Evaluation Model on Activation Classification of Coal Mine Goaf Ground Considering High-Speed Railway Loads

• Published in: Applied sciences Vol. 14; no. 4; p. 1404
• Main Authors: Li, Xianquan, Ren, Lianwei, He, Pengfei, Yang, Quanwei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2024
• Subjects: activation classification; Analysis; catastrophe progression method; Coal industry; coal mine goaf ground; Coal mining; Decomposition; Design and construction; Discriminant analysis; Engineering; Fuzzy sets; High speed rail; High speed trains; high-speed railway; model test; Railroads; Variables
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14041404

The construction and expansion of high-speed railway networks in China has been occurring at a fast pace, resulting in some lines crossing through coal mine goaf sites. However, the embankment and train loads may trigger the activation of the coal mine goaf ground, posing a threat to traffic safety. To ensure the safety of construction and railway lines, an evaluation model on the activation classification of coal mine goaf ground, taking into account the high-speed railway load, is proposed, which is mainly applicable for middle–deep and level goaf areas using a longwall mining method. Firstly, 12 influencing factors are selected as the underlying evaluation indexes, and the catastrophe progression method model for evaluating the coal mine goaf ground stability is constructed. The findings of the evaluation were found to align with the actual results, indicating the reliability of the model. Then, the additional stress calculation model for high-speed railway ground with different embankment heights, train speeds, and axle loads was established, and the train load disturbance depth with a 5% criterion was determined. The influence degree of load on high-speed railway was divided, and the weight of each factor was determined. Finally, the extension comprehensive evaluation method was used to unite the stability grade of the coal mine goaf site and the influence degree of the train, so the evaluation model on activation classification was proposed. The accurateness and reliability of the proposed model was verified using the Taijiao high-speed railway cases and the model test.