Adaptive phase-field modelling of fracture propagation in poroelastic media using the scaled boundary finite element method
- Wijesinghe, Dakshith, Natarajan, Sundararajan, You, Greg, Khandelwal, Manoj, Dyson, Ashley, Song, Chongmin, Ooi, Ean Tat
- Authors: Wijesinghe, Dakshith , Natarajan, Sundararajan , You, Greg , Khandelwal, Manoj , Dyson, Ashley , Song, Chongmin , Ooi, Ean Tat
- Date: 2023
- Type: Text , Journal article
- Relation: Computer Methods in Applied Mechanics and Engineering Vol. 411, no. (2023), p.
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- Description: A scaled boundary finite element-based phase field formulation is proposed to model two-dimensional fracture in saturated poroelastic media. The mechanical response of the poroelastic media is simulated following Biot's theory, and the fracture surface evolution is modelled according to the phase field formulation. To avoid the application of fine uniform meshes that are constrained by the element size requirement when adopting phase field models, an adaptive refinement strategy based on quadtree meshes is adopted. The unique advantage of the scaled boundary finite element method is conducive to the application of quadtree adaptivity, as it can be directly formulated on quadtree meshes without the need for any special treatment of hanging nodes. Efficient computation is achieved by exploiting the unique patterns of the quadtree cells. An appropriate scaling is applied to the relevant matrices and vectors according the physical size of the cells in the mesh during the simulations. This avoids repetitive calculations of cells with the same configurations. The proposed model is validated using a benchmark with a known analytical solution. Numerical examples of hydraulic fractures driven by the injected fluid in cracks are modelled to illustrate the capabilities of the proposed model in handling crack propagation problems involving complex geometries. © 2023 The Author(s)
- Authors: Wijesinghe, Dakshith , Natarajan, Sundararajan , You, Greg , Khandelwal, Manoj , Dyson, Ashley , Song, Chongmin , Ooi, Ean Tat
- Date: 2023
- Type: Text , Journal article
- Relation: Computer Methods in Applied Mechanics and Engineering Vol. 411, no. (2023), p.
- Full Text:
- Reviewed:
- Description: A scaled boundary finite element-based phase field formulation is proposed to model two-dimensional fracture in saturated poroelastic media. The mechanical response of the poroelastic media is simulated following Biot's theory, and the fracture surface evolution is modelled according to the phase field formulation. To avoid the application of fine uniform meshes that are constrained by the element size requirement when adopting phase field models, an adaptive refinement strategy based on quadtree meshes is adopted. The unique advantage of the scaled boundary finite element method is conducive to the application of quadtree adaptivity, as it can be directly formulated on quadtree meshes without the need for any special treatment of hanging nodes. Efficient computation is achieved by exploiting the unique patterns of the quadtree cells. An appropriate scaling is applied to the relevant matrices and vectors according the physical size of the cells in the mesh during the simulations. This avoids repetitive calculations of cells with the same configurations. The proposed model is validated using a benchmark with a known analytical solution. Numerical examples of hydraulic fractures driven by the injected fluid in cracks are modelled to illustrate the capabilities of the proposed model in handling crack propagation problems involving complex geometries. © 2023 The Author(s)
Application of various robust techniques to study and evaluate the role of effective parameters on rock fragmentation
- Mehrdanesh, Amirhossein, Monjezi, Masoud, Khandelwal, Manoj, Bayat, Parichehr
- Authors: Mehrdanesh, Amirhossein , Monjezi, Masoud , Khandelwal, Manoj , Bayat, Parichehr
- Date: 2023
- Type: Text , Journal article
- Relation: Engineering with Computers Vol. 39, no. 2 (2023), p. 1317-1327
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- Description: In this paper, an attempt has been made to implement various robust techniques to predict rock fragmentation due to blasting in open pit mines using effective parameters. As rock fragmentation prediction is very complex and complicated, and due to that various artificial intelligence-based techniques, such as artificial neural network (ANN), classification and regression tree and support vector machines were selected for the modeling. To validate and compare the prediction results, conventional multivariate regression analysis was also utilized on the same data sets. Since accuracy and generality of the modeling is dependent on the number of inputs, it was tried to collect enough required information from four different open pit mines of Iran. According to the obtained results, it was revealed that ANN with a determination coefficient of 0.986 is the most precise method of modeling as compared to the other applied techniques. Also, based on the performed sensitivity analysis, it was observed that the most prevailing parameters on the rock fragmentation are rock quality designation, Schmidt hardness value, mean in-situ block size and the minimum effective ones are hole diameter, burden and spacing. The advantage of back propagation neural network technique for using in this study compared to other soft computing methods is that they are able to describe complex and nonlinear multivariable problems in a transparent way. Furthermore, ANN can be used as a first approach, where much knowledge about the influencing parameters are missing. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
