基于贝叶斯网络的盾构隧道横截面变形预测方法

The Cross-section Deformation Prediction Method for Shield Tunnel Using Bayesian Network

  • 摘要: 盾构隧道横截面变形是影响隧道安全运营的重要因素,目前尚缺乏合理考虑多源不确定性的横截面变形预测方法,同时也忽视了钢筋锈蚀对其变形的影响。首先,根据钢筋腐蚀影响建立了管片刚度退化模型;然后,采用均质圆环法建立了基于贝叶斯网络的隧道管片横截面变形预测模型;最后,利用监测数据及马尔科夫链蒙特卡罗模拟方法对模型进行更新和验证。研究结果表明,经过贝叶斯网络模型更新后,模型预测的结果与监测值更为接近,其中2013年的模型后验预测结果与实测值完全一致,2015年的模型预测值与实测值的误差仅为1.6%。而贝叶斯网络更新前,2013年及2015年的预测误差分别为21.1%及21.9%,可见当融入从实测数据得到的信息时,贝叶斯网络的预测结果与实际监测数据更为符合,提高了模型预测精度。

     

    Abstract: The cross-section deformation is one of the major factors affecting the service safety of the shield tunnel. However, the cross-section deformation prediction method with reasonable consideration on multi-source uncertainty is still lacking, and the influence of reinforcement corrosion is neglected. In this paper, the segment bending degradation model is built considering the corrosion effect, and then a cross-section deformation model is built using uniform rigidity ring method based on a Bayesian network .The monitoring data and Markov Chain Monte Carlo simulation method are used to update the model. The study shows that the model prediction after Bayesian updating is closer to the monitoring data, the model prediction in 2013 after updating is nearly the same as the monitoring data,and there has only 1.6% error between the model prediction in 2015 after updating and the monitoring data. In contrast, the prediction errors for 2013 and 2015 are 21.1% and 21.9% respectively. It is obvious that the model prediction of Bayesian network is revised after fusing the monitoring data, making it closer to the monitoring data and the model prediction accuracy is improved greatly.

     

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