Risk assessment model of tunnel water inrush based on improved attribute mathematical theory

• Published in: Journal of Central South University Vol. 25; no. 2; pp. 379 - 391
• Main Authors: Yang, Xiao-li, Zhang, Sheng
• Format: Journal Article
• Language: English
• Published: Changsha Central South University 01-02-2018; Springer Nature B.V
• Subjects: Disasters; Engineering; Mathematical models; Metallic Materials; Model accuracy; Pressure head; Risk assessment; Trigonometric functions; Underground construction; tunnel water inrush; 风险评估模型; 相似权重法; risk assessment model; similar weight method; 隧道突涌水; nonlinear measurement function; 属性数学理论; attribute mathematical theory; 非线性测度函数
• ISSN: 2095-2899; 2227-5223
• DOI: 10.1007/s11771-018-3744-5

Tunnel water inrush is one of the common geological disasters in the underground engineering construction. In order to effectively evaluate and control the occurrence of water inrush, the risk assessment model of tunnel water inrush was proposed based on improved attribute mathematical theory. The trigonometric functions were adopted to optimize the attribute mathematical theory, avoiding the influence of mutation points and linear variation zones in traditional linear measurement functions on the accuracy of the model. Based on comprehensive analysis of various factors, five parameters were selected as the evaluation indicators for the model, including tunnel head pressure, permeability coefficient of surrounding rock, crushing degree of surrounding rock, relative angle of joint plane and tunnel section size, under the principle of dimension rationality, independence, directness and quantification. The indicator classifications were determined. The links among measured data were analyzed in detail, and the objective weight of each indicator was determined by using similar weight method. Thereby the tunnel water inrush risk assessment model is established and applied in four target segments of two different tunnels in engineering. The evaluation results and the actual excavation data agree well, which indicates that the model is of high credibility and feasibility.