山岭隧道洞口段地表沉降时序预测研究  

Time series prediction for ground settlement in portal section of mountain tunnels

作　　者：王述红[1] 朱宝强 WANG Shu-hong;ZHU Bao-qiang(School of Resource and Civil Engineering,Northeastern University,Shenyang 110819,China)

机构地区：[1]东北大学资源与土木工程学院,辽宁沈阳110819

出　　处：《岩土工程学报》2021年第5期813-821,I0003,共10页Chinese Journal of Geotechnical Engineering

基　　金：国家自然科学基金项目(U1602232);中央高校基本科研业务费专项资金项目(N170108029);辽宁省重点研发计划(2019JH2/10100035);东北大学双一流建设项目(2018)。

摘　　要：地表沉降监测值具有复杂性及非线性动态变化特征,以往静态模型预测时常存在易受历史监测数据干扰且模型输入权值及阈值选择较为困难的问题,鉴于此,提出一种洞口段地表沉降动态预测方法。利用3次样条函数插值法将监测数据等距化,并结合时序分析理论和变分模态分解(VMD),将地表沉降分解为趋势项和随机项位移;通过采用灰狼优化算法(GWO)对在线贯序极限学习机模型(OSELM)的权值及阈值进行优化,建立了GWO-OSELM动态预测模型,分别对位移分量进行预测;以重庆市兴隆隧道洞口段为例,利用该模型进行预测,并与传统模型进行对比,最后探讨了激励函数的选择对模型预测性能的影响及随机项位移的部分影响因素。结果表明:非等距时序数据预处理后,模型能够有效地对位移分量进行预测,预测精度高、误差小,且Sigmoid激励函数更适合该模型,而地表沉降速率和拱顶下沉速率对随机项位移有重要影响。可为山岭隧道洞口段地表沉降的长期预测提供一种新的思路和方法。The monitoring value of ground settlement is characterized by complexity and nonlinear dynamic change. Aiming at the problems that the previous static models are easily disturbed by historical monitoring data and the model input weights and thresholds are more difficult to choose, a dynamic prediction method for ground settlement of the portal section of tunnels is proposed. The ground settlement is equidistant by the cubic-spline function interpolation method and decomposed into the trend and random term displacement by the time series analysis theory and the variational mode decomposition(VMD). By using the grey wolf optimizer(GWO) to optimize the weights and thresholds of the online sequential extreme learning machine(OSELM), the GWO-OSELM dynamic prediction model is established to predict the displacement components separately. Taking the portal section of Xinglong tunnel in Chongqing as an example, the proposed model is compared with the traditional model. Finally, the influences of the choice of activation function on the prediction performance of the model and some factors influencing the random term displacement are analyzed. The results show that the model can effectively predict the displacement components after the preprocessing of non-equidistant time series data, and it has high prediction accuracy and small prediction error. Moreover, the Sigmoid activation function is more suitable for the model, and the rates of the ground settlement and the vault subsidence have important influences on the random term displacement. The model provides a new way of thinking and a method for the long-term prediction of ground settlement in the portal section of mountain tunnels.

关 键 词：山岭隧道 地表沉降 非等距时间序列 变分模态分解 灰狼优化 在线贯序极限学习机 

分 类 号：U459.2[建筑科学—桥梁与隧道工程]