Reliability-Based Performance Optimization of Tunnel Boring Machine Considering Geological Uncertainties

This paper is focused on developing an effective method to optimize the performance (e.g., advance rate and energy consumption) of a tunnel boring machine (TBM) by determining reasonable operating and structural parameters according to geological conditions. The TBM is a complex mechatronic system consisting of closely coupled subsystems. The changing of an operating or structural parameter can significantly influence the performances of several subsystems simultaneously in different manners. In addition, most of the subsystems of the TBM are subject to rock loads with server uncertainties, as a result of the probabilistic natures of rock characteristics. To overcome these challenges, multidisciplinary modeling of the subsystems of the TBM is performed to derive the performance functions and limit state functions of the whole machine. Based on design of experiment, sensitive analysis (SA) is carried out to detect the significant factors and their influences on the performances of the whole machine and the subsystems. Based on the SA results, the optimization efficiency can be improved by disregarding insignificant factors. Taking the stochastic properties of rocks into account, a reliability-based performance optimization strategy is proposed. Case studies are carried out and proved that with the proposed method, the performances of the TBM can be greatly improved while the system reliability is kept at high level.