Design and Implementation of Disc Cutter Changing Robot for Tunnel Boring Machine (TBM) Although Tunnel Boring Machine (TBM) is a well developed semiautomated equipment for infrastructure construction, it still needs manual operation such as disc cutter changing task. There is still no practical solution answering the arising accident reports of injury or death during the manually underground cutter changing process around the world. In this paper, we first analysis the current situation and key problems preventing the implementation of automatic cutter changing operation. Then, describe a proposal with detailed consideration solving several main challenges, such as a narrow but long path versus large working space and large payload in the existing TBM, complicated environmental information that is difficult to obtain, such as dark, high pressure, mud and rust. In the finally part an implementation, test, and discussion are carried out to verify the feasibility of this first prototype.

Prediction of TBM Tunneling Parameters through an LSTM Neural Network With the wide application of tunnel boring machines (TBMs) in tunnel construction, the adaptive tuning of the TBM tunneling parameters has become a research focus. Nowadays, since complicated geological conditions are still challenging to predict, the finetuning of tunneling parameters mainly relies on operational experience. Artificial intelligence provides a convenient solution for predicting tunneling parameters through data mining and machine learning. Based on insitu data, this work proposed a novel method for segmenting the original data in tunneling cycles and analyzing the parameter correlation for data size reduction. Subsequently, a model was established based on an LSTM to predict tunneling parameters in the steady phase based on the data in the rising phase. The results demonstrated that the model is capable of predicting torque and thrust accurately. This makes it possible to adjust the TBM tunneling parameters according to current geological conditions in real time. The present study is of great significance for the tunneling efficiency and construction safety in the actual TBM construction, since it can improve its scientific and intelligent level.