Prediction of hard rock TBM penetration rate based on Data Mining techniques - Martins, Francisco F. (2013)
The aim of this work is to use Data Mining tools to develop models for the prediction of hard rock tunnel boring machine (TBM) penetration rate (ROP). A database published by Yagiz (2008) was used to develop these models. The parameters of the database were the uniaxial compressive strength (UCS), an index used to quantify the brittleness and toughness and denominated peak slope index (PSI), the distance between the planes of weakness (DPW), the angle between tunnel axis and the planes of weakness (α) and the output parameter rate of penetration (ROP). The R program environment was used as a modeling tool to apply the artificial neural networks (ANN) and the support vector machine (SVM) algorithms and the corresponding models. These models were compared with two equations presented by Yagiz (2008) and Yagiz and Karahan (2011). It was concluded that the ANN model has the best performance. Moreover, these new models allowed computing the importance of the different input parameters for predicting machine performance. It was concluded that PSI is the most important parameter and UCS is the less important parameter.