A true triaxial strength criterion for rocks by gene expression programming
- Zhou, Jian, Zhang, Rui, Qiu, Yingui, Khandelwal, Manoj
- Authors: Zhou, Jian , Zhang, Rui , Qiu, Yingui , Khandelwal, Manoj
- Date: 2023
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 15, no. 10 (2023), p. 2508-2520
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- Description: Rock strength is a crucial factor to consider when designing and constructing underground projects. This study utilizes a gene expression programming (GEP) algorithm-based model to predict the true triaxial strength of rocks, taking into account the influence of rock genesis on their mechanical behavior during the model building process. A true triaxial strength criterion based on the GEP model for igneous, metamorphic and magmatic rocks was obtained by training the model using collected data. Compared to the modified Weibols-Cook criterion, the modified Mohr-Coulomb criterion, and the modified Lade criterion, the strength criterion based on the GEP model exhibits superior prediction accuracy performance. The strength criterion based on the GEP model has better performance in R2, RMSE and MAPE for the data set used in this study. Furthermore, the strength criterion based on the GEP model shows greater stability in predicting the true triaxial strength of rocks across different types. Compared to the existing strength criterion based on the genetic programming (GP) model, the proposed criterion based on GEP model achieves more accurate predictions of the variation of true triaxial strength (
- Authors: Zhou, Jian , Zhang, Rui , Qiu, Yingui , Khandelwal, Manoj
- Date: 2023
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 15, no. 10 (2023), p. 2508-2520
- Full Text:
- Reviewed:
- Description: Rock strength is a crucial factor to consider when designing and constructing underground projects. This study utilizes a gene expression programming (GEP) algorithm-based model to predict the true triaxial strength of rocks, taking into account the influence of rock genesis on their mechanical behavior during the model building process. A true triaxial strength criterion based on the GEP model for igneous, metamorphic and magmatic rocks was obtained by training the model using collected data. Compared to the modified Weibols-Cook criterion, the modified Mohr-Coulomb criterion, and the modified Lade criterion, the strength criterion based on the GEP model exhibits superior prediction accuracy performance. The strength criterion based on the GEP model has better performance in R2, RMSE and MAPE for the data set used in this study. Furthermore, the strength criterion based on the GEP model shows greater stability in predicting the true triaxial strength of rocks across different types. Compared to the existing strength criterion based on the genetic programming (GP) model, the proposed criterion based on GEP model achieves more accurate predictions of the variation of true triaxial strength (
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 KRR, K-NN and GPR algorithms for predicting the soaked CBR of fine-grained plastic soils
- Verma, Gaurav, Kumar, Brind, Kumar, Chintoo, Ray, Arunava, Khandelwal, Manoj
- Authors: Verma, Gaurav , Kumar, Brind , Kumar, Chintoo , Ray, Arunava , Khandelwal, Manoj
- Date: 2023
- Type: Text , Journal article
- Relation: Arabian Journal for Science and Engineering Vol. 48, no. 10 (2023), p. 13901-13927
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- Description: California bearing ratio (CBR) test is one of the comprehensive tests used for the last few decades to design the pavement thickness of roadways, railways and airport runways. Laboratory-performed CBR test is considerably rigorous and time-taking. In a quest for an alternative solution, this study utilizes novel computational approaches, including the kernel ridges regression, K-nearest neighbor and Gaussian process regression (GPR), to predict the soaked CBR value of soils. A vast quantity of 1011 in situ soil samples were collected from an ongoing highway project work site. Two data divisional approaches, i.e., K-Fold and fuzzy c-means (FCM) clustering, were used to separate the dataset into training and testing subsets. Apart from the numerous statistical performance measurement indices, ranking and overfitting analysis were used to identify the best-fitted CBR prediction model. Additionally, the literature models were also tried to validate through present study datasets. From the results of Pearson’s correlation analysis, Sand, Fine Content, Plastic Limit, Plasticity Index, Maximum Dry Density and Optimum Moisture Content were found to be most influencing input parameters in developing the soaked CBR of fine-grained plastic soils. Experimental results also establish the proficiency of the GPR model developed through FCM and K-Fold data division approaches. The K-Fold data division approach was found to be helpful in removing the overfitting of the models. Furthermore, the predictive ability of any model is considerably influenced by the geological location of the soils/materials used for the model development. © 2023, The Author(s).
- Authors: Verma, Gaurav , Kumar, Brind , Kumar, Chintoo , Ray, Arunava , Khandelwal, Manoj
- Date: 2023
- Type: Text , Journal article
- Relation: Arabian Journal for Science and Engineering Vol. 48, no. 10 (2023), p. 13901-13927
- Full Text:
- Reviewed:
- Description: California bearing ratio (CBR) test is one of the comprehensive tests used for the last few decades to design the pavement thickness of roadways, railways and airport runways. Laboratory-performed CBR test is considerably rigorous and time-taking. In a quest for an alternative solution, this study utilizes novel computational approaches, including the kernel ridges regression, K-nearest neighbor and Gaussian process regression (GPR), to predict the soaked CBR value of soils. A vast quantity of 1011 in situ soil samples were collected from an ongoing highway project work site. Two data divisional approaches, i.e., K-Fold and fuzzy c-means (FCM) clustering, were used to separate the dataset into training and testing subsets. Apart from the numerous statistical performance measurement indices, ranking and overfitting analysis were used to identify the best-fitted CBR prediction model. Additionally, the literature models were also tried to validate through present study datasets. From the results of Pearson’s correlation analysis, Sand, Fine Content, Plastic Limit, Plasticity Index, Maximum Dry Density and Optimum Moisture Content were found to be most influencing input parameters in developing the soaked CBR of fine-grained plastic soils. Experimental results also establish the proficiency of the GPR model developed through FCM and K-Fold data division approaches. The K-Fold data division approach was found to be helpful in removing the overfitting of the models. Furthermore, the predictive ability of any model is considerably influenced by the geological location of the soils/materials used for the model development. © 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
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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.
