Risk assessment of water inrush caused by karst cave in tunnels based on reliability and GA-BP neural network
| Autor: | Shengqi Yang, Jiao Qinglei, Mitian Wang, Zhaoyang Li, Yingchao Wang, C. Guney Olgun |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Reliability theory
010504 meteorology & atmospheric sciences lcsh:Risk in industry. Risk management ga-bp neural network 0211 other engineering and technologies 02 engineering and technology 01 natural sciences lcsh:TD1-1066 Mining engineering Quantitative assessment lcsh:Environmental technology. Sanitary engineering lcsh:Environmental sciences Reliability (statistics) 021101 geological & geomatics engineering 0105 earth and related environmental sciences General Environmental Science lcsh:GE1-350 geography Risk level geography.geographical_feature_category Artificial neural network Karst Inrush current reliability theory lcsh:HD61 quantitative assessment General Earth and Planetary Sciences water inrush Risk assessment Geology |
| Zdroj: | Geomatics, Natural Hazards & Risk, Vol 11, Iss 1, Pp 1212-1232 (2020) |
| ISSN: | 1947-5713 1947-5705 |
| DOI: | 10.1080/19475705.2020.1785956 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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