Prediction of Pipe-Jacking Forces Using a Bayesian Updating Approach
Prediction of Pipe-Jacking Forces Using a Bayesian Updating Approach
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DOI：
10.1061/(asce)gt.1943-5606.0002645
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发表时间：
2022-01
影响因子：
3.9
通讯作者：
B. Sheil;S. Suryasentana;J. Templeman;B. Phillips;W. Cheng;Limin Zhang
中科院分区：
文献类型：
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作者：
B. Sheil;S. Suryasentana;J. Templeman;B. Phillips;W. Cheng;Limin Zhang
准确估计在微隧道施工过程中可能遇到的顶推力是管段设计、中间顶管站的位置以及顶管工程本身的有效性的一个关键设计问题。本文提出了一种贝叶斯修正方法来预测微隧道掘进时顶力的大小。所提出的框架被应用于在英国完成的两个5管顶管实例，其中包括在粉砂和粉砂上的275m打入和在泥岩中的61237m打入。为了对贝叶斯预测进行基准测试，还实施了一种经典的优化技术，即遗传算法。结果表明，使用模型输入参数的先验最佳估计对8个顶管力进行预测，对两个驱动器的监测顶推力提供了显着的超前预测。这突出了在规定的设计方法内捕捉隧道施工过程中复杂的岩土条件的困难，以及强大的反分析技术的重要性。贝叶斯更新也被证明是一种非常有效的选项，其中随着从驱动器获取更多数据，总顶推力的均值预测和相关方差获得了显著改进。使用在驾驶过程中获得的最新监测数据。所提出的框架被应用于在英国完成的两个顶管案例，包括在粉砂和粉砂中的275m打入和在泥岩中的1237m打入。使用贝叶斯更新方法确定的预测与使用经典的基准优化技术确定的预测进行比较。
An accurate estimation of the jacking forces likely to be experienced during microtunnelling is a key 2 design concern for the design of pipe segments, the location of intermediate jacking stations and 3 the efficacy of the pipe jacking project itself. This paper presents a Bayesian updating approach for 4 the prediction of jacking forces during microtunnelling. The proposed framework is applied to two 5 pipe jacking case histories completed in the UK including a 275 m drive in silt and silty sand and a 6 1237 m drive in mudstone. To benchmark the Bayesian predictions, a ‘classical’ optimisation 7 technique, namely genetic algorithms, is also implemented. The results show that predictions of 8 pipe jacking forces using the prior best estimate of model input parameters provide a significant 9 over-prediction of the monitored jacking forces for both drives. This highlights the difficulty in 10 capturing the complex geotechnical conditions during tunnelling within prescriptive design 11 approaches and the importance of robust back-analysis techniques. Bayesian updating is also 12 shown to be a very effective option where significant improvements in the mean predictions, and 13 associated variance, of the total jacking force are obtained as more data is acquired from the drive. using the latest monitoring data acquired during the drive. The proposed framework is applied to two pipe jacking case histories completed in the UK including a 275 m drive in silt and silty sand and a 1237 m drive in mudstone. Predictions determined using the Bayesian updating approach are compared to those determined using ‘classical’ optimisation techniques for benchmarking.