Probabilistic Evaluation of Tunnel Boring Machine Penetration Rate Based on Case Analysis

Although geological parameters are known to affect the penetration rate (PR) of a tunnel boring machine (TBM), their relation to the probability of TBM PR has been rarely considered. In this article, a probabilistic evaluation model of TBM PR was proposed. Firstly, the marginal distributions of five geological parameters were confirmed by mathematical statistics. Then Copula theory was used to construct a five-dimensional joint probability distribution of the geological parameters in line with the marginal distributions. Next, the collected geological parameters were utilized to train a three-layer backpropagation neural network (BPNN) model for predicting the TBM PR. Finally, A Copula-BPNN coupled model was built for estimating the probability of TBM PR, and a Weibull distribution function of the predicted TBM PR was obtained through Monte Carlo simulation. Considering the uncertainty, correlation, and multi-factor influence, this paper realized the probabilistic evaluation of TBM PR. Discussion on the parameter uncertainty and independence shows that the variability of the geological parameters is necessary in TBM PR prediction. Quantitative probability estimation of the TBM PR can help with optimizing the driving parameters under different geological conditions to improve construction efficiency.