- Authors: Mehrdanesh, Amirhossein , Monjezi, Masoud , Khandelwal, Manoj , Bayat, Parichehr
- Date: 2023
- Type: Text , Journal article
- Relation: Engineering with Computers Vol. 39, no. 2 (2023), p. 1317-1327
- Full Text:
- Reviewed:
- Description: In this paper, an attempt has been made to implement various robust techniques to predict rock fragmentation due to blasting in open pit mines using effective parameters. As rock fragmentation prediction is very complex and complicated, and due to that various artificial intelligence-based techniques, such as artificial neural network (ANN), classification and regression tree and support vector machines were selected for the modeling. To validate and compare the prediction results, conventional multivariate regression analysis was also utilized on the same data sets. Since accuracy and generality of the modeling is dependent on the number of inputs, it was tried to collect enough required information from four different open pit mines of Iran. According to the obtained results, it was revealed that ANN with a determination coefficient of 0.986 is the most precise method of modeling as compared to the other applied techniques. Also, based on the performed sensitivity analysis, it was observed that the most prevailing parameters on the rock fragmentation are rock quality designation, Schmidt hardness value, mean in-situ block size and the minimum effective ones are hole diameter, burden and spacing. The advantage of back propagation neural network technique for using in this study compared to other soft computing methods is that they are able to describe complex and nonlinear multivariable problems in a transparent way. Furthermore, ANN can be used as a first approach, where much knowledge about the influencing parameters are missing. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Estimating the mean cutting force of conical picks using random forest with salp swarm algorithm
- Zhou, Jian, Dai, Yong, Tao, Ming, Khandelwal, Manoj, Zhao, Mingsheng, Li, Qiyue
- Authors: Zhou, Jian , Dai, Yong , Tao, Ming , Khandelwal, Manoj , Zhao, Mingsheng , Li, Qiyue
- Date: 2023
- Type: Text , Journal article
- Relation: Results in Engineering Vol. 17, no. (2023), p.
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- Description: Conical picks are widely used as cutting tools in shearers and roadheaders, and the mean cutting force (MCF) is one of the important parameters affecting conical pick performance. As MCF depends on a number of parameters and due to that the existing empirical and theoretical formulas and numerical modelling are not sufficient enough and reliable to predict MCF in a proficient manner. So, in this research, a novel intelligent model based on a random forest algorithm (RF) and a heuristic algorithm called the salp swarm algorithm (SSA) have been applied to determine the optimal hyper-parameters in RF, and root mean square error is used as a fitness function. A total of 188 data samples including 50 rock types and seven parameters (tensile strength of the rock
- Authors: Zhou, Jian , Dai, Yong , Tao, Ming , Khandelwal, Manoj , Zhao, Mingsheng , Li, Qiyue
- Date: 2023
- Type: Text , Journal article
- Relation: Results in Engineering Vol. 17, no. (2023), p.
- Full Text:
- Reviewed:
- Description: Conical picks are widely used as cutting tools in shearers and roadheaders, and the mean cutting force (MCF) is one of the important parameters affecting conical pick performance. As MCF depends on a number of parameters and due to that the existing empirical and theoretical formulas and numerical modelling are not sufficient enough and reliable to predict MCF in a proficient manner. So, in this research, a novel intelligent model based on a random forest algorithm (RF) and a heuristic algorithm called the salp swarm algorithm (SSA) have been applied to determine the optimal hyper-parameters in RF, and root mean square error is used as a fitness function. A total of 188 data samples including 50 rock types and seven parameters (tensile strength of the rock
Sensitivity analysis on blast design parameters to improve bench blasting outcomes using the Taguchi method
- Hosseini, Mostafa, Khandelwal, Manoj, Lotfi, Rahman, Eslahi, Mohsen
- Authors: Hosseini, Mostafa , Khandelwal, Manoj , Lotfi, Rahman , Eslahi, Mohsen
- Date: 2023
- Type: Text , Journal article
- Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 9, no. 1 (2023), p.