Comparative evaluation of empirical approaches and artificial intelligence techniques for predicting uniaxial compressive strength of rock
- Li, Chuanqi, Zhou, Jian, Dias, Daniel, Du, Kun, Khandelwal, Manoj
- Authors: Li, Chuanqi , Zhou, Jian , Dias, Daniel , Du, Kun , Khandelwal, Manoj
- Date: 2023
- Type: Text , Journal article
- Relation: Geosciences (Switzerland) Vol. 13, no. 10 (2023), p.
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- Description: The uniaxial compressive strength (UCS) of rocks is one of the key parameters for evaluating the safety and stability of civil and mining structures. In this study, 386 rock samples containing four properties named the load strength (PLS), the porosity (Pn), the P-wave velocity (Vp), and the Schmidt hardness rebound number (SHR) are utilized to predict the UCS using several typical empirical equations (EA) and artificial intelligence (AI) methods, i.e., 16 single regression (SR) equations, 2 multiple regression (MR) equations, and the random forest (RF) models optimized by grey wolf optimization (GWO), moth flame optimization (MFO), lion swarm optimization (LSO), and sparrow search algorithm (SSA). The root mean square error (RMSE), determination coefficient (R2), Willmott’s index (WI), and variance accounted for (VAF) are used to evaluate the predictive performance of all developed models. The evaluation results show that the overall performance of AI models is superior to empirical approaches, especially the LSO-RF model. In addition, the most important input variable is the Pn for predicting the UCS. Therefore, AI techniques are considered as more efficient and accurate approaches to replace the empirical equations for predicting the UCS of these collected rock samples, which provides a reliable and effective idea to predict the rock UCS in the filed site. © 2023 by the authors.
- Authors: Li, Chuanqi , Zhou, Jian , Dias, Daniel , Du, Kun , Khandelwal, Manoj
- Date: 2023
- Type: Text , Journal article
- Relation: Geosciences (Switzerland) Vol. 13, no. 10 (2023), p.
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- Description: The uniaxial compressive strength (UCS) of rocks is one of the key parameters for evaluating the safety and stability of civil and mining structures. In this study, 386 rock samples containing four properties named the load strength (PLS), the porosity (Pn), the P-wave velocity (Vp), and the Schmidt hardness rebound number (SHR) are utilized to predict the UCS using several typical empirical equations (EA) and artificial intelligence (AI) methods, i.e., 16 single regression (SR) equations, 2 multiple regression (MR) equations, and the random forest (RF) models optimized by grey wolf optimization (GWO), moth flame optimization (MFO), lion swarm optimization (LSO), and sparrow search algorithm (SSA). The root mean square error (RMSE), determination coefficient (R2), Willmott’s index (WI), and variance accounted for (VAF) are used to evaluate the predictive performance of all developed models. The evaluation results show that the overall performance of AI models is superior to empirical approaches, especially the LSO-RF model. In addition, the most important input variable is the Pn for predicting the UCS. Therefore, AI techniques are considered as more efficient and accurate approaches to replace the empirical equations for predicting the UCS of these collected rock samples, which provides a reliable and effective idea to predict the rock UCS in the filed site. © 2023 by the authors.
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.
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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
Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method
- Zhou, Jian, Chen, Yuxin, Chen, Hui, Khandelwal, Manoj, Monjezi, Masoud, Peng, Kang
- Authors: Zhou, Jian , Chen, Yuxin , Chen, Hui , Khandelwal, Manoj , Monjezi, Masoud , Peng, Kang
- Date: 2023
- Type: Text , Journal article
- Relation: Frontiers in Public Health Vol. 11, no. (2023), p.
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- Description: Pillar stability is an important condition for safe work in room-and-pillar mines. The instability of pillars will lead to large-scale collapse hazards, and the accurate estimation of induced stresses at different positions in the pillar is helpful for pillar design and guaranteeing pillar stability. There are many modeling methods to design pillars and evaluate their stability, including empirical and numerical method. However, empirical methods are difficult to be applied to places other than the original environmental characteristics, and numerical methods often simplify the boundary conditions and material properties, which cannot guarantee the stability of the design. Currently, machine learning (ML) algorithms have been successfully applied to pillar stability assessment with higher accuracy. Thus, the study adopted a back-propagation neural network (BPNN) and five elements including the sparrow search algorithm (SSA), gray wolf optimizer (GWO), butterfly optimization algorithm (BOA), tunicate swarm algorithm (TSA), and multi-verse optimizer (MVO). Combining metaheuristic algorithms, five hybrid models were developed to predict the induced stress within the pillar. The weight and threshold of the BPNN model are optimized by metaheuristic algorithms, in which the mean absolute error (MAE) is utilized as the fitness function. A database containing 149 data samples was established, where the input variables were the angle of goafline (A), depth of the working coal seam (H), specific gravity (G), distance of the point from the center of the pillar (C), and distance of the point from goafline (D), and the output variable was the induced stress. Furthermore, the predictive performance of the proposed model is evaluated by five metrics, namely coefficient of determination (R2), root mean squared error (RMSE), variance accounted for (VAF), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results showed that the five hybrid models developed have good prediction performance, especially the GWO-BPNN model performed the best (Training set: R2 = 0.9991, RMSE = 0.1535, VAF = 99.91, MAE = 0.0884, MAPE = 0.6107; Test set: R2 = 0.9983, RMSE = 0.1783, VAF = 99.83, MAE = 0.1230, MAPE = 0.9253). Copyright © 2023 Zhou, Chen, Chen, Khandelwal, Monjezi and Peng.
