Knowledge Mapping of Pipeline Accidents: A Bibliometric Analysis

The pipeline production system is characterized by high operating and maintenance risks, alongside a high accident rate, making the pipeline’s safe and smooth operation critical. The goal of this study was to perform a full bibliometric analysis of 1,584 papers obtained from the Web of Science database between 2004 and 2023. The study focuses on time trends, major institutions, major authors, journal citations, and literature citations; the results are visualized and analyzed based on CiteSpace and VOSviewer to comprehend the field’s evolutionary trends, research hotspots, and frontiers. The study discovered that the number of publications on pipeline accidents grew dramatically between 2004 and 2023. Despite the theoretical framework for studying pipeline accidents, a great number of cutting-edge branches and research topics continue to emerge. In particular, the topics of failure analysis, risk assessment, leak monitoring, accident prevention, reliability, and pipeline integrity are the main development directions and research hotspots at present. In terms of innovation, the application of bibliometric methods has improved the efficiency and ability to handle large databases of literature and to perform network analysis. Machine learning approaches are becoming increasingly prevalent as research methods, as seen by the growing number of papers that use different kinds of machine learning techniques. This research offers a theoretical foundation and recommendations for the creation of methods for identifying pipeline accident hazards and preventing accidents.