摘要
为提高隧道变形预测精度,首先,探讨不同小波变换参数的去噪效果,并将隧道的变形数据分为趋势项和误差项;之后,对趋势项和误差项进行单项预测和组合预测,对比分析本文预测结果,研究本文预测模型的有效性。结果表明:sym8小波函数在采用软阈值选取方法、启发式阈值标准及8层小波分解时的去噪结果最优。采用剔除最大误差倒数法通过对趋势项及误差项进行组合预测可知,趋势项组合预测、误差项组合预测较其单项预测的预测精度分别提高了2.5~3.5倍、4.0~5.4倍,达到了提高预测精度的目的,且也不同程度地提高了预测结果的稳定性。通过对本文预测模型的实例分析,验证了本文预测思路的可行性和有效性,预测结果满足大变形预测的要求,具有较高的可行度。
In order to improve the accuracy of tunnel deformation prediction, firstly,we discussed the denoising re-sults by different wavelet transformation parameters, and divided the tunnel deformation data into trend term and er-ror term. Secondly,we established individual and combinatorial forecasting models to predict the trend term and er-ror term, and compared the results so as to verify the proposed prediction model in this paper. The results show that the denoising results of Sym8 wavelet function are the optimum by using the soft threshold selection method, heuris-tic threshold criteria and eight-layer wavelet decomposition. By removing the maximum error and then determine the weights of the rest predicted values in reciprocal order, the prediction accuracy of trend term and error term by combinatorial forecasting model has improved by 2.5-3.5 times and 4.0-5.4 times respectively than that by individu-al forecasting model, hence the reliability of the prediction results are enhanced. Through example analysis of the prediction model,we verified the feasibility and validity of this prediction method,and the results of high feasibility could meet the requirements of large deformation prediction.
出处
《长江科学院院报》
CSCD
北大核心
2017年第7期94-98,105,共6页
Journal of Changjiang River Scientific Research Institute
关键词
隧道工程
小波去噪
大变形
组合预测
剔除最大误差倒数法
对比分析
tunneling engineering
wavelet denoising
large deformation
combination forecasting
error reciprocal after removing maximum error comparative analysis
