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地铁隧道结构沉降主要受土层状况、地下水位、轨道荷载及周边环境等诸多因素影响,机理复杂,随机性强,而且随时间不断变化,难以用固定的数学模型表示,是一个非平稳的过程。据此,结合以灰色系统理论为基础的TGM(1,1)模型和以时间序列为基础的ARMA(n,m)模型各自的特点,建立了时变参数灰序模型TGM(1,1)-ARMA(n,m),并对地铁隧道主体结构沉降进行了分析与预报,结果表明时变参数灰序模型预测精度较高,且适用于中长期预测。因此,该组合模型在地铁隧道结构沉降预测分析中有较强的适用性,具有一定的应用价值。
The subsidence of metro tunnel structure is mainly affected by soil conditions, groundwater level, orbital load and the surrounding environment and other factors, the mechanism is complex, random, and changes with time, it is difficult to use a fixed mathematical model that is a non-stationary process . Based on the characteristics of the TGM (1,1) model based on the gray system theory and the ARMA (n, m) model based on the time series, the TGM (1,1) -ARMA (n, m). The subsidence of the subway tunnel is analyzed and predicted. The results show that the gray prediction model of time-varying parameters has higher prediction accuracy and is suitable for mid-long term prediction. Therefore, the combined model has strong applicability in the prediction and analysis of subsidence of metro tunnel structures and has certain application value.