Abstract
In order to evaluate the risk level of water inrush caused by karst cave accurately and effectively, a novel quantitative assessment model was established based on the reliability theory and genetic algorithm-back propagation (GA-BP) neural network. First, the reliability theory and the calculation formula of the minimum safe thickness were used to calculate the water inrush probability. Second, the GA-BP neural network was applied to predict the disaster consequence caused by water inrush. Six factors, including water pressure, hydraulic supply, type of gap, filling situation, degree of water enrichment and reserves of cave, were selected as the input layer of the neural network. The disaster consequence was selected as the output layer. Similar projects were screened to obtain statistical information for indices, and the Normand function in MATLAB was used to transform the information into quantitative data. Finally, the model was established by combining the probability and disaster consequence of water inrush. The 602cave in Yesanguan tunnel was taken as an engineering sample to verify the feasibility of the novel model. The obtained results showed that the proposed model is comprehensive and accurate in quantitative assessment, which has good application prospects in engineering.
Recommended Citation
Z. Li et al., "Risk Assessment of Water Inrush Caused by Karst Cave in Tunnels based on Reliability and GA-BP Neural Network," Geomatics, Natural Hazards and Risk, vol. 11, no. 1, pp. 1212 - 1232, Taylor & Francis, Nov 2020.
The definitive version is available at https://doi.org/10.1080/19475705.2020.1785956
Department(s)
Civil, Architectural and Environmental Engineering
Keywords and Phrases
GA-BP neural network; quantitative assessment; Reliability theory; water inrush
International Standard Serial Number (ISSN)
1947-5705; 1947-5713
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2020 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Nov 2020
Comments
The study was supported by the Independent Innovation Project for Double First-level Construction (China University of Mining and Technology) (2018ZZCX04).