven Data Mining Analysis in Civil Engineering: A Scientometric Review

Abstract

In the contemporary landscape, the remarkable evolution of civil engineering is being driven by the pervasive integration of Information and Communication Technology (ICT). ICT‐driven innovations are playing a crucial role in advancing sustainable development goals by promoting energy efficiency, minimizing resource consumption, and fostering resilient infrastructure. Solutions such as smart grids, intelligent transportation systems, and sustainable urban planning are integral to this progress to address global challenges. The goal of the current study is to conduct a scientometric analysis of scholarly literature published in the recent decade within the domain of ICT‐assisted civil engineering. To achieve this, the study categorizes the civil engineering field into seven major subfields. It includes structural engineering, geotechnical engineering, transportation engineering, water resources engineering, environmental engineering, construction management, and urban planning and design. Employing CiteSpace as the analytical tool, the research offers insights into the intellectual foundations of the civil engineering. This is accomplished through reference co‐citation analysis, cluster analysis, and burst reference analysis. The results demonstrate the adoption of advanced technologies such as Internet of Things (IoT), Machine Learning (ML), Extreme Gradient Boosting (XGBoost), and artificial neural networks in resolving complex civil engineering challenges that reflect the dynamism and diversity of the field. Moreover, it addresses current research challenges within this knowledge domain and explores potential research prospects. The findings emphasize the importance of collaborative efforts among academia, industry stakeholders, and government entities.