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Research of the Mining Subsidence in TongChuan Mining Area of China Forecasting System Based on Catastrophe Theory

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Book cover Advances in Technology and Management

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 165))

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Abstract

Mining subsidence is the main geological disaster in the mountainous area of Tong Chuan coal mining area of Shaanxi province of China. The geological disaster control and forecasting system of Tong Chuan coal mining area was put forward already, but the forecasting system was not proved to be ideal. In order to improve the result accuracy of the forecasting system, this paper set up a new disaster forecasting model through Catastrophe Theory. The characters and distributing rule were analyzed carefully on mining subsidence of Tong Chuan mining area. The elasticity was used to analyze the developing mechanism of mining subsidence, and the Catastrophe Theory was used to set up the model on identification of mining subsidence disaster. At last the similar material simulation validated that the forecasting is accurate. The results of the paper are the following: The main geological factors of controlled the mining subsidence disaster are large mining depth and small mining height and thick bedrock and thin unconsolidated layers. When the length of working face exceeded 220m, the earth’s surface would distort to destroy the buildings so that the mining subsidence disaster would occur. The results show that the forecasting system has high accuracy.

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Correspondence to Sun Xue-yang .

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© 2012 Springer-Verlag GmbH Berlin Heidelberg

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Xue-yang, S., Yu-cheng, X. (2012). Research of the Mining Subsidence in TongChuan Mining Area of China Forecasting System Based on Catastrophe Theory. In: Kim, H. (eds) Advances in Technology and Management. Advances in Intelligent and Soft Computing, vol 165. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29637-6_2

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  • DOI: https://doi.org/10.1007/978-3-642-29637-6_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29636-9

  • Online ISBN: 978-3-642-29637-6

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