University of BirjandJournal of Geomine2981-08763220250601Statistical analysis and validation of the cement slurry flow rate calculated by analytical and numerical models in the grouting process8292357210.22077/jgm.2025.9533.1051ENMajidMasoudiDepartment of Civil Engineering, Technical and Vocational University (TVU), Tehran, IranAlirezaGhasemiGraduated from Mining Engineering, University of Kashan. IranSaeedSoltani MohammadiAssociate Professor, Department of Mining Engineering, Faculty of Engineering, University of Kashan0000-0002-8390-1917Journal Article20250603Grouting operations are widely used to reduce leakage and increase the strength and consolidation of jointed rock in construction sites. One key challenge in these operations is accurately estimating the volume of cement slurry required. Precise estimation enables the grouting process to be optimized both technically and economically, and it also allows for predicting grout penetration in sections with similar conditions based on the results. This research focuses on estimating the cement slurry volume using both analytical and numerical models. Five analytical models are introduced, each assuming the presence of a joint with a constant aperture bb, and calculating the penetration volume accordingly. For numerical modeling, UDEC software was employed. In this approach, the cross-section of the block, the borehole, and the grouting joint were modeled, followed by the application of grout properties and the ultimate grouting pressure. Geotechnical and grouting data from the Seymareh Dam site were used as a case study. To validate the models, statistical analyses were conducted under varying conditions of rock quality factor and grouting section depth. Based on the overall results, analytical model number 4 demonstrated the best accuracy in estimating grout volume, while model number 5 showed the weakest performance. Therefore, if a single model is to be selected, model number 4 is recommended.https://jgm.birjand.ac.ir/article_3572_842424a1d0595b76ec4fa03c46e8d755.pdfUniversity of BirjandJournal of Geomine2981-08763220250601Use of Well Logging Data to Estimate Fluid Saturation Based on Artificial Neural Network Algorithms93105357310.22077/jgm.2025.9704.1052ENAdelEidpoorDepartment of Petroleum Engineering, Kho.C., Islamic Azad University, Khomeinishahr, IranMojtabaRahimiDepartment of Petroleum Engineering, Kho.C., Islamic Azad University, Khomeinishahr, Iran; Stone Research Center, Kho.C., Islamic Azad University, Khomeinishahr, Iran0000-0002-2567-0508AliHeidariDepartment of Mechanical Engineering, Kho.C., Islamic Azad University, Khomeinishahr, IranJournal Article20250708 Among all the methods used for determining fluid saturation of the reservoir rock, the ability of neural networks to predict fluid saturation in reservoir rock is of great interest to researchers. This study gathers the necessary data for estimating this important reservoir parameter and the variables involved in the process. Afterward, artificial neural networks (ANNs) and particle swarm optimization (PSO) algorithms are combined to provide a proper and accurate model for estimating water saturation. This combination provides an outstanding model in which fluid saturation distribution at any point in one of Iran’s carbonate oil reservoirs can be obtained. To predict the water saturation value as the model output, several input parameters, including depth, gamma ray, resistance, neutron, micro-spherical resistance, and spontaneous potential logs, are employed. The multi-layer perceptron neural network (MLP) and radial basis function neural network (RBF) are the two models used, and the accuracy of each model is examined. Although the relationship between fluid saturation in the reservoir and logging information is completely nonlinear, these two artificial intelligence (AI) models can very well recognize this nonlinear relationship and provide great predictions with high correlation coefficient (R) values and low average absolute relative deviation (AARD) and root mean square error (RMSE) values. The values of R, AARD, and RMSE for the MLP model are obtained as 0.9739, 33.24, and 0.0824, respectively. Those for the RBF model are 0.9986, 7.47, and 0.0024, respectively, reflecting that the RBF model is superior to the MLP model due to its higher R value and lower AARD and RMSE values.https://jgm.birjand.ac.ir/article_3573_8baca01b732cf56f7ce83df216514363.pdfUniversity of BirjandJournal of Geomine2981-08763220250601Prediction of Slope Stability in Open-Pit Mines Using Intelligent Algorithms: SVM and RF Optimized by Genetic Algorithm106116357410.22077/jgm.2025.9736.1055ENHassanHosseinzadehDepartment of Mining Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.0009-0008-9644-9094GholamrezaNowrouziDepartment of Mining Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran0000-0002-6549-9741ElhamRafieeniaDepartment of Mining Engineering, Faculty of Engineering, University of Sistan & Baluchestan, Sistan & Baluchestan, IranJournal Article20250712 Monitoring and predicting slope stability in open-pit mines plays a critical role in enhancing safety, minimizing losses, and improving operational efficiency. Slope instability can lead to severe and often irreversible economic, human, and environmental consequences. Traditional stability analysis methods, such as limit equilibrium and numerical modeling, face limitations due to geometric simplifications, linear assumptions, and their inability to capture complex patterns—factors that reduce their effectiveness in real-world conditions. In recent years, machine learning approaches have emerged as powerful tools in geotechnical analysis. This study aims to predict the stability status of open-pit mine slopes using machine learning models, specifically Support Vector Machine (SVM) and Random Forest (RF). To improve the accuracy of these models, their parameters were optimized using a Genetic Algorithm (GA). The dataset used includes geotechnical and geometric features influencing slope stability, obtained from field investigations and documented sources. The results indicate that the RF–GA hybrid model outperforms the SVM–GA model, achieving 93% accuracy with an AUC of 0.93, compared to 86% accuracy and an AUC of 0.86 for the SVM–GA model. Moreover, the RF model demonstrated higher sensitivity in identifying stable slopes and reduced the number of false negatives. These findings highlight the strong potential of the RF–GA model in