隧道围岩压力的神经网络时间序列分析

隧道围岩压力的神经网络时间序列分析

秦胜伍，陈剑平

吉林大学建设工程学院, 长春130026

收稿日期:2008-04-02 修回日期:1900-01-01 出版日期:2008-11-26 发布日期:2008-11-26
通讯作者: 秦胜伍

Time Series Analysis of Surrounding Rock Pressure with Artificial Neural Networks

QIN Sheng-wu，CHEN Jian-ping

College of Construction Engineering, Jilin University, Changchun 130026, China

Received:2008-04-02 Revised:1900-01-01 Online:2008-11-26 Published:2008-11-26
Contact: QIN Sheng-wu

摘要/Abstract

摘要： 围岩压力是隧道开挖后重要的反馈信息之一，受不确定性因素影响，围岩压力监测数据是一个不平稳的时间序列，包括趋势项和随机项。采用BP网络对不平稳时间序列进行数据拟合，处理趋势部分，利用ARMA模型处理随机部分。结合累进算法，对浙江某新建隧道围岩压力进行时间序列预测。结果表明该方法具有较高的预测精度，最大相对误差为3.73％，能够应用于工程实际当中。

关键词: 人工神经网络, ARMA, 时间序列, 围岩压力, 累进算法

Abstract: Surrounding rock pressure is a one of significant feedback information after tunnel excavation. Affected by excavation and other uncertain factors, the monitoring data of rock pressure is an unsmooth time series, which includes trend term and random part. The trend part of the data can be fitted with BP(back propagation) neural network and the random part is processed by a normal ARMA(auto regressive moving average) model. Combined with a progression arithmetic, time series analysis of rock pressure in a tunnel in Zhejiang Province is carried out. The results show that the method has a high prediction accuracy, is in a maximum relative error of 3.73%, and can be used in practical engineering.

Key words: artificial neural network, ARMA, time series, surrounding rock pressure, progression arithmetic

中图分类号:

P642

秦胜伍，陈剑平. 隧道围岩压力的神经网络时间序列分析[J]. J4, 2008, 38(6): 1005-1009.

QIN Sheng-wu，CHEN Jian-ping. Time Series Analysis of Surrounding Rock Pressure with Artificial Neural Networks[J]. J4, 2008, 38(6): 1005-1009.

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