Showing 6 results for Predict
, 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.
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Volume 6, Issue 1 (11-2012)
Abstract
Prediction of location of future earthquakes with event probability is useful in reduction of earthquake hazard. Determination of predicted locations has attracted more attention to design, seismic rehabilitation and reliability of structures in these sites. Many theories were proposed in the prediction of time of occurrence of earthquake. There is not a method for prediction time of future earthquakes. Many studies have been done in the prediction of magnitude of earthquakes, but there are not any investigations on prediction of earthquake hazard zonation. In this study, the locations that have probability of the event of future earthquake have been predicted by artificial neural networks in Qum and Semnan. Neural networks used in this study can extract to complicate properties of patterns by receipting the interval patterns. Furthermore, the map of earthquake hazard zonation has been drawn. Properties of occurred earthquake were collected since 1903. The most probable event of earthquake in Qum has been predicted 31.6% in center, and 28.9% in north of Semnan
Reza Ghaderi -Meybodi, Gh Khanlari,
Volume 6, Issue 2 (4-2013)
Abstract
One of the geotechnical hazards in the tunnels under high overburden and high in situ stresses is the phenomenon of rock burst. Rock burst is a typical geologic phenomenon caused by excavation in rock masses. In this phenomenon, because of stress released and explosion in rock masses, they are broken as large and small pieces and are distributed, so that leads to damage of peoples or equipments. Therefore, familiar with this phenomenon and its mechanism of occurrence, is need to analyze this issue. The second part of water supply Karaj-Tehran tunnel with a length of 14 km and about 4.5 m diameter is located in Tehran province. Rock burst analysis has been carried out in the tunnel from kilometer 6 to 9.5 that is critical section because of high overburden (up to 800 m) and presence of faults and crushed zones. In this paper, for predicting rock burst in the critical section of second part of Karaj-Tehran tunnel, four criteria including, Strain energy, Rock brittleness, Seismic energy and Tangential stress criterion are used. Analysis results show that units with high overburden have high possibility of rock burst.
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Volume 12, Issue 5 (12-2018)
Abstract
In urban areas, it is essential to protect the existing adjacent structures and underground facilities from the damage due to tunneling. In order to minimize the risk, a tunnel engineer needs to be able to make reliable prediction of ground deformations induced by tunneling. Numerous investigations have been conducted in recent years to predict the settlement associated with tunneling; the selection of appropriate method depends on the complexity of the problems. This research intends to develop a method based on Artificial Neural Network (ANN) for the prediction of tunnelling-induced surface settlement. Surface settlements above a tunnel due to tunnel construction are predicted with the help of input variables that have direct physical significance. The data used in running the network models have been obtained from line 2 of Mashhad subway tunnel project. In order to predict the tunnelling-induced surface settlement, a Multi-Layer Perceptron (MLP) analysis is used. A three-layer, feed-forward, back-propagation neural network, with a topology of 7-24-1 was found to be optimum. For optimum ANN architecture, the correlation factor and the minimum of Mean Squared Error are 0.963 and 2.41E-04, respectively. The results showed that an appropriately trained neural network could reliably predict tunnelling-induced surface settlement.
Dr Nasrollah Eftekhari, Dr Sasan Motaghed, Dr Lotfallah Emadali, Dr Hasi Sayyadpour,
Volume 16, Issue 2 (9-2022)
Abstract
In the variability of earthquake hazard analysis results, ground motion prediction equations play an important role. Selection of appropriate prediction relationships for the region can lead to stability and accuracy of earthquake hazard analysis results. In this study, different prediction relationships were investigated and analyzed for earthquake hazard analysis in Ahvaz city. These relationships were ranked based on the criteria of logarithmic probability, Euclidean distance and deviation information in different periods. Then the most efficient relationships were selected by data envelopment analysis (DEA) method on the basis of differences in the obtained results. Out of 67 possible relationships, 5 were identified as suitable relationships for earthquake hazard analysis in the Ahvaz urban area. Then, a special efficiency criterion was used to determine the weight of these relationships. The results of this study can help to reduce to a large extent the uncertainties involved in analyzing the seismic hazard of the area studied.
Dr Mohammad Fathollahy, Mr. Habib Rahimi Menbar, Dr. Gholamreza Shoaei,
Volume 16, Issue 3 (12-2022)
Abstract
Shear strength parameters are important for assessing the stability of structures, and are costly to calculate using conventional methods. In this research, simple geotechnical techniques and artificial intelligence were used to calculate the angle of internal friction and soil cohesion without the need for more complex testing. To this end, intact samples from 14 boreholes in Bandar Abbas, which had undergone primary geotechnical testing and direct cutting, were selected and used to train neural networks. 195 networks were trained in in this research. To achieve the best performance, feedforward neural networks were first trained in single and double layer modes with a low number of neurons in the middle layer, and the TRAIN BR function was selected due to the high ratio of R (0.97). Then, by incorporating additional layers, the Median model was trained using configurations of 3, 4, and 5 layers, each with varying numbers of neurons in the intermediate layer (50, 40, 30, 20, and 10). The results show that the four-layer MLP network gives the best results, for this mode R training 1, the test R is 0.90 and the total R is 0.98. Finally, to validate the neural network, 15 samples were selected and the input parameters of the network were trained in the optimal states of 2, 3, and 4 layers, then the output of the network was evaluated. For cohesion prediction, the neural network in 4-layer mode (R2=0.99) and 2, 3 and 4-layer networks (R2=0.99) have the best output for the friction angle.