- Authors: Zhou, Jian , Chen, Yuxin , Chen, Hui , Khandelwal, Manoj , Monjezi, Masoud , Peng, Kang
- Date: 2023
- Type: Text , Journal article
- Relation: Frontiers in Public Health Vol. 11, no. (2023), p.
- Full Text:
- Reviewed:
- Description: Pillar stability is an important condition for safe work in room-and-pillar mines. The instability of pillars will lead to large-scale collapse hazards, and the accurate estimation of induced stresses at different positions in the pillar is helpful for pillar design and guaranteeing pillar stability. There are many modeling methods to design pillars and evaluate their stability, including empirical and numerical method. However, empirical methods are difficult to be applied to places other than the original environmental characteristics, and numerical methods often simplify the boundary conditions and material properties, which cannot guarantee the stability of the design. Currently, machine learning (ML) algorithms have been successfully applied to pillar stability assessment with higher accuracy. Thus, the study adopted a back-propagation neural network (BPNN) and five elements including the sparrow search algorithm (SSA), gray wolf optimizer (GWO), butterfly optimization algorithm (BOA), tunicate swarm algorithm (TSA), and multi-verse optimizer (MVO). Combining metaheuristic algorithms, five hybrid models were developed to predict the induced stress within the pillar. The weight and threshold of the BPNN model are optimized by metaheuristic algorithms, in which the mean absolute error (MAE) is utilized as the fitness function. A database containing 149 data samples was established, where the input variables were the angle of goafline (A), depth of the working coal seam (H), specific gravity (G), distance of the point from the center of the pillar (C), and distance of the point from goafline (D), and the output variable was the induced stress. Furthermore, the predictive performance of the proposed model is evaluated by five metrics, namely coefficient of determination (R2), root mean squared error (RMSE), variance accounted for (VAF), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results showed that the five hybrid models developed have good prediction performance, especially the GWO-BPNN model performed the best (Training set: R2 = 0.9991, RMSE = 0.1535, VAF = 99.91, MAE = 0.0884, MAPE = 0.6107; Test set: R2 = 0.9983, RMSE = 0.1783, VAF = 99.83, MAE = 0.1230, MAPE = 0.9253). Copyright © 2023 Zhou, Chen, Chen, Khandelwal, Monjezi and Peng.
- Wijesinghe, Dakshith, Dyson, Ashley, You, Greg, Khandelwal, Manoj, Ooi, Ean Tat
- Authors: Wijesinghe, Dakshith , Dyson, Ashley , You, Greg , Khandelwal, Manoj , Ooi, Ean Tat
- Date: 2023
- Type: Text , Journal article
- Relation: Engineering Failure Analysis Vol. 148, no. (2023), p.
- Full Text: false
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- Description: Accurate interpretation of stratigraphic profiles, the phreatic surface and the spatial variability of geomaterials are essential to produce representative behaviour of geomechanical systems through numerical simulations. When considering slope stability, variations in soil layer boundaries and the phreatic surface may result in misleading metrics such as the Factor of Safety (FoS) and the Probability of Failure (PoF). This paper presents an image-based technique for generating continuous stratigraphic profiles, including random fluctuations based on Brownian motion. Brownian bridges are used to produce random walks between known points on both soil layer boundaries and the phreatic level that overlap with the slope profile image. Quadtree decomposition is used to discretise the stratigraphy and the phreatic level through an automatic process using generated digital images for mesh generation while also integrating material properties. The Scaled Boundary Finite Element (SBFEM) is used to analyse the slope stability problem. Images based on random walks along an unknown stratigraphic material boundary and the phreatic surface are randomly generated and have many random possibilities, which are used to undertake probabilistic analysis to obtain PoF. This process is complex when using numerical methods, such as the Finite Element Method, as it requires mesh generation from different slope profiles with alternating material interfaces at each probabilistic instance. When the Scaled Boundary Finite Element Method is applied, probabilistic numerical analyses can be fully automated for randomly generated material interfaces. The feasibility of the proposed method is illustrated through several cases of a slope with multiple material layers, in addition to a slope incorporating a Brownian bridge phreatic surface formulation. © 2023 Elsevier Ltd
- Zhang, Yulin, He, Haini, Khandelwal, Manoj, Du, Kun, Zhou, Jian
- Authors: Zhang, Yulin , He, Haini , Khandelwal, Manoj , Du, Kun , Zhou, Jian
- Date: 2023
- Type: Text , Journal article , Review
- Relation: Environmental Science and Pollution Research Vol. 30, no. 47 (2023), p. 103534-103555
- Full Text: false
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- Description: Blasting constitutes an essential component of the mining and construction industries. However, the associated nuisances, particularly blast vibration, have emerged as significant concerns that pose threats to operational stability and the safety of the surrounding areas. Given the increasing emphasis on sustainability, ecological responsibility, safety, and geo-environmental practices, the impact of blast vibration has garnered heightened attention and scrutiny. Nevertheless, the field still lacks comprehensive phase analysis studies. Therefore, it is imperative to elucidate the research progress on blast vibration and discern its current frontiers of investigation. To address this need, this study employs bibliometric methods and the CiteSpace 6.1.R2 software to analyze 3093 papers from the Web of Science database. Through this comprehensive analysis, the study aims to chronicle the developmental trajectory, assess the present research status, and identify future trends in the field of blast vibration. The findings of this study reveal that research on “blasting vibration” is advancing rapidly, with the number of citations exhibiting a J-shaped growth curve over time. China emerges as the leading contributor to this research, followed by India, and the foremost institution in this field is Central South University in China. Cluster analysis identifies the effects of ground vibration, numerical simulation, blast load, blasting vibration and rockburst hazard as the most prominent research areas presently. The primary research directions in this domain revolve around the rock fragmentation, compressive strength, particle swarm optimization, and ann. The emergence of these keywords underscores a dynamic shift towards a more holistic and multidisciplinary approach in the field of blasting-induced ground vibration. Furthermore, this study provides a concise overview of blast vibration, discusses prediction techniques, and proposes measures for its control. Additionally, the discussion delves into the social significance of intelligent blasting systems within the context of artificial intelligence, aiming to address the hazards associated with blast-induced ground vibrations. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Order of intermittent rock fractured surfaces
- Aligholi, Saeed, Khandelwal, Manoj
- Authors: Aligholi, Saeed , Khandelwal, Manoj
- Date: 2023
- Type: Text , Journal article
- Relation: Sustainability (Switzerland) Vol. 15, no. 1 (2023), p.
