Microtunnelling is an increasingly popular means of locating utilities below ground. The ability to predict the total jacking force requirements during a drive is highly desirable for anomaly detection, to ensure the available thrust is not exceeded, and to prevent damage to the pipe string and/or launch shaft. However, prediction of the total jacking force is complicated by site geology, the use of a lubricated overcut, work stoppages, tunnel boring machine driving style and pipe misalignment. This paper introduces a probabilistic observational approach for forecasting jacking forces during microtunnelling. Gaussian process regression is adopted for this purpose which allows forecasts to be performed within a probabilistic framework. The proposed approach is applied to two recent UK microtunnelling monitoring projects and the forecasts are appraised through comparisons to predictions determined using design methods currently applied in industry. The results show that the proposed framework provides excellent forecasts of the monitored field data and highlights a significant opportunity to complement existing prescriptive design methods with probabilistic forecasting techniques.

微隧道管理是一种将公用设施定位在地下的越来越流行的方法。对于异常检测，确保不超过可用推力并防止损坏管柱和/或发射轴，非常需要能够预测驱动过程中总顶压力的能力。但是，总起顶力的预测因现场地质，润滑的过挖，工作停工，隧道掘进机的驱动方式和管道未对准而变得复杂。本文介绍了一种概率观测方法，用于预测微隧道中的顶升力。为此，采用了高斯过程回归，从而可以在概率框架内执行预测。拟议的方法应用于最近的两个英国微隧道监测项目，并且通过与使用当前行业中使用的设计方法确定的预测进行比较来评估预测。结果表明，所提出的框架为监测的现场数据提供了出色的预测，并突出了利用概率预测技术补充现有的规范设计方法的重要机会。