Simulation of EPB tunneling using discrete event model: Case study of various grounds in Korea

A Tunnel Boring Machine (TBM) tunneling is one of the most recognized methods used for underground construction in urban area as it has many advantages of safety, high speed, high quality, and is environmentally friendly. Thus an accurate estimation of its performance is necessary for the justification and successful use of TBM on any tunneling project. Normally the evaluation of TBM performance uses the rate of penetration and utilization, which are highly affected by geological condition, machine and operation including site setting, various logistics and their interdependencies. Uncertain downtime and the combination of each factors should also be accounts. Some of above factors are hard to quantify and uncertain, making the evaluation inaccurate. To overcome the difficulty, a tunnel simulation technique was introduced. In this study, modelling tunneling activities and downtimes using discrete event simulation approach is applied to predict TBM performance. A railway tunnel project using EPB passing through mixed ground, hard rock and soft soil sections is considered for the development and verification of the model. Probabilistic input parameters are employed to take into unknowns occurred on site such as uncertain downtime and coupling factors. The results show possibility of predicting TBM performance based on good quality of input parameters of tunneling activities. Simulation modeling uses three methods: discrete event, agent based, and system dynamic. In the beginning, it was classified as detailed element work during the 24-month excavation period, but to improve the reliability of the probability distribution, element work was mainly classified based on the middle category and applied to modeling. In connection with the simulation model, the location of the TBM can be determined in the longitudinal section and plan view, and the status and location of the locomotive can be checked in real time. Various charts linked to the probabilistic working time generated from the simulation model, the work status can be checked in real time. In the future, for the development and expansion of the simulation model, it is necessary to focus on the recording of detailed cycle times for various TBM projects and data management.