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- Description: According to chaos theory, some underlying patterns can disclose the order of disordered systems. Here, it has been discussed that intermittency of rough rock fractured surfaces is an orderable disorder at intermediate length scales. However, this kind of disorder is more complicated than simple fractal or even multi-scaling behaviours. It is planned to deal with some multifractal spectra that systematically change as a function of the analysed domain. Accordingly, some parameters are introduced that can perfectly take into account such systematic behaviour and quantify the intermittency of the studied surfaces. This framework can be used to quantify and model the roughness of fractured surfaces as a prerequisite factor for the analysis of fluid flow in rock media as well as the shear strength of rock joints. Ultimately, the presented framework can be used for analysing the intermittency of time series and developing new models for predicting, for instance, seismic or flood events in a short time with higher accuracy. © 2022 by the authors.
- Authors: Aligholi, Saeed , Khandelwal, Manoj
- Date: 2023
- Type: Text , Journal article
- Relation: Sustainability (Switzerland) Vol. 15, no. 1 (2023), p.
- Full Text:
- Reviewed:
- Description: According to chaos theory, some underlying patterns can disclose the order of disordered systems. Here, it has been discussed that intermittency of rough rock fractured surfaces is an orderable disorder at intermediate length scales. However, this kind of disorder is more complicated than simple fractal or even multi-scaling behaviours. It is planned to deal with some multifractal spectra that systematically change as a function of the analysed domain. Accordingly, some parameters are introduced that can perfectly take into account such systematic behaviour and quantify the intermittency of the studied surfaces. This framework can be used to quantify and model the roughness of fractured surfaces as a prerequisite factor for the analysis of fluid flow in rock media as well as the shear strength of rock joints. Ultimately, the presented framework can be used for analysing the intermittency of time series and developing new models for predicting, for instance, seismic or flood events in a short time with higher accuracy. © 2022 by the authors.
Performance evaluation of rockburst prediction based on PSO-SVM, HHO-SVM, and MFO-SVM hybrid models
- Zhou, Jian, Yang, Peixi, Peng, Pingan, Khandelwal, Manoj, Qiu, Yingui
- Authors: Zhou, Jian , Yang, Peixi , Peng, Pingan , Khandelwal, Manoj , Qiu, Yingui
- Date: 2023
- Type: Text , Journal article
- Relation: Mining, Metallurgy and Exploration Vol. 40, no. 2 (2023), p. 617-635
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- Description: Rockburst prediction is crucial in deep hard rock mines and tunnels to make safer working conditions. Due to the complex interaction of many factors involved in rockburst prediction, such as multi-variable and multi-interference factors, three hybrid support vector machine (SVM) models optimized by particle swarm optimization (PSO), Harris hawk optimization (HHO), and moth flame optimization (MFO) are proposed to predict rockburst hazard level (RHL). The RHL is determined according to four kinds of microseismic characteristic parameters including angular frequency ratio, total energy, apparent stress, and convexity radius. Then, six types of microseismic characteristic parameters are taken as input variables in 343 sets of data, including angular frequency ratio and total energy, etc. And the RHL is taken as the output target of rockburst prediction. The classification performance of PSO-SVM, HHO-SVM, and MFO-SVM hybrid models is evaluated by accuracy (ACC), precision (PRE), and kappa coefficient. Findings reveal that the MFO-SVM model performs best in terms of accuracy, with ACC, PRE, and kappa coefficients reaching 0.9559, 0.9063, and 0.9094 respectively, while PSO-SVM and HHO-SVM have similar performances. However, the PSO-SVM, HHO-SVM, and MFO-SVM all perform better than the unoptimized SVM model. This confirms that the three optimization algorithms significantly enhance the rockburst prediction capacity of the SVM model to help mine practitioners apply machine learning methods to rockburst prediction problems appropriately. © 2023, Society for Mining, Metallurgy & Exploration Inc.
- Authors: Zhou, Jian , Yang, Peixi , Peng, Pingan , Khandelwal, Manoj , Qiu, Yingui
- Date: 2023
- Type: Text , Journal article
- Relation: Mining, Metallurgy and Exploration Vol. 40, no. 2 (2023), p. 617-635
- Full Text:
- Reviewed:
- Description: Rockburst prediction is crucial in deep hard rock mines and tunnels to make safer working conditions. Due to the complex interaction of many factors involved in rockburst prediction, such as multi-variable and multi-interference factors, three hybrid support vector machine (SVM) models optimized by particle swarm optimization (PSO), Harris hawk optimization (HHO), and moth flame optimization (MFO) are proposed to predict rockburst hazard level (RHL). The RHL is determined according to four kinds of microseismic characteristic parameters including angular frequency ratio, total energy, apparent stress, and convexity radius. Then, six types of microseismic characteristic parameters are taken as input variables in 343 sets of data, including angular frequency ratio and total energy, etc. And the RHL is taken as the output target of rockburst prediction. The classification performance of PSO-SVM, HHO-SVM, and MFO-SVM hybrid models is evaluated by accuracy (ACC), precision (PRE), and kappa coefficient. Findings reveal that the MFO-SVM model performs best in terms of accuracy, with ACC, PRE, and kappa coefficients reaching 0.9559, 0.9063, and 0.9094 respectively, while PSO-SVM and HHO-SVM have similar performances. However, the PSO-SVM, HHO-SVM, and MFO-SVM all perform better than the unoptimized SVM model. This confirms that the three optimization algorithms significantly enhance the rockburst prediction capacity of the SVM model to help mine practitioners apply machine learning methods to rockburst prediction problems appropriately. © 2023, Society for Mining, Metallurgy & Exploration Inc.