delivering reliable predictions and supporting decision-making in slope stability management. The integration of intelligent algorithms with local data offers a robust alternative to traditional methods in geotechnical engineering.https://jgm.birjand.ac.ir/article_3574_1b9f38268c50805669fd8caf8f3cc84a.pdfUniversity of BirjandJournal of Geomine2981-08763220250601Measurement of Vibrations Due to Blasting in Angouran Surface and Underground Mine on the Stability of Underground Spaces117125359010.22077/jgm.2025.9800.1057ENMostafaGhadimiIran Mineral Materials Procurement and Production Company (IMPASCO)0009-0005-5479-7195Journal Article20250722 The effect of blast-induced ground vibrations on mines and on adjacent structures and facilities is an example of the adverse consequences of the mining cycle. In order to control the vibrations, and also, manage the adverse effects, the measurement and modeling of the concerned wave attenuation are necessary. Usually, by implementing several trial blasts and analyzing corresponding ground vibrations, we can determine the related attenuation model for different sites and mines. The purpose of this research is the measurement of vibration due to blasting in Angouran surface and underground mines on the stability of underground spaced. In this research, blast-induced ground vibrations obtained from the Angouran surface and underground mines are closely examined. In the first phase, the peak particle velocity (PPV) is estimated based on the maximum charge amount per delay (Q) and the distance (R). Therefore, the stability of underground mines in relation to monitoring using common blast patterns in the Angouran open-pit mine (with a diameter of 127 mm, a burden of 4.2 m, and a spacing of 5 m) has been measured using four seismographs. The results indicate that the maximum ground vibrations resulting from open-pit blasting at the underground mine exceed the permissible vibration standards. The vibration frequencies measured in this phase were estimated to be between 6 and 90 Hz. The second phase of the studies was conducted using a new blast pattern (with a diameter of 101 mm, a burden of 3 m, and a spacing of 3.5 m) using the Power Dock method. The results of the analyses showed an acceptable reduction in seismic waves (in the range of 9 to 53) and minimal impact on the underground mine. To reduce possible risks, the managers decided to continue mining operations with the Power Dock method. It was decided to install instruments in the underground mine to monitor stability.https://jgm.birjand.ac.ir/article_3590_da6ea77475918a3d83c7e49223d453cc.pdfUniversity of BirjandJournal of Geomine2981-08763220250601Artificial Intelligence Methods for Estimation of Formation Porosity from Well Logging Data126139364110.22077/jgm.2025.9727.1054ENRezaRookiBirjand University of Technology, Birjand, IranMojtabaRahimiDepartment of Petroleum Engineering, Kho.C., Islamic Azad University, Khomeinishahr, Iran; Stone Research Center, Kho.C., Islamic Azad University, Khomeinishahr, Iran0000-0002-2567-0508Journal Article20250711Porosity is an essential rock property reflecting the capacity of the reservoir to store hydrocarbons, making it a critical parameter in the exploration and development of oil and gas resources. Well logging is a fundamental technique in the oil and gas industry for estimating the porosity of subsurface formations. Well logging methods do not directly measure porosity, but instead record physical parameters (e.g., bulk density, acoustic travel time, hydrogen index) that can be empirically or theoretically related to the porosity of the rock. The artificial intelligence (AI) methods of Backpropagation Neural Network (BPNN), General Regression Neural Network (GRNN), and Support Vector Machine (SVM) were adopted to estimate porosity from well-logging data points using MATLAB software. For this purpose, 70% of the data was used for training, and 30% of the data was used for testing these AI methods. The well logging dataset obtained from one of the Kaggle datasets belongs to the Peninsular Malaysia hydrocarbon field. The comparison of the porosity values predicted by the AI methods and the actual values indicated that the three AI methods predict porosity values with great accuracy (with <em>R</em> values all equal or very close to one). However, the BPNN method has a smaller error in estimating porosity compared to the other two AI methods, suggesting that BPNN outperforms the other two AI techniques in this study.https://jgm.birjand.ac.ir/article_3641_22c5a901070d1c2ad33e821d071ae97e.pdfUniversity of BirjandJournal of Geomine2981-08763220250601Hydrogeological Risk Assessment of Tunnel Excavation Using a Hybrid AHP-AI Model: A Case Study140146361610.22077/jgm.2025.9786.1056ENSaeedSamieiuniversity of kashanAliAalianvari2 Associate Professor, Faculty of Engineering, Mining Engineering Department, university of kashan, ali_alianvari@kashanu.ac.irJournal Article20250719This study investigates the hydrogeological impacts of the Bazideraz water transfer tunnel on the spring discharge of the Sarab Garm Spring in western Iran, a critical local water resource. A hybrid methodology is developed, integrating the Analytic Hierarchy Process (AHP) with Artificial Intelligence (AI) techniques, which are employed to refine the weights assigned to critical parameters. Fourteen key factors—including karst development, rock permeability, and fault influences—are assessed to quantify the risk of reduced spring discharge due to tunnel excavation. The AHP facilitates structured pairwise comparisons, while AI methods enhance the validation of these weights. The calculated risk index ® for Sarab Garm Spring is found to be 257, categorizing it within the moderate-to-high risk range and indicating a significant likelihood of flow reduction if no mitigation measures are implemented. Sensitivity analysis reveals that karst potential and rock mass permeability are the most influential factors affecting risk. These findings underscore the importance of geological structures, karst features, and aquifer-tunnel interactions in assessing spring vulnerability. This research provides a robust, data-driven tool for proactive pre-construction risk assessment. It supports sustainable tunnel design by informing targeted mitigation strategies to ensure water security and environmental protection in complex hydrogeological settings.https://jgm.birjand.ac.ir/article_3616_78d69f40906679a976dc4d45cebffbe6.pdf