• Geomechanics and Engineering
• /
• v.33 no.3
• /
• pp.231-242
• /
• 2023

This paper represents a comparative study in which Gene Expression Programming (GEP), Group Method of Data Handling (GMDH), and multiple linear regressions (MLR) were utilized to derive new equations for the prediction of time-dependent bearing capacity of pile foundations driven in cohesive soil, technically called pile set-up. This term means that many piles which are installed in cohesive soil experience a noticeable increase in bearing capacity after a specific time. Results of researches indicate that side resistance encounters more increase than toe resistance. The main reason leading to pile setup in saturated soil has been found to be the dissipation of excess pore water pressure generated in the process of pile installation, while in unsaturated conditions aging is the major justification. In this study, a comprehensive dataset containing information about 169 test piles was obtained from literature reviews used to develop the models. to prepare the data for further developments using intelligent algorithms, Data mining techniques were performed as a fundamental stage of the study. To verify the models, the data were randomly divided into training and testing datasets. The most striking difference between this study and the previous researches is that the dataset used in this study includes different piles driven in soil with varied geotechnical characterization; therefore, the proposed equations are more generalizable. According to the evaluation criteria, GEP was found to be the most effective method to predict set-up among the other approaches developed earlier for the pertinent research.

• Geomechanics and Engineering
• /
• v.33 no.6
• /
• pp.567-581
• /
• 2023

The prediction and achievement of a proper rock fragmentation size is the main challenge of blasting operations in surface mines. This is because an optimum size distribution can optimize the overall mine/plant economics. To this end, this study attempts to develop four improved artificial intelligence models to predict rock fragmentation through cascaded forward neural network (CFNN) and radial basis function neural network (RBFNN) models. In this regards, the CFNN was trained by the Levenberg-Marquardt algorithm (LMA) and Conjugate gradient backpropagation (CGP). Further, the RBFNN was optimized by the Dragonfly Algorithm (DA) and teaching-learning-based optimization (TLBO). For developing the models, the database required was collected from the Midouk copper mine, Iran. After modeling, the statistical functions were computed to check the accuracy of the models, and the root mean square errors (RMSEs) of CFNN-LMA, CFNN-CGP, RBFNN-DA, and RBFNN-TLBO were obtained as 1.0656, 1.9698, 2.2235, and 1.6216, respectively. Accordingly, CFNN-LMA, with the lowest RMSE, was determined as the model with the best prediction results among the four examined in this study.

• Geomechanics and Engineering
• /
• v.35 no.4
• /
• pp.425-436
• /
• 2023

An effective tool for researching actual problems in geotechnical and mining engineering is to conduct physical modeling tests using similar materials. A reliable geometric scaled model test requires selecting similar materials and conducting tests to determine physical properties such as the mixing ratio of the mixed materials. In this paper, a method is proposed to determine similar materials that can reproduce target properties using a polynomial model based on experimental results on modeling materials using a gypsum-sand mixture (GSM) to simulate rocks. To that end, a database is prepared using the unconfined compressive strength, elastic modulus, and density of 459 GSM samples as output parameters and the weight ratio of the mixing materials as input parameters. Further, a model that can predict the physical properties of the GSM using this database and a polynomial approach is proposed. The performance of the developed method is evaluated by comparing the predicted and observed values; the results demonstrate that the proposed polynomial model can predict the physical properties of the GSM with high accuracy. Sensitivity analysis results indicated that the gypsum-water ratio significantly affects the prediction of the physical properties of the GSM. The proposed polynomial model is used as a powerful tool to simplify the process of determining similar materials for rocks and conduct highly reliable experiments in a physical modeling test.

• Geomechanics and Engineering
• /
• v.37 no.2
• /
• pp.149-166
• /
• 2024

The acoustic emission (AE) technique is utilized to estimate the rock failure status in underground spaces. Understanding the AE characteristics under loading conditions is essential to ensure the reliability of AE monitoring. The AE characteristics depend on the material properties (p-wave velocity, density, UCS, and Young's modulus) and damage stages (stress ratio) of the target rock mass. In this study, two groups of granite specimens (based on the p-wave velocity regime) were prepared to explore the effect of material properties on AE characteristics. Uniaxial compressive loading tests with an AE measurement system were performed to investigate the effect of the rock properties using AE indices (count index, energy index, and amplitude index). The test results were analyzed according to three damage stages classified by the stress ratio of the specimens. Count index was determined to be the most suitable AE index for evaluating rock mass stability.

• Geomechanics and Engineering
• /
• v.37 no.4
• /
• pp.359-370
• /
• 2024

The primary goal of this study is to evaluate the migration of fine granular materials into overlying layers under cyclic loading using a modified large-scale triaxial system as a physical model test. Samples prepared for the modified large-scale triaxial system comprised a 60 mm thick gravel layer overlying a 120 mm thick subgrade layer, which could be either tailings or railway sand. A quantitative analysis of the migration of fine granular materials was based on the mass percentage and grain size of migrated materials collected in the gravel. In addition, the cyclic characteristics, i.e., accumulated axial strain and excess pore water pressure, were evaluated. As a result, the total migration rate of the railway sand sample was found to be small. However, the total migration rate of the sample containing tailings in the subgrade layer was much higher than that of the railway sand sample. In addition, the migration analysis revealed that finer tailings particles tended to be migrated into the upper gravel layer easier than coarser tailings particles under cyclic loading. This could be involved in significant increases in excess pore water pressure at the last cycles of the physical model test.