Prediction of blast-induced air overpressure using a hybrid machine learning model and gene expression programming (GEP) : a case study from an iron ore mine
- Kazemi, Mohammad, Nabavi, Zohreh, Khandelwal, Manoj
- Authors: Kazemi, Mohammad , Nabavi, Zohreh , Khandelwal, Manoj
- Date: 2023
- Type: Text , Journal article
- Relation: AIMS Geosciences Vol. 9, no. 2 (2023), p. 357-381
- Full Text:
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- Description: Mine blasting can have a destructive effect on the environment. Among these effects, air overpressure (AOp) is a major concern. Therefore, a careful assessment of the AOp intensity should be conducted before any blasting operation in order to minimize the associated environmental detriment. Several empirical models have been established to predict and control AOp. However, the current empirical methods have many limitations, including low accuracy, poor generalizability, consideration only of linear relationships among influencing parameters, and investigation of only a few influencing parameters. Thus, the current research presents a hybrid model which combines an extreme gradient boosting algorithm (XGB) with grey wolf optimization (GWO) for accurately predicting AOp. Furthermore, an empirical model and gene expression programming (GEP) were used to assess the validity of the hybrid model (XGB-GWO). An analysis of 66 blastings with their corresponding AOp values and influential parameters was conducted to achieve the goals of this research. The efficiency of AOp prediction methods was evaluated in terms of mean absolute error (MAE), coefficient of determination (R 2 ), and root mean square error (RMSE). Based on the calculations, the XGB-GWO model has performed as well as the empirical and GEP models. Next, the most significant parameters for predicting AOp were determined using a sensitivity analysis. Based on the analysis results, stemming length and rock quality designation (RQD) were identified as two variables with the greatest influence. This study showed that the proposed XGB-GWO method was robust and applicable for predicting AOp driven by blasting operations.
- Authors: Kazemi, Mohammad , Nabavi, Zohreh , Khandelwal, Manoj
- Date: 2023
- Type: Text , Journal article
- Relation: AIMS Geosciences Vol. 9, no. 2 (2023), p. 357-381
- Full Text:
- Reviewed:
- Description: Mine blasting can have a destructive effect on the environment. Among these effects, air overpressure (AOp) is a major concern. Therefore, a careful assessment of the AOp intensity should be conducted before any blasting operation in order to minimize the associated environmental detriment. Several empirical models have been established to predict and control AOp. However, the current empirical methods have many limitations, including low accuracy, poor generalizability, consideration only of linear relationships among influencing parameters, and investigation of only a few influencing parameters. Thus, the current research presents a hybrid model which combines an extreme gradient boosting algorithm (XGB) with grey wolf optimization (GWO) for accurately predicting AOp. Furthermore, an empirical model and gene expression programming (GEP) were used to assess the validity of the hybrid model (XGB-GWO). An analysis of 66 blastings with their corresponding AOp values and influential parameters was conducted to achieve the goals of this research. The efficiency of AOp prediction methods was evaluated in terms of mean absolute error (MAE), coefficient of determination (R 2 ), and root mean square error (RMSE). Based on the calculations, the XGB-GWO model has performed as well as the empirical and GEP models. Next, the most significant parameters for predicting AOp were determined using a sensitivity analysis. Based on the analysis results, stemming length and rock quality designation (RQD) were identified as two variables with the greatest influence. This study showed that the proposed XGB-GWO method was robust and applicable for predicting AOp driven by blasting operations.
Quantifying the cohesive strength of rock materials by roughness analysis using a domain based multifractal framework
- Aligholi, Saeed, Torabi, Ali, Khandelwal, Manoj
- Authors: Aligholi, Saeed , Torabi, Ali , Khandelwal, Manoj
- Date: 2023
- Type: Text , Journal article
- Relation: International Journal of Rock Mechanics and Mining Sciences Vol. 170, no. (2023), p.
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- Description: Cohesive strength or intrinsic tensile strength as well as cohesive length are two important unknowns for cohesive modelling of fracture and failure analysis of quasi-brittle materials including rocks. There is no direct method for measuring these parameters and their quantification is always challenging and controversial. In this study, a novel multifractal framework is employed to quantify the cohesive length of four different rock types including sandstone, marble, fine-grained granite and coarse-grained granite by analysing the roughness of their fracture surfaces in a wide range of length scales. On the one hand, microstructural heterogeneities of rock material at small enough length scales will cause multifractality of the roughness of its fractured surface. On the other hand, this intrinsic heterogeneity together with extrinsic features including loading and environmental conditions as well as geometrical features including shape and size of a quasi-brittle specimen or structure are forming a fracture process zone (FPZ) in front of any stress concentrators before crack propagation. Therefore, it is proposed that locating the transition from multifractality to mono-fractality of a rough rock fractured surface using the employed statistical mechanics method leads to quantifying the effective length of FPZ of a sharp crack or the cohesive length. This length is quantified for the studied rocks ranging from 0.4 to 1.1 mm. Moreover, by employing the theory of critical distances, the cohesive strength
- Authors: Aligholi, Saeed , Torabi, Ali , Khandelwal, Manoj
- Date: 2023
- Type: Text , Journal article
- Relation: International Journal of Rock Mechanics and Mining Sciences Vol. 170, no. (2023), p.
