Prediction of TBM penetration rate based on the model of PLS-FNN

Many researches on the prediction of the penetration rate of the tunneling boring machine (TBM) have been carried out. The prediction of the penetration rate will contribute to reduce the danger of TBM construction, decrease the cost and provide the support for the construction planning. Most methods for predicting penetration rate rely on the fixed equation relationship between the input parameters and output parameters. In this paper, we build up a dynamic model. We mainly make use of the partial least squares regression algorithm (PLS) and the structure of the fuzzy-neuron network (FNN) to build up the model. All the data should be normalized. We randomly select 120 groups of the data from TBM construction as the training data and 33 groups of data as the testing data. The training and testing results are analyzed by the mean square error and the correlation coefficient. At the same time, we compare the prediction of the PLS-FNN model with the prediction of the FNN model. The simulation result shows that the PLS-FNN model has the good performance for the prediction.