Showing 3 results for آنالیز حساسیت
, ,
Volume 4, Issue 1 (11-2010)
Abstract
One of the major problems in urban subway tunnels is tunnel stability analysis and determination of the safety factor, and the prediction of the settlement that caused to provide stability during the performance, and then at the time utilization structure. The objectives of this study is using different methods to predict and development of these methods by use of each other. In this paper, analyze and evaluate the stability of Tabriz Metro tunnel- Line 1 has been carried out using numerical methods, artificial neural networks and empirical equations. The two excavating methods used in Tabriz Metro tunnel- Line 1 (using machine TBM tunnel method and NATM). In the first part of this research, the excavated zone of the tunnel with NATM method has been analyzed using numerical method and surface settlement and amount of tunnel convergence in the tunnel walls have been predicted by this method. After that, surface settlement has been predicted using artificial neural networks and then it has been compared with obtained value from numerical method analysis and empirical relations. Then, based on these results, empirical relations of convergence - settlement have been modified for Tabriz Metro tunnel- Line 1. In the second part of the research, the TBM penetration rate was predicted by use of neural network which is an important parameter, when one faced with troublesome areas and is very useful to use appropriate pressure EPB for TBM.
, Gholam Lashkaripour, M Akbari,
Volume 5, Issue 2 (4-2012)
Abstract
Tunnel boring machines (TBM) are widely used in excavating urban tunnels. These kinds of machines have different types based on supporting faces and tunnel walls. One type of these machines, is the Earth Pressure Balance (EPB) type that was used in excavating the Line 1 Tunnel of Tabriz Metro. Different parameters such as geological conditions, rock mass properties, dip and machine specifications affect the efficiency of the machine. One method of predicting the efficiency of these machines is to estimate their penetration rates. In this study the value of TBM penetration rates are predicted by an artificial neural network. Predicting of this parameter is so effective for conducting in high risk regions by understanding the time of facing to these regions. The main result of this study is to forecast the penetration rate with an acceptable accuracy and to determine the effective parameters through sensitivity analysis measured by an artificial neural network.
Majid Jazebi, Mohammad Mehdi Ahmadi,
Volume 12, Issue 5 (12-2018)
Abstract
This study numerically investigates the bearing capacity of drilled shafts (bored piles) in clay using FLAC2D. The results obtained in this study are compared with centrifuge test results. The results of the empirical relationships available in the literature are compared with the results of the present numerical study. A series of analyses is also conducted to assess the effects of various soil and pile parameters on the magnitude of tip and side resistance of bored piles embedded in clay. These parameters include the soil elastic modulus, pile length and diameter, undrained shear strength, unit weight, and Poisson’s ratio of soil. Furthermore, the coupling effect of soil undrained shear strength and elastic modulus of soil on tip resistance are investigated. The results show that the lower value of soil elastic modulus results to lower effect of soil undrained shear strength. The effect of soil undrained shear strength on tip resistance is approximately constant (about 83% for a change of soil undrained shear strength between 25 to 200 kPa) for the range of elastic modulus between 20 and 180 MPa. Also, a new equation is proposed to estimate the bearing capacity factor of N*c.