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- Description: In surface mines, bench blasting is a typical way of excavating hard rock mass. Although a significant development has taken place in explosive technology but still only a part of the energy is used to excavate and a large proportion of energy is wasted away and creates a number of nuisances. Backbreak, massive rock fragmentation, and high-intensity ground vibration are all symptoms of improper blasting. As a result, production costs increase significantly while productivity decreases. The blasting outcomes are affected by a variety of factors, which may be classified into three categories: rock properties, explosive properties, and blast geometry. Consequently, it is necessary to examine the effect of these parameters on bench blasting. So, in this study, a sensitivity analysis has been performed on various blast design parameters using the Taguchi method to study the influence of blast design parameters on blast vibration, backbreak, and rock fragmentation. A total of 32 experiments have been designed and numerical modeling was also carried out, using LS DYNA software to simulate the blast results. It was found that the blast hole diameter is the most important factor influencing the blasting outcomes. However, the number of rows in a blast affects backbreak almost slightly more than the hole diameter, but blast vibrations and the surrounding rock damage strongly depend on the hole diameter. Furthermore, rock blast geometry significantly affected rock blast vibration and damage compared to explosive properties. However, both blast geometry parameters and explosive properties play a significant role in backbreaking. © 2023, The Author(s).
- Authors: Hosseini, Mostafa , Khandelwal, Manoj , Lotfi, Rahman , Eslahi, Mohsen
- Date: 2023
- Type: Text , Journal article
- Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 9, no. 1 (2023), p.
- Full Text:
- Reviewed:
- Description: In surface mines, bench blasting is a typical way of excavating hard rock mass. Although a significant development has taken place in explosive technology but still only a part of the energy is used to excavate and a large proportion of energy is wasted away and creates a number of nuisances. Backbreak, massive rock fragmentation, and high-intensity ground vibration are all symptoms of improper blasting. As a result, production costs increase significantly while productivity decreases. The blasting outcomes are affected by a variety of factors, which may be classified into three categories: rock properties, explosive properties, and blast geometry. Consequently, it is necessary to examine the effect of these parameters on bench blasting. So, in this study, a sensitivity analysis has been performed on various blast design parameters using the Taguchi method to study the influence of blast design parameters on blast vibration, backbreak, and rock fragmentation. A total of 32 experiments have been designed and numerical modeling was also carried out, using LS DYNA software to simulate the blast results. It was found that the blast hole diameter is the most important factor influencing the blasting outcomes. However, the number of rows in a blast affects backbreak almost slightly more than the hole diameter, but blast vibrations and the surrounding rock damage strongly depend on the hole diameter. Furthermore, rock blast geometry significantly affected rock blast vibration and damage compared to explosive properties. However, both blast geometry parameters and explosive properties play a significant role in backbreaking. © 2023, The Author(s).
The lithium-ion battery recycling process from a circular economy perspective—a review and future directions
- Sheth, Rahil, Ranawat, Narendra, Chakraborty, Ayon, Mishra, Rajesh, Khandelwal, Manoj
- Authors: Sheth, Rahil , Ranawat, Narendra , Chakraborty, Ayon , Mishra, Rajesh , Khandelwal, Manoj
- Date: 2023
- Type: Text , Journal article , Review
- Relation: Energies Vol. 16, no. 7 (2023), p.
