حاصل امینی خوشالان

Determining the appropriate blasting pattern is important to prevent any damage to the tunnel perimeter in conventional tunneling by blasting operation in hard rocks. In this research work, the LS-DYNA software and numerical finite element method (FEM) are used for simulation of the blasting process in the Miyaneh-Ardabil railway tunnel. For this aim, the strong explosive model and nonlinear kinematic plastic material model are considered. Furthermore, the parameters required for the Johnson-Holmquist behavioral model are based on the Johnson-Holmquist-Ceramic material model relationships and are determined for the andesitic rock mass around studied tunnel. The model geometry is designed using AUTOCAD software and Hyper-mesh software is applied for meshing simulation. After introducing elements properties and material behavioral models and applying control and output parameters in LS-PrePost software, the modeling process is performed by LS-DYNA software. Different patterns of blastholes including 66, 23, and 19 holes, with diameters of 40 and 51 mm, and depths of 3 to 3.8 m are investigated by three-dimensional FEM. The borehole pressure caused by the ammonium nitrate-fuel oil (ANFO) detonation is considered based on the Jones-Wilkins-Lee (JWL) equation of state in the LS-DYNA software. The outer boundaries of the model are considered non-reflective to prevent the wave’s return. The results showed that LS-DYNA software can efficiently simulate the blasting process. Moreover, the post-failure rate of the blasting is reduced by more than 30% using the main charge with less explosive power and reducing the distance and diameter of contour holes.

In this research work, a comprehensive study is conducted to predict flyrock as a typical and undesirable phenomenon occurring during the blasting operation in open-pit mining. Despite the availability of several empirical methods for predicting the flyrock distance, the complexity of flyrock analysis has resulted in the low performance of these models. Therefore, the statistical and robust artificial intelligence techniques are applied for flyrock prediction in the Sungun copper mine in Iran. For this purpose, the linear multivariate regression (LMR), imperialist competitive algorithm (ICA), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN) methods are applied to predict flyrock with effective parameters including the blasthole diameter, stemming, burden, powder factor, and maximum charge per delay. According to the attained results, the ANN model with the structure of 5-8-1, Levenberg-Marquardt as the learning algorithm, and log-sigmoid (logsig) as the transfer functions are selected as the optimal network with the RMSE and R2 values of 5.04 m and 95.6% to predict flyrock, respectively. Also it can be concluded that the ICA technique has a relatively high capability in predicting flyrock, with the LMR and ANFIS models placed in the next. Finally, the sensitivity analysis reveal that the powder factor and blasthole diameters have the most importance on the flyrock distance in the present work.

Nowadays, earth pressure balance tunnel boring machines (EPB-TBMs) are favorably used in urban mechanized tunneling projects. The availability of these machines is rather low, so their performance, time planning, maintenance scheduling and cost control may need further improvements. The reliability, availability and maintainability (RAM) of any machine could be analyzed by evaluating the actual performance of that machine or system and thereby minimizing maintenance costs. In current research work, RAM analysis of the electrical system of the EPB-TBM in line 1 of Tabriz metro in Iran is performed. For this investigation, failure and repair data were collected during about 26 months of machine operation. Statistical tests including trend and serial correlation tests indicated that the data are independently distributed, consequently the statistical technique was used for modeling. The data analysis also revealed that the time between failures (TBFs) and time to repairs (TTRs) data fit to the Lognormal (3P) and Gamma (3P) distributions, respectively. Reliability analysis showed that there is approximately 80.1% chance for the electrical system of TBM machine to be operational for 15 h without any failure. Moreover, the maintainability curve indicated that most of the failures have been repaired in less than 1 hour and that it is more likely that 80% of these failures would be repaired in 1.88h. Also the availability of electrical system of this machine is found to be equal to 97.8%.

The Cheraghvays dam is located in 17Km away from the west of Saqez in Kurdistan province. Basically, the arrangement of grout holes was based on the quality of bedrock, so the grout holes were arranged at 2 m intervals and 10.5m final depth. Also, the consolidation grouting process was conducted from the bottom to the top of the grout holes.Particular attention to the geological structures in the initial studies and construction of dams is important which locking of it can be caused some problems. Therefore, in this paper, permeability and cement take of 1438 grouted holes were studied and interpreted by using geological view of the beneath clay core of Cheraghvays dam. Finally two parameters, permeability and cement take of mentioned grouted holes were compared and their nonconformity is interpreted by geological view and imperfection of Lugeon test.