Journal of Engineering Geology

Earthquake possibility space of Ahvaz city based on intuitionistic fuzzy theory

sasan motaghed

In this study, we present the Seismic Hazard Possibility Space (SHPS) for the city of Ahvaz. To achieve this, we applied the intuitionistic fuzzy method to weigh the logic tree used in the hazard analysis and constructed the SHPS based on expert opinions regarding the degrees of membership and non-membership. Hazard disaggregation was performed by through the concept of intuitionistic fuzzy sets, leading to the development of an intuitionistic fuzzy of an Intuitionistic Fuzzy Logic Tree (IFLT). The SHPS includes both the degree of membership and non-membership for pathways contributing to hazard generation. The SHPS illustrates the acceptance, non-acceptance, and ambiguity associated with potential hazard values from an expert perspective, thus assisting analysts in selecting appropriate hazard values. According to the numerical results of our analysis in the Ahvaz region, the seismic hazard is located in an uncertainty (unacceptability) zone, indicating that experts have low confidence in the results of the probabilistic seismic hazard analysis (PSHA) for Ahvaz. In addition, the hazard is characterized by an "unconfident zone". This finding indicates that experts are fairly confident in the results of the analysis for Ahvaz. This finding implies that the models and parameters used in the PSHA for this region are not accepted by experts, and further efforts are needed to identify or develop appropriate models and accurate parameters specific to the area. In conclusion, this research demonstrates how intuitionistic fuzzy sets can be used to construct SHPS, providing a novel framework for quantifying uncertainty and expert opinion in hazard assessment.

Proposing a Deep Learning Algorithm for Estimating the Brittleness Index Using Conventional Log Data in the Asmari Formation of a Southwestern Iranian Oil Field

Reza Mohebian

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.

Water quality assessment of Qarasu River using IRWQIsc index

Behzad Rahnama

Many researchers believe that providing safe water, sanitary disposal and optimal management are the three axes of health, and in all these cases, while paying attention to the process of doing work, continuous control should also be done. This study was designed and implemented with the aim of seasonally investigating the physicochemical and microbial water quality of Qarasu River in Golestan province using the IRWQIsc index. 6 sampling stations were identified for Qarasu River and sampling was done once every month in four seasons of 1400. The measured parameters include pH, BOD, COD, dissolved oxygen (DO), electrical conductivity (EC), ammonium (NH₄), nitrate (NO₃), phosphate (PO₄), total hardness (TH), turbidity and total suspended solids. It was a stool form. According to the measured parameters, Iran's surface water quality index IRWQISC was calculated. The results of the study based on the index showed that the quality of this index for all stations in all seasons was between 70.5 and 14.7 and according to the IRWQISC index, it was in five good categories (70-1.85), relatively good. (55-1/70), relatively bad (30-44-9), bad (15-29-9) and very bad (less than 15). The influencing parameters were total suspended solids, turbidity, nitrate, temperature and fecal coliform. It can be concluded that the amount of 70.5 with good quality is related to (Tuskestan village) in winter and the amount of 7.14 with very bad quality is related to (Pol Qara Tepe) in summer that the quality of the river water in The Gorgan to Aqqla road bridge station (Qorban Abad) is in bad condition in all seasons due to the entry of urban and industrial pollutants into this station, and Tuskestan village station has good and relatively good quality in most seasons because Tuskestan is in It is located in high altitudeand the entrance of clean running water into thisarea is more and it is far from industrial and urban pollutants.

The Effect of Lime and Coal Ash Mixture on the Geotechnical Properties of Clay for use in Engineering Embankments

Rouzbeh Dabiri

Recently, the demand for rapid and cost-effective infrastructure development has led to the has led to the development of various soil improvement techniques, including stabilization. Studies on the incorporation of mineral materials such as lime and coal ash into soil stabilization have been carried out in several countries, and these studies have shown promising results. Given the beneficial properties or properties of coal ash, together with its availability and cost-effectiveness, the combination of coal ash with lime for clay soil stabilization is a viable option. This study evaluates lime and coal ash on the behavior and geotechnical properties of clay soils. The evaluation includes plasticity index (PI), compaction, uniaxial compressive strength, California bearing ratio (CBR) and direct shear tests, and direct shear tests. To achieve this, the process began with the mixing of clay with varying percentages of hydrated lime (4%, 6% and 7%), followed by a 28-day curing period for the samples. Coal ash was then added at different (5%, 15%, 25% and 50%) was incorporated into the clay and also cured for 28 days. In the final stage, the optimum amount of hydrated lime was determined, an amount of hydrated lime, equivalent to the amount of coal ash used, was added to the clay and the mixture was cured for a further 28 days. The results indicate that A mixture of 7% hydrated lime and 50% coal ash, after 28 days of curing, is an optimum combination for stabilizing the clay in the study area. This combination increased the uniaxial compressive strength by 1.87 times, the shear strength by 1.34 times and the CBR value by 6.4 times, making it suitable for use in the for use in the construction of pavement layers.