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- Description: Ever since the introduction of lithium-ion batteries (LIBs) in the 1970s, their demand has increased exponentially with their applications in electric vehicles, smartphones, and energy storage systems. To cope with the increase in demand and the ensuing environmental effects of excessive mining activities and waste production, it becomes crucial to explore ways of manufacturing LIBs from the resources that have already been extracted from nature. It is possible by promoting the re-usage, refurbishing, and recycling of the batteries and their constituent components, rethinking the fundamental design of devices using these batteries, and introducing the circular economy model in the battery industry. This paper through a literature review provides the current state of CE adoption in the lithium-ion battery industry. The review suggests that the focus is mostly on recycling at this moment in the battery industry, and a further understanding of the process is needed to better adapt to other CE practices such as reuse, remanufacture, refurbishment, etc. The paper also provides the steps involved in the recycling process and, through secondary case studies, shows how some of the industries are currently approaching battery recycling. Thus, this paper, through review and secondary cases, helps us to understand the current state of LIB recycling and CE adoption. © 2023 by the authors.
- Authors: Sheth, Rahil , Ranawat, Narendra , Chakraborty, Ayon , Mishra, Rajesh , Khandelwal, Manoj
- Date: 2023
- Type: Text , Journal article , Review
- Relation: Energies Vol. 16, no. 7 (2023), p.
- Full Text:
- Reviewed:
- Description: Ever since the introduction of lithium-ion batteries (LIBs) in the 1970s, their demand has increased exponentially with their applications in electric vehicles, smartphones, and energy storage systems. To cope with the increase in demand and the ensuing environmental effects of excessive mining activities and waste production, it becomes crucial to explore ways of manufacturing LIBs from the resources that have already been extracted from nature. It is possible by promoting the re-usage, refurbishing, and recycling of the batteries and their constituent components, rethinking the fundamental design of devices using these batteries, and introducing the circular economy model in the battery industry. This paper through a literature review provides the current state of CE adoption in the lithium-ion battery industry. The review suggests that the focus is mostly on recycling at this moment in the battery industry, and a further understanding of the process is needed to better adapt to other CE practices such as reuse, remanufacture, refurbishment, etc. The paper also provides the steps involved in the recycling process and, through secondary case studies, shows how some of the industries are currently approaching battery recycling. Thus, this paper, through review and secondary cases, helps us to understand the current state of LIB recycling and CE adoption. © 2023 by the authors.
Blasting pattern optimization using gene expression programming and grasshopper optimization algorithm to minimise blast-induced ground vibrations
- Bayat, Parichehra, Monjezi, Mejrdamesj, Mehrdanesh, Amirhosseina, Khandelwal, Manoj
- Authors: Bayat, Parichehra , Monjezi, Mejrdamesj , Mehrdanesh, Amirhosseina , Khandelwal, Manoj
- Date: 2022
- Type: Text , Journal article
- Relation: Engineering with Computers Vol. 38, no. 4 (2022), p. 3341-3350
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- Description: Blast-induced ground vibration is considered as one of the most hazardous phenomena of mine blasting, which can even cause casualties and severe damages to the adjacent properties. Measuring peak particle velocity (PPV) is helpful to know the actual vibration level but prediction of blast vibration prior to the blast is a tedious job due to involvement of blast design, explosive and rock parameters. Nowadays, efficient application of intelligent systems has been approved in different branches of science and technology. In this paper, a gene expression programming (GEP) model was developed to predict PPV using various blasting patterns as model inputs, which showed a high level of accuracy for the implemented model. Also, to optimize blast pattern attaining minimum ground vibration during blasting operation, the developed functional GEP model was taken as objective function for grasshopper optimization algorithm (GOA). Construction of GOA model was performed using a trial and error mechanism to find out the best possible pertinent GOA parameters. Finally, it was observed that utilizing GOA technique, PPV can be reduced by 67% with optimized blast parameters including burden of 3.21 m, spacing of 3.75 m, and charge per delay of 225 kg. A sensitivity analysis was also performed to understand the influence of each input parameters on the blast vibrations. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature.
