Tunnel deformation is of obvious non-linear characteristics. In the present research, models based on the Kalman filter and a variety of optimization support vector machines are built for accurate prediction. The applicability of various models is discussed, and further combinatorial prediction is conducted. Meanwhile, V/S analysis is adopted to calculate the Hurst index of deformation series for deformation trend judgment. The judgment result iscompared with prediction result in the aim of obtaining the comprehensive deformation rules of tunnel. Results suggest that the least squares support vector machine has the optimum effect, and the deformation in the next four cycles would keep increasing. Moreover, the Hurst index of deformation series and deformation rate series is 0.845 and 0.602, respectively, both larger than 0.5, indicating that the deformation in late stage would experience a sustained growth, which is consistent with the prediction result.
Key words
tunnel /
support vector machine /
coefficient of variation /
V/S analysis /
deformation prediction /
trend judgment
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
References
[1] 赵洪波. 支持向量机在隧道围岩变形预测中的应用[J]. 岩石力学与工程学报,2005,24(4):649-652.
[2] 刘开云,乔春生,刘保国. 高速公路连拱隧道施工变形预测的GA-SVR智能模型研究[J]. 公路交通科技,2009,26(5):75-79.
[3] 刘 宇. 基于支持向量机的隧道变形预测模型研究[J].内蒙古科技大学学报,2015,34(4):370-373.
[4] 罗亦泳,张 豪,张立亭. 基于遗传支持向量机的多维灰色变形预测模型研究[J]. 浙江工业大学学报,2010,38(1):79-83.
[5] 李晓龙,魏 丹,王复明. 基于线性规划支持向量机的隧道围岩变形预测[J]. 中外公路,2009,29(4):157-162.
[6] 范思遐,周奇才,熊肖磊,等. 基于粒子群与支持向量机的隧道变形预测模型[J]. 计算机工程与应用,2014,50(5):6-10,15.
[7] 邬长福,涂志刚,万佳威,等. 基于R/S分析与V/S分析的滑坡变形趋势判断及稳定性研究[J]. 水电能源科学,2015,33(1):111-114,107.
[8] 左昌群,刘代国,丁少林,等. 基于分形理论的隧道地表沉降分析及预测[J]. raybet体育在线
院报,2016,33(4):51-56.
[9] 王 成,何美琳,覃 婕,等. 半参数Kalman滤波模型在GPS变形数据处理中的应用[J]. 施工技术,2015,44(增2):818-821.