Performance Prediction of Hard Rock TBM Based on Extreme Learning Machine

Abstract

Performance prediction of hard rock TBM is the key to the successful tunnel excavations. A series of TBM performance prediction models have been developed since 1970s. The empirical, semi-empirical models such as CSM, NTNU models have their limitations, because the models are unable to completely reflect the correlation between the parameters of the models and penetration rate (PR). Researchers propose some models based on data-driven, like neural network model, which have the over fitting problem generally. This paper proposes a new on-line prediction model with incremental learning method based on extreme learning machine (ELM). This algorithm randomly chooses hidden nodes and analytically determines the output weights of single-hidden layer feed forward neural networks (SLFNs). Unlike neural network model, over fitting does not need to be concerned and the iterative learning steps are not required in ELM. The database used to validate the model is collected from the Queens Water Tunnel #3, Stage 2, New York City, USA. Compared with other methods such as PLS, GP, LSSVM, ELM prediction model tends to provide precise prediction at extremely fast learning speed.