- Authors: Bayat, Parichehra , Monjezi, Mejrdamesj , Mehrdanesh, Amirhosseina , Khandelwal, Manoj
- Date: 2022
- Type: Text , Journal article
- Relation: Engineering with Computers Vol. 38, no. 4 (2022), p. 3341-3350
- Full Text:
- Reviewed:
- Description: Blast-induced ground vibration is considered as one of the most hazardous phenomena of mine blasting, which can even cause casualties and severe damages to the adjacent properties. Measuring peak particle velocity (PPV) is helpful to know the actual vibration level but prediction of blast vibration prior to the blast is a tedious job due to involvement of blast design, explosive and rock parameters. Nowadays, efficient application of intelligent systems has been approved in different branches of science and technology. In this paper, a gene expression programming (GEP) model was developed to predict PPV using various blasting patterns as model inputs, which showed a high level of accuracy for the implemented model. Also, to optimize blast pattern attaining minimum ground vibration during blasting operation, the developed functional GEP model was taken as objective function for grasshopper optimization algorithm (GOA). Construction of GOA model was performed using a trial and error mechanism to find out the best possible pertinent GOA parameters. Finally, it was observed that utilizing GOA technique, PPV can be reduced by 67% with optimized blast parameters including burden of 3.21 m, spacing of 3.75 m, and charge per delay of 225 kg. A sensitivity analysis was also performed to understand the influence of each input parameters on the blast vibrations. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature.
- Zhou, Jian, Chen, Chao, Khandelwal, Manoj, Tao, Ming, Li, Chuanqi
- Authors: Zhou, Jian , Chen, Chao , Khandelwal, Manoj , Tao, Ming , Li, Chuanqi
- Date: 2022
- Type: Text , Journal article
- Relation: Engineering with computers Vol. 38, no. Suppl 5 (2022), p. 3789-3809
- Full Text: false
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- Description: In recent years, block caving has drawn the attention of many mine enterprises due to the admired extraction rate and lower cost, which can exploit the materials via gravity inflow. At the same time, the limitation of this advanced method cannot be underestimated easily, such as surface subsidence and boulder, usually, the latter leads to the frequent secondary blast and damage of bottom structure. Thus, it is significant and crucial to evaluate the fragmentation before the implement of this method. But, traditional fragmentation assessment model suffers from the complex process of modeling and simulation. In this study, a hybrid model consists of unascertained measurement theory and information entropy was constructed to meet the requirements of this prospective mining method. Considering the influence of various parameters on rock fragmentation at the same time, twenty-three factors (i.e., uniaxial compressive strength, modulus ratio, fracture frequency, aperture, persistence, joint orientation, roughness, infilling, weathering, in situ stresses, stress orientation, stress ratio, underground water, fine ratio, hydraulic radius, undercut height, draw column height, draw points geometry, draw rate, multiple draw interaction, air gap height, broken ore density and undercut direction) were chosen to extract the main characteristics of rock mass samples from the two different mines, namely Reserve North ( Chile ), Diablo Regimiento ( Chile ) and Kemess mine ( Canada ). A new membership function (logarithm curve) was added to eliminate uncertainty results from the low level of knowledge about rock mass properties. Then, information entropy was performed to quantify the impacts of individual index. A credible degree identification criterion ( R η ) was also applied to review the sample attributes qualitatively. Ultimately, degree of fragmentation of the three samples was judged easily on the basis of a composite measurement vectors and R η . The evaluation results showed that the fragmentation grades of Reserve North , Diablo Regimiento and Kemess mine , separately, were “Good”, “Medium” and “Good”. With regard to the excellent performance of this hybrid model, it can be seen as a reliable approach to describe the fragmentation potential during the ore extraction using block caving mining method.
