Prediction of long-term settlements of subway tunnel in the soft soil area
Prediction of long-term settlements of subway tunnel in the soft soil area
复制标题
软土地区地铁隧道长期沉降预测
DOI：
10.1007/s11069-014-1228-y
复制
发表时间：
2014-11-01
期刊：
影响因子：
3.7
通讯作者：
Ren, Shi-Xi
中科院分区：
文献类型：
--
作者：
Cui, Zhen-Dong;Ren, Shi-Xi
目前，土体沉降预测问题已逐渐成为一个重要的研究领域。静载作用下土体沉降预测的理论比较成熟，而动载作用下土体沉降预测的方法尚处于探索阶段。本文旨在寻找一种适合地铁隧道长期沉降预测的模型。以上海地铁1号线沉降监测数据为例，分析了地铁沉降监测结果对地铁沉降的影响.本文对目前沉降的非线性预测方法进行了总结。介绍了该拟合方法，并将其应用于上海地铁隧道沉降数据，拟合结果的相关系数r在大多数情况下都能保持较高水平，说明了分段模拟的有效性。本文介绍了两种预测方法及其应用方法，灰色模型（1，1）和自回归滑动平均模型（n，m）。采用GM（1，1）和阿尔马（n，m）模型对上海地铁1号线的沉降趋势进行了预测。结果表明，阿尔马（n，m）模型比GM（1，1）模型具有更高的预测精度。阿尔马（n，m）模型作为沉降预测领域的一种新方法，具有很好的应用前景。
Nowadays, the issue of predicting soil settlement has gradually become an important research area. The theory of predicting soil settlement under static load is comparatively mature, while the method of predicting soil settlement under dynamic loading is still at the exploratory stage. This paper aimed to find a suitable model to satisfy the prediction of long-term settlements of subway tunnel. The settlement monitoring data of Subway Line 1 in Shanghai were taken as the case. In this paper, current nonlinear prediction methods of settlement were summarized. The fitting method was introduced and applied in the settlement data of Shanghai subway tunnel; correlation coefficient r of the fitting results can keep a high level in most cases, illustrating the validity of segmentation simulation. Two kinds of prediction methods and its utilizing methods were introduced in this paper, i.e., Grey Model (1, 1) and Auto-Regressive and Moving Average Model (n, m). The settlement trend of Subway Line 1 in Shanghai was predicted by GM (1, 1) and ARMA (n, m) model. The results show that ARMA (n, m) model is more precise than the GM (1, 1). As a new method in settlement prediction field, ARMA (n, m) model is prospective in the future.