Application of 2D multistage median filter for reducing random seismic noise

Ehsan Pegah

Random noise reduction has always been one of the most important issues in seismic data processing. This study investigates one of the most effective random noise reduction methods, the 2D multi-stage median filter. This filter is applied to seismic data by applying a series of 1D median filters in different directions and then selecting the output value corresponding to the center of the 2D window. By applying a 2D multi-stage median filter to both synthetic and real data, it is shown that the filter can effectively attenuate random spike-like noise in both pre-stack and post-stack data. Similarly, based on spectral analysis, it is shown that this filter does a good job of reducing the level of high frequency random noise in both synthetic and real data. In this study, a 2D median filter is applied to synthetic data containing random noise with a density of 10%. Since increasing the filter length can damage useful signals in addition to attenuating random noise, it is important to specify an appropriate filter length. For synthetic data, the error ratio plot shows that a filter length of 9 points is appropriate for the first stage. In the second stage, a 2D median filter with a length of 7 points was applied to the output of the first stage filter. The effect of this filter on random noise suppression can then be observed by spectral analysis. In addition, median filters of 7 points and 5 points were applied to the pre-stack and post-stack real data, respectively. The effect and efficiency of this filter is assessed by examining the resulting difference plots, sections and spectral analysis.

Pseudo Static Study of Tunnel with Mohr-Coulomb and Hardening Models Located in Soft Soil

Masoud Amelsakhi

Tunnels behave differently under seismic conditions due to their geometric shape, geotechnical parameters and installation depth. Although tunnels are less damaged compared to surface structures, they are still damaged during earthquakes. Various experiences have proved this matter, so researchers are concerned to study the seismic behavior of tunnels. In this research, circular tunnels are discussed under static and pseudo-static loading. In addition to different pseudo static earthquake factors, internal soil friction angle, soil behavior models, sliding and non-sliding of tunnel wall are also studied. Three different soft, medium and stiff soil conditions are studied. Some results show that in all three soil conditions and two soil behavior models, Mohr-Coulomb and hardening soil, the horizontal displacements increase due to the increase of the pseudo static earthquake factor. It should be noted that softening of the soil increases the horizontal displacements.

Assessment of hydrochemical characteristics and quality of the Garmabdasht River, Golestan Province, NE Iran

Giti forghani

The Garmabdasht River as the first tributary of the Qarasu River, flows through the city of Gorgan and eventually flows into Gorgan Bay. In order to study the hydrochemistry and to assess the water quality, 10 water samples were collected in June 2022. Physicochemical properties (pH, electrical conductivity, total dissolved solids), major ion concentrations, and microbiological parameters (dissolved oxygen content, biological oxygen demand, chemical oxygen demand, and coliform bacteria) were measured by standard methods. The obtained results show that the pH of the water samples varies between 7.5 and 8.5 and the electrical conductivity of water samples varied between 376 and 665 µs/cm. In terms of hardness, water samples were classified as hard and very hard. The concentrations of the major ions, phosphate and nitrate were within the permissible range for drinking usage. By calculating the ionic ratios and drawing the Durov diagram, it was found that the water chemistry was mainly controlled by the dissolution process. The position of the samples on the Piper diagram shows that the type and facies of the river water samples were calcium bicarbonate, magnesium bicarbonate and calcium sulphate. According to the Wilcox diagram, the Garmabdasht river water was suitable for irrigation. The residual sodium carbonate and sodium percentage values confirm this conclusion; however, based on the magnesium hazard index, the studied samples were not suitable for irrigation. The values of dissolved oxygen in all samples were within the permissible limit. The amounts of biological oxygen demand and chemical oxygen demand in some stations exceeded the permissible limit due to the influx of livestock and agricultural effluents. The obtained results show that the samples were microbially polluted, which may induce the health problem in the local population. The values of NSFWQI also shows that, except for the upstream samples of S1 and S2, the quality of the studied samples for drinking is in the bad to medium class.