- Qiu, Yingui, Zhou, Jian, Khandelwal, Manoj, Yang, Haitao, Yang, Peixi, Li, Chuanqi
- Authors: Qiu, Yingui , Zhou, Jian , Khandelwal, Manoj , Yang, Haitao , Yang, Peixi , Li, Chuanqi
- Date: 2022
- Type: Text , Journal article
- Relation: Engineering with Computers Vol. 38, no. (2022), p. 4145-4162
- Full Text: false
- Reviewed:
- Description: Accurate prediction of ground vibration caused by blasting has always been a significant issue in the mining industry. Ground vibration caused by blasting is a harmful phenomenon to nearby buildings and should be prevented. In this regard, a new intelligent method for predicting peak particle velocity (PPV) induced by blasting had been developed. Accordingly, 150 sets of data composed of thirteen uncontrollable and controllable indicators are selected as input dependent variables, and the measured PPV is used as the output target for characterizing blast-induced ground vibration. Also, in order to enhance its predictive accuracy, the gray wolf optimization (GWO), whale optimization algorithm (WOA) and Bayesian optimization algorithm (BO) are applied to fine-tune the hyper-parameters of the extreme gradient boosting (XGBoost) model. According to the root mean squared error (RMSE), determination coefficient (R2), the variance accounted for (VAF), and mean absolute error (MAE), the hybrid models GWO-XGBoost, WOA-XGBoost, and BO-XGBoost were verified. Additionally, XGBoost, CatBoost (CatB), Random Forest, and gradient boosting regression (GBR) were also considered and used to compare the multiple hybrid-XGBoost models that have been developed. The values of RMSE, R2, VAF, and MAE obtained from WOA-XGBoost, GWO-XGBoost, and BO-XGBoost models were equal to (3.0538, 0.9757, 97.68, 2.5032), (3.0954, 0.9751, 97.62, 2.5189), and (3.2409, 0.9727, 97.65, 2.5867), respectively. Findings reveal that compared with other machine learning models, the proposed WOA-XGBoost became the most reliable model. These three optimized hybrid models are superior to the GBR model, CatB model, Random Forest model, and the XGBoost model, confirming the ability of the meta-heuristic algorithm to enhance the performance of the PPV model, which can be helpful for mine planners and engineers using advanced supervised machine learning with metaheuristic algorithms for predicting ground vibration caused by explosions. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Stability evaluation of dump slope using artificial neural network and multiple regression
- Bharati, , Ashutosh, Ray, Arunava, Khandelwal, Manoj, Rai, Rajesha, Jaiswal, , Ashok
- Authors: Bharati, , Ashutosh , Ray, Arunava , Khandelwal, Manoj , Rai, Rajesha , Jaiswal, , Ashok
- Date: 2022
- Type: Text , Journal article
- Relation: Engineering with Computers Vol. 38, no. (2022), p. 1835-1843
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- Description: The present paper focuses on designing an artificial neural network (ANN) model and a multiple regression analysis (MRA) that could be used to predict factor of safety of dragline dump slope. To implement these two models, the dataset was utilized from the numerical simulation results of dragline dump slopes, wherein 216 dragline dump slope models were simulated using a numerical modeling technique employed with the finite element method. The finite element model was incorporated a combination of three geometrical parameters, namely, coal-rib height (Crh), dragline dump slope height (Sh), and dragline dump slope angle (Sa) of the dump slope. The predicted results derived from the MRA and ANN models were compared with the results obtained from the numerical simulation of the dump slope models. Moreover, to compare the validity of both the models, various performance indicators, such as variance account for (VAF), determination coefficient (R2), root mean square error (RMSE), and residual error were calculated. Based on these performance indicators, the ANN model has shown a higher prediction accuracy than the MRA model. The study reveals that the ANN model developed in this research could be handy in designing the dragline dump slopes at the preliminary stage. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature.
- Authors: Bharati, , Ashutosh , Ray, Arunava , Khandelwal, Manoj , Rai, Rajesha , Jaiswal, , Ashok
- Date: 2022
- Type: Text , Journal article
- Relation: Engineering with Computers Vol. 38, no. (2022), p. 1835-1843
- Full Text:
- Reviewed:
- Description: The present paper focuses on designing an artificial neural network (ANN) model and a multiple regression analysis (MRA) that could be used to predict factor of safety of dragline dump slope. To implement these two models, the dataset was utilized from the numerical simulation results of dragline dump slopes, wherein 216 dragline dump slope models were simulated using a numerical modeling technique employed with the finite element method. The finite element model was incorporated a combination of three geometrical parameters, namely, coal-rib height (Crh), dragline dump slope height (Sh), and dragline dump slope angle (Sa) of the dump slope. The predicted results derived from the MRA and ANN models were compared with the results obtained from the numerical simulation of the dump slope models. Moreover, to compare the validity of both the models, various performance indicators, such as variance account for (VAF), determination coefficient (R2), root mean square error (RMSE), and residual error were calculated. Based on these performance indicators, the ANN model has shown a higher prediction accuracy than the MRA model. The study reveals that the ANN model developed in this research could be handy in designing the dragline dump slopes at the preliminary stage. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature.
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