A TBM advance rate prediction method considering the effects of operating factors. (January 2021) - Jing, Liu-jie; Li, Jian-bin; Zhang, Na; Chen, Shuai; Yang, Chen; Cao, Hong-bo
Highlights: Operating factors affect and determine the TBM penetration rate. TBM penetration rate prediction model is established. TBM downtime is classified and evaluated to obtain the utilization factor. TBM advance rate is determined by the penetration rate and utilization factor. The boreability of surrounding rock can be classified based on the advance rate. Abstract: Due to the widespread use of Tunnel Boring Machines (TBMs) in China, all affected parties involved in related construction of projects have proposed higher requirements for the controllability of TBM construction periods. Consequently, this report proposes a new TBM advance rate prediction method. By mining TBM construction data obtained from a project diverting water from the Songhua River to cities in the middle region of the Jilin Province in China (hereinafter referred to as the "Songhua River Diversion Water Supply Project") and analysing the varying rules of the tunnelling parameters under different surrounding rock conditions, this report proposes a corresponding relationship relating the surrounding rock conditions, reduction coefficient of the disc cutter thrust (Fn ) and cutterhead rotation speed (N), and establishes a TBM penetration rate (PR) prediction model, which is affected by factors associated with the operator's control. Furthermore, after investigating and summarizing the TBM construction processes of several projects, TBM downtime is classified and evaluated to obtain the TBM utilization factor (U). Based on the PR and U, a TBM advance rate (AR) prediction model is established. The established AR model was applied to a water diversion project in the Xinjiang Uygur Autonomous Region of China. This engineering practice has demonstrated that the operator adjusts the PR according to the surrounding rock conditions, and the prediction of the PR affected by the operating factors has been proven accurate. Furthermore, the prediction method of U obtained by evaluating the downtime is simple and feasible. Overall, the results indicate that the proposed AR model possesses satisfactory predictive performance.