Showing 4 results for Evaluation
Volume 1, Issue 3 (3-2004)
Abstract
(Paper pages 255-270) The groundwater protection is important in order to have a good management of water resources. The Ghazvin plain situated in west of Tehran, Iran has a critical situation in which the groundwater level declines and aquifer pollution has been observed in recent years. In this research, for evaluating the groundwater vulnerability, DRASTIC index has been used for this plain. Then, a Geographic Information System (GIS), ILWIS has been used to create a groundwater vulnerability map. The results of this study estimated DRASTIC value to be in the range of 35-108 using general DRASTIC value, almost 11% of the study area was recognized to have low feasibility, 43% moderate and 37% high and 10% very high feasibility for pollution. The DRASTIC results show a good adaptation between increasing the nitrate rate and the DRASTIC index
Ali Ghanbari, Mohsen Mojezi, Meysam Fadaee,
Volume 6, Issue 2 (4-2013)
Abstract
Construction of asphaltic core dams is a relatively novel method especially in Iran. Iran is located in a region with high seismicity risk. Therefore, many researchers have focused on the behavior of such types of dams under earthquake loading. In this research, the behavior of asphaltic core rockfill dams (ACRD) has been studied under earthquake loading using nonlinear dynamic analysis method and a new method is presented to assess seismic stability of these types of dams in earthquake conditions. Based on nonlinear dynamic analysis, the current study attempts to provide an appropriate criterion for predicting the behavior of earth and rockfill dams considering real behavior of materials together with actual records of earthquake loading. In this method, the maximum acceleration of the earthquake record (PGA) increases until instability conditions. Finally, a new criterion is presented for evaluating seismic safety of ACRDs via demonstrating curves of the crest's permanent settlement and maximum shear strain against maximum earthquake acceleration. Results of the proposed criteria can assist designers of asphaltic core dams to predict dam stability during earthquake event
Mehdi Talkhablou, Seyed Mahmoud Fatemi Aghda, Habibulah Heidari Renani,
Volume 16, Issue 2 (9-2022)
Abstract
The stabilization of underground spaces is one of the most challenging topics in engineering geology. There are several methods to determine the type of tunnel stabilization system, but most of these methods have several weaknesses. Therefore, the development of a method that comprehensively considers almost all parameters influencing tunnel stability and their interdependencies has not received sufficient attention. The aim of this research is to investigate the parameters influencing the stability of tunnels using the rock mechanics system method. In this paper, 6 tunnels with different geological characteristics were selected. The effective parameters on the primary stabilization of these tunnels were coded using the ESQ method. Subsequent analyses were performed using the RES rock engineering system method to estimate and evaluate the optimal tunnel stabilization system. The results showed that parameters such as weathering of the joint surface, backfill and joint spacing played a more effective role than other parameters. For comparison, the analyses were also carried out using the RMR rock mass ranking method. The comparison between the results of the RES and RMR methods showed that the results of the RES method are in better agreement with the actual tunnel conditions and the shotcrete thickness of the proposed stabilization system of the studied tunnels. Since there is no limit to the number of input parameters in this method and, on the other hand, the mutual influence of the parameters on each other is considered, the relationships obtained from the RES method in this research can be effectively used in engineering projects along with other methods.
Mr. Farhad Mollaei, Dr. Reza Mohebian, Dr. Ali Moradzadeh,
Volume 18, Issue 3 (12-2024)
Abstract
The brittlenessindex is one of the most important parameters in geomechanical analysis and modeling. Many methods have been proposed to estimate the brittleness index. One of the recently used methods is the intelligent method. In this paper, firstly the aim is to introduce a new algorithm using deep learning algorithms to predict the brittleness index in one of the wells of the hydrocarbon field in southwest Iran. In this article, first, the effective features for the input of the algorithms were determined using Pearson's correlation coefficient, and then using (recurrent neural network + multi-layer perceptron neural network) (LSTM + MLP) and (convolutional neural network + recurrent neural network) (CNN+ LSTM) brittleness index was estimated and the mean error value (MSE) and coefficient of determination (R2) were calculated for the training and test data. For both training and test data, both algorithms have a coefficient of determination close to 1 and a very low error. Also, in order to ensure the results of the algorithms, a part of the data was set aside as blind data, and the error and coefficient of determination were calculated for this data, and the error was MSE CNN+LSTM =26.0425, MSE LSTM+MLP =32.0751 and the coefficient of determination was R2 CNN+LSTM =0.8064, R2 LSTM+MLP =0.7615 . The results show the effectiveness of the introduced deep learning algorithms as a new method in predicting the brittleness index, and comparing the two algorithms presented, the CNN+LSTM algorithm has higher accuracy and less error.
