神經網絡在大尺度采空區損傷演化統計與預測中應用

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基金項目:

國家自然科學基金(No.50074002)和國家教委博士點基金(No.2000000802)資助項目

Application of Neural Network to the Statistics and Prediction of Dynamical Damage and Evolutement in the Large Scale Mine-out Area Supported by Rock-Based Composite Materials

1. Civil and Environmental Engineering School, University of Science and Technology Beijing, Beijing 100083, China;
2. Energy School, Xi'an University of Science and Technology, Xi'an 710054, China

摘要: 利用神經網絡結構計算方法對巖石基復合材料支護大尺度采空區動力損傷演化趨勢進行統計和預測,并與工程現場多種方法的綜合監測數據進行了比較,其結果完全吻合.
- 神經網絡 /
- 大尺度采空區 /
- 動力損傷與演化 /
- 統計與預測
Abstract: The neural network structural computation method was applied to the trend statistics and prediction of stress evolvement in the large scale mine-out area supported by rock-based composite materials. The predicting values were compared with the in-situ monitoring ones. The results show they are very agreeable.
- neural network /
- large scale mined-out area /
- dynamical damage and evolvement /
- statistics and prediction