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Inclusion of stone columns in the underlain soft soil is one of the most prominent methods for improving the stability of embankments. The stone columns are encased with a geosynthetic material to further enhance the stability. The influence of this partial replacement of weak foundation soil with stone columns on the performance of embankments nee...
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... Therefore, the capacity of EGC to avoid complete shear failure of granular columns is crucial, thus averting total structural collapse. Recently, several researchers have become interested in using geosynthetic-encased granular columns to prevent the course of the slip surface and reduce the probability of failures like deep-seated failures in the case of embankments due to this intact behavior of EGC even at higher shear deformations (Jasim and Tonaroglu 2023;Dar and Shah 2021). ...
Geosynthetic encapsulation of granular columns has proven to be an effective ground improvement solution. The behavior of this innovative eco-composite under vertical loading is well documented in the literature. Granular columns, however, also endure significant lateral shear stress, particularly when located at the toes of embankments or retaining walls and due to stresses brought on by earthquakes. This study experimentally investigated lateral shear loading on ordinary and granular columns encapsulated by geosynthetic material. The experimental tests were performed using the large-scale direct shear testing machine. Based on the findings of this experimental investigation, extra confining forces provided to columns by geosynthetic encapsulation led to the development of apparent cohesion within the column, increasing the lateral shear resistance of the composite. The effect of critical factors like the morphology of column infill material and column configurations on the shear strength parameters of soil-column composites has been highlighted. Also, it was observed that ordinary granular columns undergo complete shear failure along the shear plane; however, for the geosynthetic-encased columns, the failure mechanism was bending rather than complete shear failure, preventing catastrophic failure.
... This is done until the maximum strain increases significantly, understanding that, at this stage, there has been a rupture. Thus, the safety factor will be equivalent to the reduction factor immediately preceding the one where the great deformation occurred [17]. ...
Slope failure poses a serious threat to the built environment as it is currently one of the fundamental contributors to climate change fears across the world and this threatens the environmental goals of the United Nations Sustainable Development Goals (UNSDGs) for year 2050. In this research paper, an optimized geo-stabilization numerical model has been developed with a Plaxis 2D code under safety and cost optimization considerations for a 37 m high slope embankment located on a soft clay watershed with an infinite extension. The site was prepared with four monitoring wells installed at 2.5 m, 7.5 m, 12.5 m and 21.5 m from the foot of the slope to measure the water level conditions and samples were collected and tested in the laboratory to determine the hydraulic and shear strength and modulus of the soil. Seven (7) different simulation alternatives were considered in terms of the model solutions to be deployed under dry and wet states, which were slope steep (angle) reduction (Alt-1), dewatering (Alt-2), jet grouting (Alt-3), jet grouting/dewatering (Alt-4), slope reduction/jet grouting (Alt-5), slope reduction/dewatering (Alt-6), and slope reduction/jet grouting/dewatering (Alt-7). The finite element model implementation of the alternatives showed that Alt-2, Alt-3, and Alt-4 had FOS of less than 1.5 and were omitted because their stability considerations did not meet the requirements for the normal operating conditions of a slope and also the short-term and long-term stability conditions according to literature. Alternatives 1, 5, 6, and 7 with FOS above 1.5 were selected for further optimization considerations. Economic and sustainability factors were selected and considered based on the cost in line with current average market prices, constructability, reliability and the environmental impact needs to achieve the required earthwork, jet grouting, dewatering and selected combinations. Finally, the Alt-1 (FOS = 1.505) though not the cheapest but was selected as the optimized choice in terms of reliability, constructability and environmental impact. However, Alt-6 (FOS = 1.520) and Alt-7 (FOS = 1.508) are the most economical but ranked low in reliability and environmental impact considerations.
... These techniques can be extended by referring to different algorithmic models as suggested in [17], wherein Limit equilibrium method, artificial neural network, Vector sum method, Numerical simulation method and Limit analysis method are described. These methods utilize slope height & angle factors [18] while designing slope analysis models, and can be used for specialized applications like Stone Column-Supported Embankments [19]. The performance of these models can be improved via use of deep learning models as described in [20], wherein hybrid stacking ensemble method is used, which is based on finite element analysis and field data. ...
Slope stability has been a matter of concern for most geologists, mainly due to the fact that unstable slopes cause a greater number of accidents, which in turn reduces efficiency of mining operations. In order to reduce the probability of these slope instabilities, methods like tension crack mapping, inclinometer measurements, time domain reflectometry, borehole extensometers, piezometer, radar systems and image processing systems are deployed. These systems work efficiently for single site slope failures, but as the number of mining sites increase, dependency of one site slope failure on nearby sites also increases. Current systems are not able to capture this data, due to which the probability of accidents at open cast mines increases. In order to reduce this probability, a high efficiency internet of things (IoT)
based continuous slope monitoring and control system is designed.