• Geomechanics and Engineering
• /
• v.36 no.3
• /
• pp.247-258
• /
• 2024

Tailings are waste materials of mining operations, consisting of a mixture of clay, silt, sand with a high content of unrecoverable metals, process water, and chemical reagents. They are usually discharged as slurry into the storage area retained by dams or earth embankments. Poor knowledge of the hydro-mechanical behaviour of tailings has often resulted in a high rate of failures in which static liquefaction has been widely recognized as one of the major causes of dam collapse. Many studies have dealt with the static liquefaction of coarse soils in saturated conditions. This research provides an extension to the case of silty tailings in unsaturated conditions. The static liquefaction resistance was evaluated in terms of stress-strain behavior by means of monotonic triaxial tests. Its dependency on the preparation method, the volumetric water content, the void ratio, and the degree of saturation was studied and compared with literature data. The static liquefaction response was proved to be dependent mainly on the preparation technique and degree of saturation that, in turn, controls the excess of pore pressure whose leading role is investigated by means of the relationship between the -B Skempton parameter and the degree of saturation. A preliminary interpretation of the static liquefaction response of Stava tailings is also provided within the Critical State framework.

• Geomechanics and Engineering
• /
• v.36 no.5
• /
• pp.475-487
• /
• 2024

Many uncertainties affect the stability assessment of rock structures. Some of these factors significantly influence technology decisions. Some of these factors belong to the geological domain, and spatial uncertainty measurements are useful for structural stability analysis. This paper presents an integrated approach to study the stability of rock structures, including spatial factors. This study models two main components: discrete structures (fault zones) and well known geotechnical parameters (rock quality indicators). The geostatistical modeling criterion are used to quantify geographic uncertainty by producing simulated maps and RQD values for multiple equally likely error regions. Slope stability theorem would be demonstrated by modeling local failure zones and RQDs. The approach proided is validated and finally, the slope stability analysis method and fuzzy Laypunov criterion are applied to mining projects with limited measurement data. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results of linear and nonlinear structures show that the proposed method is able to identify structural parameters and their changes due to damage and unknown excitations. Therefore, the goal is believed to achieved in the near future by the ongoing development of AI and fuzzy theory.

• Geomechanics and Engineering
• /
• v.20 no.6
• /
• pp.537-546
• /
• 2020

Oil and natural gas reserves have been recognised abundantly in clayey rich rock formations in deep costal reservoirs. It is necessary to understand the sedimentary history of those reservoir rocks to well explore these natural resources. This work designs a group of laboratory experiments to mimic the physical process of the sedimentary clay-rich rock formation. It presents characterisation results of the physical properties of the artificial clayey rocks synthesized from illite clay, quartz sand and brine water by high-pressure consolidation tests. Special focus is given on the effects of illite clay content and high-stress consolidation on the physical properties. Multi-step loaded consolidation experiments were carried out with stress up to 35 MPa on mixtures constituting of the illite clay, quartz sand and brine water with five initial illite clay contents (w=85%, 70%, 55%, 40% and 25%). Compressibility and void ratio were characterised throughout the physical compaction process of the mixtures constituting of five illite clay contents and their water permeability was measured as well. Results show that the applied stress induces a great reduction of clayey rock void ratio. Illite clay contents has a significant influence on the compressibility, void ratio and the permeability of the physically synthesized clayey rocks. There is a critical illite clay content w=70% that induces the minimum void ratio in the physically synthesised clayey rocks. The SEM study indicates, in the high-pressure synthesised clayey rocks with high illite clay contents, the illite clay minerals are located in layers and serve as the material matrix, and the quartz minerals fill in the inter-mineral pores or are embedded in the illite clay matrix. The arrangements of the minerals in microscale originate the structural anisotropy of the high-pressure synthesised clayey rock. The test findings can give an intuitive physical understanding of the deep-buried clayey rock basins in energy reservoirs.

• Geomechanics and Engineering
• /
• v.21 no.1
• /
• pp.23-33
• /
• 2020

An advancing cutting roof for gob-side entry retaining with no-pillar mining under specific geological conditions is more conducive to the safe and efficient production in a coalmine. This method is being promoted for use in a large number of coalmines because it has many advantages compared to the retaining method with an artificial filling wall as the gateway side filling body. In order to observe the inner structure of the gateway cutting roof and understand its stability mechanism, an equivalent material simulation experiment for a coalmine with complex geological conditions was carried out in this study. The results show that a "self-stabilization bearing structure" equilibrium model was found after the cutting roof caving when the cut line deviation angle was unequal to zero and the cut height was greater than the mining height, and the caving roof rock was hard without damage. The model showed that its stability was mainly controlled by two key blocks. Furthermore, in order to determine the optimal parameters of the cut height and the cut line deviation angle for the cutting roof of the retaining gateway, an in-depth analysis with theoretical mechanics and mine rock mechanics of the model was performed, and the relationship between the roof balance control force and the cut height and cut line deviation angle was solved. It was found that the selection of the values of the cut height and the cut line deviation angle had to conform to a certain principle that it should not only utilize the support force provided by the coal wall and the contact surface of the two key blocks but also prevent the failure of the coal wall and the contact surface.

• Geomechanics and Engineering
• /
• v.35 no.2
• /
• pp.121-133
• /
• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.