- Full Text:
- Reviewed:
- Description: Cohesive strength or intrinsic tensile strength as well as cohesive length are two important unknowns for cohesive modelling of fracture and failure analysis of quasi-brittle materials including rocks. There is no direct method for measuring these parameters and their quantification is always challenging and controversial. In this study, a novel multifractal framework is employed to quantify the cohesive length of four different rock types including sandstone, marble, fine-grained granite and coarse-grained granite by analysing the roughness of their fracture surfaces in a wide range of length scales. On the one hand, microstructural heterogeneities of rock material at small enough length scales will cause multifractality of the roughness of its fractured surface. On the other hand, this intrinsic heterogeneity together with extrinsic features including loading and environmental conditions as well as geometrical features including shape and size of a quasi-brittle specimen or structure are forming a fracture process zone (FPZ) in front of any stress concentrators before crack propagation. Therefore, it is proposed that locating the transition from multifractality to mono-fractality of a rough rock fractured surface using the employed statistical mechanics method leads to quantifying the effective length of FPZ of a sharp crack or the cohesive length. This length is quantified for the studied rocks ranging from 0.4 to 1.1 mm. Moreover, by employing the theory of critical distances, the cohesive strength
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).
Stability prediction of underground entry-type excavations based on particle swarm optimization and gradient boosting decision tree
- Zhou, Jian, Huang, Shuai, Tao, Ming, Khandelwal, Manoj, Dai, Yong, Zhao, Mingsheng
- Authors: Zhou, Jian , Huang, Shuai , Tao, Ming , Khandelwal, Manoj , Dai, Yong , Zhao, Mingsheng
- Date: 2023
- Type: Text , Journal article
- Relation: Underground Space (China) Vol. 9, no. (2023), p. 234-249
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- Description: The stability of underground entry-type excavations will directly affect the working environment and the safety of staff. Empirical critical span graphs and traditional statistics learning methods can not meet the requirements of high accuracy for stability assessment of entry-type excavations. Therefore, this study proposes a new prediction method based on machine learning to scientifically adjust the critical span graph. Accordingly, the particle swarm optimization (PSO) algorithm is used to optimize the core parameters of the gradient boosting decision tree (GBDT), abbreviated as PSO-GBDT. Moreover, the classification performance of eight other classifiers including GDBT, k-nearest neighbors (KNN), two kinds of support vector machines (SVM), Gaussian naive Bayes (GNB), logistic regression (LR) and linear discriminant analysis (LDA) are also applied to compare with the proposed model. Findings revealed that compared with the other eight models, the prediction performance of PSO-GBDT is undoubtedly the most reliable, and its classification accuracy is up to 0.93. Therefore, this model has great potential to provide a more scientific and accurate choice for the stability prediction of underground excavations. In addition, each classification model is used to predict the stability category of several grid points divided by the critical span graph, and the updated critical span graph of each model is discussed in combination with previous studies. The results show that the PSO-GBDT model has the advantages of being scientific, accurate and efficient in updating the critical span graph, and its output decision boundary has strict theoretical support, which can help mine operators make favorable economic decisions. © 2022
- Authors: Zhou, Jian , Huang, Shuai , Tao, Ming , Khandelwal, Manoj , Dai, Yong , Zhao, Mingsheng
- Date: 2023
- Type: Text , Journal article
- Relation: Underground Space (China) Vol. 9, no. (2023), p. 234-249
- Full Text:
- Reviewed:
- Description: The stability of underground entry-type excavations will directly affect the working environment and the safety of staff. Empirical critical span graphs and traditional statistics learning methods can not meet the requirements of high accuracy for stability assessment of entry-type excavations. Therefore, this study proposes a new prediction method based on machine learning to scientifically adjust the critical span graph. Accordingly, the particle swarm optimization (PSO) algorithm is used to optimize the core parameters of the gradient boosting decision tree (GBDT), abbreviated as PSO-GBDT. Moreover, the classification performance of eight other classifiers including GDBT, k-nearest neighbors (KNN), two kinds of support vector machines (SVM), Gaussian naive Bayes (GNB), logistic regression (LR) and linear discriminant analysis (LDA) are also applied to compare with the proposed model. Findings revealed that compared with the other eight models, the prediction performance of PSO-GBDT is undoubtedly the most reliable, and its classification accuracy is up to 0.93. Therefore, this model has great potential to provide a more scientific and accurate choice for the stability prediction of underground excavations. In addition, each classification model is used to predict the stability category of several grid points divided by the critical span graph, and the updated critical span graph of each model is discussed in combination with previous studies. The results show that the PSO-GBDT model has the advantages of being scientific, accurate and efficient in updating the critical span graph, and its output decision boundary has strict theoretical support, which can help mine operators make favorable economic decisions. © 2022
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.