This system assists in improving the efficiency of real-time slope monitoring via usage of a sensor array consisting of radar, reflectometer, inclinometer, piezometer and borehole extensometer. All these measurements are given to a high efficiency machine learning classifier which uses data mining, and based on its output suitable actions are taken to reduce accidents during mining. This information is dissipated to nearby mining sites in order to inform them about any inconsistencies which might occur due to the slope changes on the current site. Results were simulated using High Resolution Slope Stability Simulator (HIRESSS), and an efficiency improvement of 6% is achieved for slope analysis in open cast mines, while probability of accident reduction is increased by 35% when compared to traditional non IoT based approach.
Keywords—Opencast; mining; slope; IoT; stability; machine
learning; data mining
... These techniques can be extended by referring to different algorithmic models as suggested in [17], wherein Limit equilibrium method, artificial neural network, Vector sum method, Numerical simulation method and Limit analysis method are described. These methods utilize slope height & angle factors [18] while designing slope analysis models, and can be used for specialized applications like Stone Column-Supported Embankments [19]. The performance of these models can be improved via use of deep learning models as described in [20], wherein hybrid stacking ensemble method is used, which is based on finite element analysis and field data. ...
Slope stability has been a matter of concern for most geologists, mainly due to the fact that unstable slopes cause a greater number of accidents, which in turn reduces efficiency of mining operations. In order to reduce the probability of these slope instabilities, methods like tension crack mapping, inclinometer measurements, time domain reflectometry, borehole extensometers, piezometer, radar systems and image processing systems are deployed. These systems work efficiently for single site slope failures, but as the number of mining sites increase, dependency of one site slope failure on nearby sites also increases. Current systems are not able to capture this data, due to which the probability of accidents at open cast mines increases. In order to reduce this probability, a high efficiency internet of things (IoT) based continuous slope monitoring and control system is designed. This system assists in improving the efficiency of real-time slope monitoring via usage of a sensor array consisting of radar, reflectometer, inclinometer, piezometer and borehole extensometer. All these measurements are given to a high efficiency machine learning classifier which uses data mining, and based on its output suitable actions are taken to reduce accidents during mining. This information is dissipated to nearby mining sites in order to inform them about any inconsistencies which might occur due to the slope changes on the current site. Results were simulated using HIgh REsolution Slope Stability Simulator (HIRESSS), and an efficiency improvement of 6% is achieved for slope analysis in open cast mines, while probability of accident reduction is increased by 35% when compared to traditional non-IoT based approach.
... Finally, with a batch size of 5 and 100 epochs, two ANN models have been structured (Eq. 10) (Fig. 8) (Dar and Shah 2021;Chi et al. 2021). Moreover, both the structured networks are tested based on their outcomes and finite difference results. ...
Slope failures are avoidable accidents in most of the scenarios. The eventuality of a failure leads to loss of lives and destruction , especially in hilly areas. Investigation, analysis and prediction of slope failure is a reliable approach to avert such mishaps. Hence, the present research work delves into the prediction of landslides and slope failures through numerical simulation and a deep learning approach. Field attributes and laboratory-tested strength data from Lower Tons Valley, Northern India has been taken as a case study. Initially, a total of 185 slope models were simulated in a finite difference code by varying four slope parameters, namely, slope angle, slope height, cohesion and angle of internal friction. These simulated results were further divided into two parts, one part with 148 datasets for the training of models and other part consisting of 37 datasets for testing of models. Two artificial neural network prediction models, along with a conventional multi-linear regression model was developed and their accuracy was accessed. The developed neural network models superseded the conventional model, in terms of performance and accuracy, as shown by statistical approaches R 2 and mean squared error values. Moreover, the neural network model with Adam optimizer achieved higher statistical accuracy than the one with stochastic gradient descent optimizer. However, all these deep learning models demonstrate significant performance, and can be used by geo-engineers for swift prediction of safety factors for excavated slopes in the study area.
... Figure 1 indicates the different types of probable embankment slope failures. In recent years many numerical studies conducted for studying the slope stability of embankments constructed over stone columns' strengthened soft soil (for example, [3,8,[11][12][13][14][15][16][17]). This paper aims to investigate numerically using the finite element code, PLAXIS 3D 2020, the performance of a full-scale embankment constructed on soft soil strengthen with deferent methods (i.e., ordinary stone column (OSC), Horizontally Reinforced Stone Columns (HRSC), Encased Stone Columns (ESC), combined Vertical-Horizontal Reinforced Stone Columns (V-HRSC), and ESC in combination with Basal Geogrids reinforcement (BGR)). ...
