Comparison between the chemistry of igneous and hydrothermal biotite in the igneous rocks of Sakhtehesar mountain

Sakhtehesar mountain is located in Urumieh-Dokhtar magmatic belt and is composed of volcanic and subvolcanic rocks (Pliocene andesite to dacite) which intruded the volcanics and pyroclastics of Paleocene age. Three alteration zones including potassic, phyllic and propylitic are recognized in the area. In this paper, the mineral chemistry of magmatic and primary biotite and the mineral chemistry of biotite in potassic and phyllic alteration zones have been studied. Investigations show that primary and secondary biotites are different from each other and hydrothermal fluids associated with the potassic alteration are distinctively different from the fluids associated with the phyllic alteration zone in the area

Estimation of Principal Induced Stresses in Longwall Faces through Seismic Moment Tensor Inversion

Summary: Induced stresses and various instabilities and deformations are resulted due to stress concentrations around a longwall face. A considerable part of the induced stresses is transferred ahead of the face and onto the adjacent T–junctions and gate roadways, which creates a zone of high stress that advances with the face advancement. A nondestructive examination based on the Seismic Moment Tensor (SMT) inversion was presented to estimate the direction of principal induced stresses around active longwall panels. The E2 longwall panel at Tabas coal mine was selected as a case study. Introduction: The induced stresses in front of the coalface can cause fractures to initiate and propagate, which in turn, lead to roof collapse. This phenomenon causes considerable problems in the face area and adjacent workings. The SMT solutions were presented for 24 seismic events at Tabas mine, which result in roof collapses and long delays in production. Most of the events occurred in the vicinity of the longwall face. The seismic waves generated during face advancement are used to estimate the SMTs through the process of SMT inversion. Methodology and Approaches: SMT inversion is the best method to calculate SMT from the recorded seismic parameters. The trick in the SMT inversion is to use long–period (low frequencies) regional distance seismic waves. The source process can be reduced to a simple delta function in space and time. The wave propagation is also simplified because filtering regional seismograms to long–periods, results in waves that have only propagated in a few wavelength cycles that can be easily predicted using relatively simple 1D layered earth models. The mathematical code is developed in MATLAB software to estimate the best solution for SMT. The resulted SMTs are decomposed in terms of its principal axes based on the eigenvalues and the directions of the eigenvectors. The directions of the principal induced stresses are then obtained based on the eigenvalues and the corresponding eigenvectors. Results and Conclusions: According to the results, it can be conceivable that roof falls similar to collapse of the immediate roof strata within the face area may be produced by compressive/tensile failure mechanisms, which result from stress concentration and gravitational forces. These are in accordance with the directions of the principal induced stresses, which obtained based on the eigenvalues and the corresponding eigenvectors. On the other hand, the shear failures are mainly resulted around the preexisting planes of weakness such as faults that intersected the coalface or excavation boundaries. In general, the maximum principal induced stress in all 24 events is mostly oriented with an acute angle with respect to horizon. This shows that the dominancy of horizontal induced stresses in occurrence of roof failures and face instabilities

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