A hybrid metaheuristic approach using random forest and particle swarm optimization to study and evaluate backbreak in open-pit blasting
- Dai, Yong, Khandelwal, Manoj, Qiu, Yingui, Zhou, Jian, Monjezi, Monjezi, Yang, Peixi
- Authors: Dai, Yong , Khandelwal, Manoj , Qiu, Yingui , Zhou, Jian , Monjezi, Monjezi , Yang, Peixi
- Date: 2022
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 34, no. 8 (2022), p. 6273-6288
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- Description: Backbreak is a rock fracture problem that exceeds the limits of the last row of holes in an explosion operation. Excessive backbreak increases operational costs and also poses a threat to mine safety. In this regard, a new hybrid intelligence approach based on random forest (RF) and particle swarm optimization (PSO) is proposed for predicting backbreak with high accuracy to reduce the unsolicited phenomenon induced by backbreak in open-pit blasting. A data set of 234 samples with six input parameters including special drilling (SD), spacing (S), burden (B), hole length (L), stemming (T) and powder factor (PF) and one output parameter backbreak (BB) is set up in this study. Seven input combinations (one with six parameters, six with five parameters) are built to generate the optimal prediction model. The PSO algorithm is integrated with the RF algorithm to find the optimal hyper-parameters of each model and the fitness function, which is the mean absolute error (MAE) of ten cross-validations. The performance capacities of the optimal models are assessed using MAE, root-mean-square error (RMSE), Pearson correlation coefficient (R2) and mean absolute percentage error (MAPE). Findings demonstrated that the PSO–RF model combining L–S–B–T–PF with MAE of 0.0132 and 0.0568, RMSE of 0.0811 and 0.1686, R2 of 0.9990 and 0.9961 and MAPE of 0.0027 and 0.0116 in training and testing phases, respectively, has optimal prediction performance. The optimal PSO–RF models were compared with the classical artificial neural network, RF, genetic programming, support vector machine and convolutional neural network models and show that the PSO–RF model has superiority in predicting backbreak. The Gini index of each input variable has also been calculated in the RF model, which was 31.2 (L), 23.1 (S), 27.4 (B), 36.6 (T), 23.4 (PF) and 16.9 (SD), respectively. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
- Authors: Dai, Yong , Khandelwal, Manoj , Qiu, Yingui , Zhou, Jian , Monjezi, Monjezi , Yang, Peixi
- Date: 2022
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 34, no. 8 (2022), p. 6273-6288
- Full Text:
- Reviewed:
- Description: Backbreak is a rock fracture problem that exceeds the limits of the last row of holes in an explosion operation. Excessive backbreak increases operational costs and also poses a threat to mine safety. In this regard, a new hybrid intelligence approach based on random forest (RF) and particle swarm optimization (PSO) is proposed for predicting backbreak with high accuracy to reduce the unsolicited phenomenon induced by backbreak in open-pit blasting. A data set of 234 samples with six input parameters including special drilling (SD), spacing (S), burden (B), hole length (L), stemming (T) and powder factor (PF) and one output parameter backbreak (BB) is set up in this study. Seven input combinations (one with six parameters, six with five parameters) are built to generate the optimal prediction model. The PSO algorithm is integrated with the RF algorithm to find the optimal hyper-parameters of each model and the fitness function, which is the mean absolute error (MAE) of ten cross-validations. The performance capacities of the optimal models are assessed using MAE, root-mean-square error (RMSE), Pearson correlation coefficient (R2) and mean absolute percentage error (MAPE). Findings demonstrated that the PSO–RF model combining L–S–B–T–PF with MAE of 0.0132 and 0.0568, RMSE of 0.0811 and 0.1686, R2 of 0.9990 and 0.9961 and MAPE of 0.0027 and 0.0116 in training and testing phases, respectively, has optimal prediction performance. The optimal PSO–RF models were compared with the classical artificial neural network, RF, genetic programming, support vector machine and convolutional neural network models and show that the PSO–RF model has superiority in predicting backbreak. The Gini index of each input variable has also been calculated in the RF model, which was 31.2 (L), 23.1 (S), 27.4 (B), 36.6 (T), 23.4 (PF) and 16.9 (SD), respectively. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
A study on environmental issues of blasting using advanced support vector machine algorithms
- Chen, Lihua, Armaghani, Danial, Fakharian, Pouyan, Bhatawdekar, Ramesh, Samui, P., Khandelwal, Manoj, Khedher, Khaled
- Authors: Chen, Lihua , Armaghani, Danial , Fakharian, Pouyan , Bhatawdekar, Ramesh , Samui, P. , Khandelwal, Manoj , Khedher, Khaled
- Date: 2022
- Type: Text , Journal article
- Relation: International Journal of Environmental Science and Technology Vol. 19, no. 7 (2022), p. 6221-6240
- Full Text:
- Reviewed:
- Description: Air overpressure is a critical negative effect of blasting in construction or production sites and projects. So far, many attempts have been made to prevent or reduce this negative effect on the nearby construction, equipment, or people. While various experiential equations have been proposed to forecast the air overpressure value for determining the blasting area, these models are typically inaccurate and impractical. Due to the recent efforts to predict the air overpressure by employing artificial intelligence techniques, this study developed five support vector machine-based models optimized by some praised optimization techniques, including the moth flame optimization, particle swarm optimization, grey wolf optimization, cuckoo optimization algorithm, and whale optimization algorithm. These algorithms optimize the most important parameters of the support vector machine, including “C” and “gamma”, and improve the performance of this model for air overpressure prediction. The findings showed that the moth flame optimization algorithm is the best optimizer for support vector machine and is suitable for air overpressure prediction. The support vector machine–moth flame optimization model achieved the best R2 (train: 0.9939; test: 0.9941) and comprehensive score (34). On the other hand, the worst model was the support vector machine–particle swarm optimization, which achieved the lowest comprehensive score (13). In addition, all optimization techniques improved the performance of the single support vector machine model. The findings of this study imply that all optimization techniques successfully enhanced the performance of the support vector machine model; however, the moth flame optimization optimizer was the most effective one. The support vector machine–moth flame optimization technique can be employed to solve other mining-related issues. © 2022, Islamic Azad University (IAU). Correction to: A study on environmental issues of blasting using advanced support vector machine algorithms (International Journal of Environmental Science and Technology, (2022), 19, 7, (6221-6240), 10.1007/s13762-022-03999-y): The original version of this article unfortunately contains two mistakes. The spelling of the third author's name was incorrect. The correct name is Pouyan Fakharian (P. Fakharian). Another error was in the acknowledgment section. The correct Grant No. is KJQN202103415. © The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University 2022