Structures constructed on soft soils may undergo significant settlement, local or global instability, and a significant lateral displacement of the soft soil layer. Ordinary stone columns (OSC) and stone columns strengthened with geosynthetic reinforcement reduce settlement and improve the subsoil's bearing capacity. Numerical analyses have been performed using a 3-dimensional finite element program (PLAXIS3D) to investigate the time-dependent behavior of embankments resting on stone columns constructed in very soft clay. The geosynthetic encasement is the more typical type of reinforcement; however, laminated layers can be adopted in this study. The geosynthetics material was used to strengthen the OSC in the form of vertical encasement, horizontal stripes, and combined vertical-horizontal reinforcement and vertical-basal geogrid reinforcement (BGR). This research compares these forms of reinforcement on embankment behavior. The research results showed that using the encased stone column (ESC) and the vertical-horizontal reinforced stone columns (V-HRSC) have provided a considerable improvement in the lateral deformation of the column over its length, generation, and dissipation of excess pore pressure, and settlement. An increase in factor of safety (FOS) against failure of the embankment was observed by 53% using the ESC compared to untreated soil. Using the horizontal geosynthetic layer (HGL) and the (BGR) after encasing the stone columns has no effect on the safety factor as the failure mechanism converted from deep-seated to surface failure.
... ere are more than two groups of tectonic joints developed in the bedding slopes in the study area, and there are many weak planes in the inter-layer. When the inclination angle of the rock layer is greater than the slope angle, the weak structural plane has no free surface in the inclination direction, and the rock layer generally does not cause the slope to slip and fail but is prone to dumping damage [13]. As shown in Figure 3, the rock mass begins to bend as a cantilever beam in the direction of the air at the front edge, and gradually develops into the slope, causing the rear edge of the slope to crack, forming reverse slope steps and grooves parallel to the strike. ...
The geological mechanics, geotechnical characteristics, and hydrogeological conditions of slopes are complex and changeable, so their stability assessment is a complicated system; their traditional engineering geological assessment does not consider the opposition of the system, the uncertainty of performance indicators, and the ambiguity of index classification, being easy to distort results due to the ambiguity. Improved convolutional neural network (CNN) has outstanding advantages in analyzing problems with randomness and fuzziness. It can perform unified numerical processing on slope assessment indicators with precise values, interval values, and qualitative judgment values, making the traditional qualitative description is transformed into quantitative calculation. Therefore, on the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of the comprehensive assessment model for slope stability and engineering geology; elaborated the development background, current status, and future challenges of the improved CNN; introduced the methods and principles of the model structure, convolutional layer design, and data flow optimization of the improved CNN; performed the assessment index system establishment and index weight determination; established the mathematical assessment model for slope stability; conducted the assessment module design for slope stability based on the improved CNN; analysed the importance of individual factors to the comprehensive engineering geological characteristics; discussed the determination of assessment value of comprehensive unit engineering geological characteristics; explored the assessment module design for slope engineering geology based on the improved CNN; and finally carried out an engineering application and its result analysis. The study results show that the improved CNN can select some universal and objective factors according to the actual conditions of slopes, including topography, stratum lithology, geological structure, atmospheric rainfall, groundwater, engineering activities, setting up factor sets and judgment sets, and making fuzzy inferences. The comprehensive assessment model can use appropriate mathematical methods to judge the pros and cons of slope’s stability and engineering geology according to certain principles and standards, and grade the results and identify the most important geological problems. The results of this paper provide a reference for further researches on the design of a comprehensive assessment model for slope stability and engineering geology based on the improved CNN.
... ANN and SVM have been the most used among all types of AI methods. Other AI methods and algorithms used by researchers in this field include multiple regression (Dar and Shah, 2020;Erzin and Cetin, 2013), extreme learning machine or ELM , MLR (Tien Bui et al., 2019), decision tree Oh and Lee, 2017), independent component analysis (Gao et al., 2020c;Koopialipoor et al., 2019a), multi-layer perceptron , support vector regression (Sari et al., 2019) and random forest (Moayedi et al., 2019b). ...
