Predicting Cutterhead Torque for TBM based on Different Characteristics and AGA-Optimized LSTM-MLP

Adaptive adjustment of excavation parameters makes a significant role in the process of tunneling by tunnel boring machine (TBM), which ensures the tunneling carried out safely and efficiently. Though substantial effort has been devoted to this area, there is still a lack of a comprehensive method for TBM data analysis. In this paper, we analyzed the TBM data from different perspectives. The data source is from the Songhua River Water Conveyance Project. In order to facilitate the processing and analysis of the data, we proposed the concepts of rising characteristic interval (RCI) and stable characteristic interval (SCI), which are the first 30 seconds of the rising stage and one sixth of the center part of the stable stage respectively. As a key parameter, the cutterhead torque (T), which reflects the interaction between the cutter and the soil, is selected as our prediction target. In order to forecast the value of T in the SCIs, the time series characteristic and the non time series (mean and variance) characteristic of the important excavation parameters in the RCIs are analyzed. A sequential combination of long short-term memory (LSTM) and multi-layer perceptrons (MLP), LSTM-MLP for short, is used to make a comprehensive analysis of the two characteristics. Notably, adaptive genetic algorithm (AGA) was employed to optimize the topology structure and the hyper parameters of our neural network, which ensures the convergence of the basic genetic algorithm and maintains the diversity of the population at the same time. The experimental results indicate that, LSTM-MLP performs better in comparison with LSTM network and backpropagation neural network (BPNN, a kind of MLP). Our work provides a reference for the control and optimization of TBM’s excavation parameters. To make our results fully reproducible, all the relevant source codes and the preprocessed dataset are publicly available at https://github.com/Dandelionslove/LSTM MLP for TBM.