基于大数据思维的隧道施工地表沉降预测方法

为研究城市地铁隧道施工引起地表沉降的分布规律，做好预测预控工作，基于大数据分析思维，利用隧道先期掘进段沉降数据资料分析Peck公式的适用性，在适用性得到验证的前提下，建立带有不确定参数的Peck公式的三维空间分布模型，对模型进行不确定参数的函数转换，根据函数转换条件运用最小二乘法对所求的解进行反转换得到确定性参数，代入Peck公式后即可得到完整的隧道施工地表沉降预测模型。以青岛地铁隧道施工的过程监测数据为例，运用上述方法得到的沉降预测模型计算监测点的预测沉降值，运用灰色预测理论中的后验差检验方法对地表沉降预测模型的精度进行检验评定，评定结果显示该模型所得沉降值和实测数据拟合精度合格率为100%，说明该方法所求参数与模型合理可行，可有效预测隧道施工过程中地表沉降值。

Surface Settlement Prediction Method of Tunnel Construction Based on Big Data Thinking

In order to study the distribution law of ground settlement caused by urban subway tunnel construction and do a good job in forecasting and pre-control, based on big data analysis thinking, the applicability of Peck formula was analyzed by using settlement data of tunnel excavation section in advance. On the premise that the applicability was verified, a three-dimensional spatial distribution model of Peck formula with uncertain parameters was established. The uncertain parameters of the model were transformed by function, and the deterministic parameters were obtained by inverse transformation of the solution according to the function transformation conditions used the least square method. A complete prediction model of tunnel construction surface settlement can be obtained by substituting Peck formula. Taking the monitoring data of Qingdao subway tunnel construction process as an example, the settlement prediction model obtained by the above method was used to calculate the predicted settlement value of the monitoring point, and the posterior difference test method in the grey prediction theory was used to test and evaluate the accuracy of the surface settlement prediction model. The evaluation results show that the qualified rate of fitting accuracy between the settlement value obtained by the model and the measured data is 100%. It shows that the parameters and model of this method are reasonable and feasible, and can effectively predict the surface settlement value during tunnel construction.