Geotechnical engineering deals with soils and rocks and their use in engineering constructions. By their nature, soils and rocks exhibit complex behaviours and a high level of uncertainty in material modelling. Artificial intelligence (AI) methods have been developed and used by an increasing number of researchers in the field of geotechnical engineering in the last three decades. These methods have been considered due to their ability to predict complex nonlinear relationships. Based on more than one thousand (i.e. 1235) published literatures, this paper presents a detailed review of the performance of AI methods and algorithms used in geotechnical engineering. Nine key areas where the application of AI methods is prominent were identified: frozen soils and soil thermal properties, rock mechanics, subgrade soil and pavements, landslide and soil liquefaction, slope stability, shallow and piles foundations, tunnelling and tunnel boring machine, dams, and unsaturated soils. Artificial Neural Network (ANN) emerged as the most widely used and preferred AI method with 52% of studies relying on it. Other methods that were used to a lesser extent were FIS, ANFIS, SVM, LSTM, CNN, ResNet and GAN. The analysis shows that the success and accuracy of AI applications depends on the number and type of datasets and selection of input parameters. The paper also provides statistical information on research incorporating AI methods and discusses the opportunities and challenges for future research and practical applications in geotechnical engineering.
... Their research showed that by encasing the gravel column with geotextile, increasing the angle of friction of the stone column material, and the cohesion of the base soil, the FOS increases. Also, the neural network with a hidden layer and 20 neurons per layer can estimate the slope stability FOS with a correlation coefficient of 0.9958 (Ahmad Dar and Yousuf Shah 2020). Support vector machines are one of the soft computing methods that have shown high capability in predicting geotechnical properties of soils. ...
In recent years, geotextile-encased gravel columns (usually called stone columns) have become a popular method to increasing soil shear strength, decreasing the settlement, acceleration of the rate of consolidation, reducing the liquefaction potential and increasing the bearing capacity of foundations. The behavior of improved loose base-soil with gravel columns under shear loading and the shear stress-horizontal displacement curves got from large scale direct shear test are of great importance in understanding the performance of this method. In the present study, by performing 36 large-scale direct shear tests on sandy base-soil with different fine-content of zero to 30% in both not improved and improved with gravel columns, the effect of the presence of gravel columns in the loose soils were investigated. The results were used to predict the shear stress-horizontal displacement curve of these samples using support vector machines (SVM). Variables such as the non-plastic fine content of base-soil (FC), the area replacement ratio of the gravel column (Arr), the geotextile encasement and the normal stress on the sample were effective factors in the shear stress-horizontal displacement curve of the samples. The training and testing data of the model showed higher power of SVM compared to multilayer perceptron (MLP) neural network in predicting shear stress- horizontal displacement curve. After ensuring the accuracy of the model evaluation, by introducing different samples to the model, the effect of different variables on the maximum shear stress of the samples was investigated. The results showed that by adding a gravel column and increasing the Arr, the friction angle (o) and cohesion (c) of the samples increase. This increase is less in base-soil with more FC, and in a proportion of the same Arr, with increasing FC, internal friction angle and cohesion decreases.
... To mitigate this problem, stone columns have been widely used for improving weak soils under the embankments. Various studies are available in the literature which have analysed the influence of stone columns on the behaviour of embankments in static conditions (Yoo and Kim 2009;Lo et al. 2010;Yoo 2010;Abusharar and Han, 2011;Tendel et al. 2013;Khabbazain et al. 2014;Zhang et al. 2014;Khadim and Fouad 2018;Dar and Shah 2021a;Dar and Shah 2022). It has been found out that the stone columns improve the stability of overlying embankments and also help in the dissipation of excess pore water pressure (EPP), thereby acting as an effective liquefaction countermeasure (Kumari et al. 2018;Kumar et al. 2020). ...
... Various researches have been carried out in the past to study the impact of reinforcing the foundation soil on the stability of embankment in static conditions (Zhang et al. 2014;Abushrar and Han 2011;Dar and Shah 2021a). It has been found out that the static stability of the embankments improves considerably by reinforcing the foundation soil with stone columns. ...
Seismic response analysis of embankments on stone column-reinforced soft ground is presented in this study. The effect of stone column reinforcement on the behaviour of embankments during seismic excitation was studied using a 3D finite element programme. The seismic response of the embankments was analysed by mapping the deformations and excess pore water pressure (EPP) development at various locations in the embankment. The effect of spacing to diameter ratio (S/D) of stone columns on the seismic response of embankments was also studied. The results showed a substantial improvement in the seismic behaviour of embankments with the inclusion of stone columns in the foundation soil. Deformations and EPP development reduced considerably due to the stone column reinforcement. The deformation at the top of the embankment reduced by 80% and the EPP values near the base of the embankment attenuated by 90% in case of stone column-reinforced embankments. The S/D ratio largely influenced the seismic response of the embankments as the deformations and EPP values reduced considerably for lower S/D ratios.
