A support vector regression model for predicting tunnel boring machine penetration rates

With widespread increasing applications of mechanized tunneling in almost all ground conditions, prediction of tunnel boring machine (TBM) performance is required for time planning, cost control and choice of excavation method in order to make tunneling economical. Penetration rate is a principal measure of full-face TBM performance and is used to evaluate the feasibility of the machine and predict advance rate of excavation. This research aims at developing a regression model to predict penetration rate of TBM in hard rock conditions based on a new artificial intelligence (AI) algorithm namely support vector regression (SVR). For this purpose, the Queens Water Tunnel, in New York City, was selected as a case study to test the proposed model. In order to find out the optimum values of the parameters and prevent over-fitting, 80% of the total data were selected randomly for training set and the rest were kept for testing the model. According to the results, it can be said that the proposed model is a useful and reliable means to predict TBM penetration rate provided that a suitable dataset exists. From the prediction results of training and testing samples, the squared correlation coefficient (R2) between the observed and predicted values of the proposed model was obtained 0.99 and 0.95, respectively, which shows a high conformity between predicted and actual penetration rate. © 2014 Elsevier Ltd

Stability prediction of gate roadways in longwall mining using artificial neural networks

© 2016 The Natural Computing Applications Forum Roadways stability in longwall coal mining is critical to mine productivity and safety of the personnel. In this regard, a typical challenge in longwall mining is to predict roadways stability equipped with a reliable support system in order to ensure their serviceability during mining life. Artificial neural networks (ANNs) were employed to predict the stability conditions of longwall roadways based on roof displacements. In this respect, datasets of the roof displacements monitored in different sections of a 1.2-km-long roadway in Tabas coal mine, Iran, were set up to develop an ANN model. On the other hand, geomechanical parameters obtained through site investigations and laboratory tests were introduced to the ANN model as independent variables. In order to predict the roadway stability, these data were introduced to a multilayer perceptron (MLP) network to estimate the unknown nonlinear relationship between the rock parameters and roof displacements in the gate roadways. A four-layer feed-forward backpropagation neural network with topology 9-7-6-1 was found to be optimum. As a result, the MLP proposed model predicted values close enough to the measured ones with an acceptable range of correlation. A high conformity (R2 = 0.911) was observed between predicted and measured roof displacement values. Concluding remark is the proposed model appears to be a suitable tool for prediction of gate roadways stability in longwall mining