神经网络在大尺度采空区损伤演化统计与预测中应用

基金项目:

国家自然科学基金(No.50074002)和国家教委博士点基金(No.2000000802)资助项目

详细信息

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: 利用神经网络结构计算方法对岩石基复合材料支护大尺度采空区动力损伤演化趋势进行统计和预测,并与工程现场多种方法的综合监测数据进行了比较,其结果完全吻合.
- 神经网络 /
- 大尺度采空区 /
- 动力损伤与演化 /
- 统计与预测
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