- Authors: Chen, Lihua , Armaghani, Danial , Fakharian, Pouyan , Bhatawdekar, Ramesh , Samui, P. , Khandelwal, Manoj , Khedher, Khaled
- Date: 2022
- Type: Text , Journal article
- Relation: International Journal of Environmental Science and Technology Vol. 19, no. 7 (2022), p. 6221-6240
- Full Text:
- Reviewed:
- Description: Air overpressure is a critical negative effect of blasting in construction or production sites and projects. So far, many attempts have been made to prevent or reduce this negative effect on the nearby construction, equipment, or people. While various experiential equations have been proposed to forecast the air overpressure value for determining the blasting area, these models are typically inaccurate and impractical. Due to the recent efforts to predict the air overpressure by employing artificial intelligence techniques, this study developed five support vector machine-based models optimized by some praised optimization techniques, including the moth flame optimization, particle swarm optimization, grey wolf optimization, cuckoo optimization algorithm, and whale optimization algorithm. These algorithms optimize the most important parameters of the support vector machine, including “C” and “gamma”, and improve the performance of this model for air overpressure prediction. The findings showed that the moth flame optimization algorithm is the best optimizer for support vector machine and is suitable for air overpressure prediction. The support vector machine–moth flame optimization model achieved the best R2 (train: 0.9939; test: 0.9941) and comprehensive score (34). On the other hand, the worst model was the support vector machine–particle swarm optimization, which achieved the lowest comprehensive score (13). In addition, all optimization techniques improved the performance of the single support vector machine model. The findings of this study imply that all optimization techniques successfully enhanced the performance of the support vector machine model; however, the moth flame optimization optimizer was the most effective one. The support vector machine–moth flame optimization technique can be employed to solve other mining-related issues. © 2022, Islamic Azad University (IAU). Correction to: A study on environmental issues of blasting using advanced support vector machine algorithms (International Journal of Environmental Science and Technology, (2022), 19, 7, (6221-6240), 10.1007/s13762-022-03999-y): The original version of this article unfortunately contains two mistakes. The spelling of the third author's name was incorrect. The correct name is Pouyan Fakharian (P. Fakharian). Another error was in the acknowledgment section. The correct Grant No. is KJQN202103415. © The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University 2022
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
- 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.
- 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.
Comparison and application of top and bottom air decks to improve blasting operations
- Monjezi, Monjezi, Amiri, Hamed, Mousavi, Mir, Hamidi, Jafar, Khandelwal, Manoj
- Authors: Monjezi, Monjezi , Amiri, Hamed , Mousavi, Mir , Hamidi, Jafar , Khandelwal, Manoj
- Date: 2022
- Type: Text , Journal article
- Relation: Aims Geosciences Vol. 9, no. 1 (2022), p. 16-33
- Full Text:
- Reviewed:
- Description: The blasting operation is an integral part of mines, and it is still being used as the most economical tool to fragment and displace rock mass. Appropriate blast optimization alleviates undesirable side effects, such as ground vibration, air blasts and flyrock, and it and enhances rock fragmentation. Blast optimization can also be effective in reducing the overall mining cost. One way of reducing blasting side effects is to use deck charges instead of continuous ones. The location of the deck(s) is still considered an unanswered question for many researchers. In this study, an investigation was carried out to find an appropriate air deck position(s) within the blast hole. For this, air decks were placed at three different positions (top, middle and bottom) within a blast hole at Cheshmeh-Parvar gypsum and Chah-Gaz iron ore mines to understand and evaluate air deck location impact on blast fragmentation and blast nuisances. The results were compared based on the existing blasting practices at both mines, as well as the air-deck blasting results. The results obtained from the blasting were very satisfactory; it was found that charging with a top air deck, as compared to current blasting practices, causes a decrement in the specific charge, as well as a decrement of 38% in the back break and 50% in flyrock; the average size of fragments obtained from blasting was increased by 26%. Thus, it can be said that the top air deck is more advantageous than the bottom air deck in terms of reducing undesired blasting consequences.
- Authors: Monjezi, Monjezi , Amiri, Hamed , Mousavi, Mir , Hamidi, Jafar , Khandelwal, Manoj
- Date: 2022
- Type: Text , Journal article
- Relation: Aims Geosciences Vol. 9, no. 1 (2022), p. 16-33
- Full Text:
- Reviewed:
- Description: The blasting operation is an integral part of mines, and it is still being used as the most economical tool to fragment and displace rock mass. Appropriate blast optimization alleviates undesirable side effects, such as ground vibration, air blasts and flyrock, and it and enhances rock fragmentation. Blast optimization can also be effective in reducing the overall mining cost. One way of reducing blasting side effects is to use deck charges instead of continuous ones. The location of the deck(s) is still considered an unanswered question for many researchers. In this study, an investigation was carried out to find an appropriate air deck position(s) within the blast hole. For this, air decks were placed at three different positions (top, middle and bottom) within a blast hole at Cheshmeh-Parvar gypsum and Chah-Gaz iron ore mines to understand and evaluate air deck location impact on blast fragmentation and blast nuisances. The results were compared based on the existing blasting practices at both mines, as well as the air-deck blasting results. The results obtained from the blasting were very satisfactory; it was found that charging with a top air deck, as compared to current blasting practices, causes a decrement in the specific charge, as well as a decrement of 38% in the back break and 50% in flyrock; the average size of fragments obtained from blasting was increased by 26%. Thus, it can be said that the top air deck is more advantageous than the bottom air deck in terms of reducing undesired blasting consequences.