A Georeferenced Dataset for Mapping and Assessing Subgrade Defects in China’s High-Speed Railways

• Published in: Scientific data 2024-03, Vol.11 (1), p.293-293, Article 293
• Main Authors: Wang, Jinchen, Wang, Luqi, Zhang, Yinsheng, Zhang, Jingyu, Li, Jianlin, Li, Sen
• Format: Article
• Language: English
• Subjects: 639/166/986; 704/844/4081; Data Descriptor; Datasets; Defects; High speed rail; Humanities and Social Sciences; multidisciplinary; Quality control; Science; Science (multidisciplinary)
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-024-03112-7

China has the world’s longest high-speed rail (HSR) network, marked by dense transportation and complex operations. However, frequent train use coupled with extreme weather conditions has led to rising subgrade issues. Existing railway defect records suffer from inconsistency, hindering direct applicability. Currently, there is a lack of a relevant dataset dedicated to HSR subgrade defects. To bridge this gap, we developed a comprehensive georeferenced dataset that encompasses defect records extracted from peer-reviewed literature published between 1999 and 2023 in China. Rigorous quality control procedures were implemented to eliminate duplicate data and ensure the accuracy of the dataset. The dataset consists of georeferenced records for eight different defects, spanning across 661 locations and categorized at various scales, ranging from provinces to townships. The most commonly reported defect types include subgrade settlement, frost damage, uplift deformation, and mud pumping. This dataset provides a comprehensive map of historical subgrade defects affecting high-speed railways in China. It could facilitate operational risk assessments and the prediction of subgrade performance.