Received date: 2014-04-20
Revised date: 2014-07-16
Online published: 2015-01-22
Foundation monitoring was necessary methods to ensure the implementation of foundation engineering safety in mines.Because the different models for the settlement of foundation monitoring exists certain difference,therefore,how to select an effective portfolio model that can predict the settlement of foundation pit accurately at a certain time in the future was the main problems.In this research,the time series prediction model and grey model(gray sequence combination forecast model) were employed to a deep foundation pit(5.7~13.7 m deep foundation pit) subsidence monitoring data analysis,and the predicted results were accurate and reliable. Meanwhile,compared with the predicted results of single model(such as ARIMA and GM (1,1)),prediction accuracy of the gray timing sequence model was much higher.The predicted results that we obtained were the closest to the measured values,which presented it was a extremely effective prediction method for foundation pit.
HUO Chengsheng , WANG Chengdong , MENG Junhai , BAI Guolong . Monitoring Predication of Foundation Based on Grey Timing Model[J]. Gold Science and Technology, 2014 , 22(5) : 79 -83 . DOI: 10.11872/j.issn.1005-2518.2014.05.079
[1] 黄红军.GM(1,1)模型在高层建筑物沉降监测中的应用[J].山西建筑,2008,34(14):102-103.
[2] 李炳军,何春花,卢秀霞.基于灰色组合模型的河南省粮食产量预测[J].农业系统科学与综合研究,2008,24(4):411-414,419.
[3] 华博深,秦岩宾,徐朝术,等.灰色线性组合模型在基坑监测中的运用[J].测绘,2011,34(4):163-164,180.
[4] 龚国勇.ARIMA模型在深圳GDP预测中的应用[J].数学的实践与认识,2008,38(4):53-57.
[5] 侯建国,王腾军.变形监测理论与应用[M].北京:测绘出版社,2008:175-176.
[6] 新平.灰色系统模型方法的研究[D].武汉:华中科技大学,2002:35-68.
[7] 王琛艳,郑治.人工神经网络在预测高速公路路基沉降中的应用[J].公路交通科技,2000,(4):7-10.
[8] 胡守仁.神经网络导论[M].长沙:国防科技大学出版社,1993:23-45.
/
〈 | 〉 |