在实际应用中,需要不断将新测的监测数据模式补充到训练模式集中,以保持模式识别器的时效性。现实计算条件要求训练模式数保持一个适当的规模,因此在增加一些训练模式时要淘汰相应数量的最“老”的训练模式。
5.实例分析
作为应用实例,以小浪底水利枢纽出水口高边坡某断面一段时间的监测数据进行分析。
表1列出了进行监测资料分析试验的监测时刻,它们对应的监测数据向量模式列于表2。分析时只需从小浪底水利枢纽安全监控系统的原始数据库中调出这些数据即可。
表1 小浪底水利枢纽出水口高边坡某断面监测资料分析模式识别采样情况
1 |
2000-1-3 14:18 |
2 |
2000-1-10 08:24 |
3 |
2000-1-17 15:40 |
4 |
2000-1-24 08:45 |
5 |
2000-1-31 15:00 |
6 |
2000-2-7 14:55 |
V1=(16.5, 13.95,8.25,2.85,1.45,0.65,13.35, 13.15, 8.25, 1.65, 2.45, 2.25, 1876.2, 1815.51, 1403.69, 1687.55, 1682.21, 1822.02, -19.82, -38.61, 0, 0, 0, 0)
V2=(16.75,13.8, 8.2, 2.75,1.4, 0.6, 13.3, 13, 8.2, 1.6, 2.4, 2.25, 1875.66, 1817.09, 1397.67, 1686.91, 1694.52, 1822.15, -19.29, -4.21, 0, 0, 0, 0)
V3=(16.95,12.95,8.3, 2.9, 1.35,0.6, 13.25, 12.95, 8.15, 1.6, 2.35, 2.2, 1872.03, 1817.2, 1403.9, 1686.03, 1693.11, 1822.45, -18.75, -4.04, 0, 0, 0, 0)
V4=(15.8, 12.8, 8.2, 3.05,1.5, 0.65,13.35, 13.05, 8.2, 1.65, 2.5, 2.25, 1873.69, 1816.26, 1402.31, 1681.57, 1681.91, 1822.96, -17.33, -4.04, 0, 0, 0, 0)
V5=(15.75,13.15,8.2, 3.03,1.5, 0.6, 13.3, 13.25, 8.15, 1.6, 2.4, 2.2, 1874.54, 1816.32, 1409, 1686.83, 1662.81, 1826.46, -18.93, -6.44, 0, 0, 0, 0)
V6=(15.85,13.25,8.15,2.9, 1.4, 0.6, 13.35, 13.35, 8.2, 1.7, 2.4, 2.25, 1874.68, 1816.27, 1405.01, 1687.63, 1701.47, 1825.66, -19.91, -5.19, 0, 0, 0, 0)
将上述监测数据向量输入边坡监测模式识别器,即刻可求出对应的边坡滑动模式,如表2所示。
这些分析成果可成为边坡极限分析程序数据文件的基础,应用于边坡的稳定分析中,也可以用来推测边坡稳定的主要因素。
6.结语
结合自动监测仪器系统的使用,应用人工神经网络模式识别技术和边坡极限分析理论,可实现边坡安全监测资料分析的自动化。自动化的在线监测功能和准确的分析成果将显著提高水电工程管理部门对边坡安全和整个水电工程系统运行的可靠性的管理水平。
表2 小浪底水利枢纽出水口高边坡某断面监测资料模式识别分析情况
序号 |
时间 |
滑裂面型式 |
1 |
2000-1-3 14:18 |
1 |
2 |
2000-1-10 08:24 |
1 |
3 |
2000-1-17 15:40 |
1 |
4 |
2000-1-24 08:45 |
1 |
5 |
2000-1-31 15:00 |
1 |
6 |
2000-2-7 14:55 |
3 |
参考文献:
陆峰,博士学位论文《边坡监测的模式识别和极限分析研究》,中国水利水电科学研究院,2001.8
陈祖煜,《岩质高边坡稳定分析和软件系统》,中国水利水电科学研究院,1995.5
Abhijit S. Pandya, Robert B. Macy 著, 徐勇, 荆涛译, 神经网络模式识别及其实现, 电子工业出版社, 1999.6
戴葵. 神经网络实现技术. 国防科技大学出版社, 1998.7
Pattern Recognition Method in Slope Monitor
Abstract: A concept of slice patterns is put forward in this paper. By using slice patterns in slope monitor, we can build the mapping relations between slice patterns and monitor information. Introducing the pattern recognition method of Artificial Neural Network, a pattern recognizer for slope monitor is built to judge the safe state patterns of the slopes at any monitor times based on the slope monitor information. The stability of slopes can be estimated based on the above information. As a case, this pattern recognizer is applied in analyzing a section of monitor data of Xiaolangdi Outtake Slope. It is showed that the effects of this pattern recognizer is reliable enough for slope monitor.
Keyword: Slope; Safe Monitor; Pattern Recognition; Artificial Neural Network