Prediction of pipe-jacking forces using a Bayesian updating approach

An accurate estimation of the jacking forces likely to be experienced during microtunnelling is a key design concern for the structural capacity of pipe segments, the location of intermediate jacking stations, and the efficacy of the pipe-jacking project itself. This paper presents a Bayesian updating approach for the prediction of jacking forces during microtunnelling. The proposed framework was applied to two pipe-jacking case histories completed in the United Kingdom: a 275-m drive in silt and silty sand, and a 1,237-m drive in mudstone. To benchmark the Bayesian predictions, a classical optimization technique, namely genetic algorithms, is also considered. The results show that predictions of pipe-jacking forces using the prior best estimate of model input parameters provided a significant overprediction of the monitored jacking forces for both drives. This highlights the difficulty of capturing the complex geotechnical conditions during tunnelling within prescriptive design approaches and the importance of robust back-analysis techniques. Bayesian updating was shown to be a very effective option, in which significant improvements in the mean predictions and associated variance of the total jacking force are obtained as more data are acquired from the drive.