Prediction of long-term settlements of subway tunnel in the soft soil area

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. Copyright Springer Science+Business Media Dordrecht 2014