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.
- Full Text:
- Reviewed:
- 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.
Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations
- Zhou, Jian, Qiu, Yingui, Khandelwal, Manoj, Zhu, Shuangli, Zhang, Xiliang
- Authors: Zhou, Jian , Qiu, Yingui , Khandelwal, Manoj , Zhu, Shuangli , Zhang, Xiliang
- Date: 2021
- Type: Text , Journal article
- Relation: International Journal of Rock Mechanics and Mining Sciences Vol. 145, no. (2021), p.
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- Description: Blasting is still being considered to be one the most important applicable alternatives for conventional excavations. Ground vibration generated due to blasting is an undesirable phenomenon which is harmful for the nearby structures and should be prevented. In this regard, a novel intelligent approach for predicting blast-induced PPV was developed. The distinctive Jaya algorithm and high efficient extreme gradient boosting machine (XGBoost) were applied to obtain the goal, called the Jaya-XGBoost model. Accordingly, 150 sets of data composed of 13 controllable and uncontrollable parameters are chosen as input independent variables and the measured peak particle velocity (PPV) is chosen as an output dependent variable. Also, the Jaya algorithm was used for optimization of hyper-parameters of XGBoost. Additionally, six empirical models and several machine learning models such as XGBoost, random forest, AdaBoost, artificial neural network and Bagging were also considered and applied for comparison of the proposed Jaya-XGBoost model. Accuracy criteria including determination coefficient (R2), root-mean-square error (RMSE), mean absolute error (MAE), and the variance accounted for (VAF) were used for the assessment of models. For this study, 150 blasting operations were analyzed. Also, the Shapley Additive Explanations (SHAP) method is used to interpret the importance of features and their contribution to PPV prediction. Findings reveal that the proposed Jaya-XGBoost emerged as the most reliable model in contrast to other machine learning models and traditional empirical models. This study may be helpful to mining researchers and engineers who use intelligent machine learning algorithms to predict blast-induced ground vibration. © 2021 Elsevier Ltd
- Authors: Zhou, Jian , Qiu, Yingui , Khandelwal, Manoj , Zhu, Shuangli , Zhang, Xiliang
- Date: 2021
- Type: Text , Journal article
- Relation: International Journal of Rock Mechanics and Mining Sciences Vol. 145, no. (2021), p.
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- Description: Blasting is still being considered to be one the most important applicable alternatives for conventional excavations. Ground vibration generated due to blasting is an undesirable phenomenon which is harmful for the nearby structures and should be prevented. In this regard, a novel intelligent approach for predicting blast-induced PPV was developed. The distinctive Jaya algorithm and high efficient extreme gradient boosting machine (XGBoost) were applied to obtain the goal, called the Jaya-XGBoost model. Accordingly, 150 sets of data composed of 13 controllable and uncontrollable parameters are chosen as input independent variables and the measured peak particle velocity (PPV) is chosen as an output dependent variable. Also, the Jaya algorithm was used for optimization of hyper-parameters of XGBoost. Additionally, six empirical models and several machine learning models such as XGBoost, random forest, AdaBoost, artificial neural network and Bagging were also considered and applied for comparison of the proposed Jaya-XGBoost model. Accuracy criteria including determination coefficient (R2), root-mean-square error (RMSE), mean absolute error (MAE), and the variance accounted for (VAF) were used for the assessment of models. For this study, 150 blasting operations were analyzed. Also, the Shapley Additive Explanations (SHAP) method is used to interpret the importance of features and their contribution to PPV prediction. Findings reveal that the proposed Jaya-XGBoost emerged as the most reliable model in contrast to other machine learning models and traditional empirical models. This study may be helpful to mining researchers and engineers who use intelligent machine learning algorithms to predict blast-induced ground vibration. © 2021 Elsevier Ltd
- 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
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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.
- 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
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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.
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
Utilization methods and practice of abandoned mines and related rock mechanics under the ecological and double carbon strategy in china—a comprehensive review
- Du, Kun, Xie, Junjie, Khandelwal, Manoj, Zhou, Jian
- Authors: Du, Kun , Xie, Junjie , Khandelwal, Manoj , Zhou, Jian
- Date: 2022
- Type: Text , Journal article , Review
- Relation: Minerals Vol. 12, no. 9 (2022), p.
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- Description: Governance of abandoned mines has become a pressing issue for China. The utilization of abandoned mines is a technology that can solve the problem of governance and recreate the value of mines, which is in line with the current strategic goals of ecological protection and double carbon in China. In this paper, the various utilization models and the advances in rock mechanics of abandoned mines across the globe are summarized and reviewed. The utilization models of abandoned mines can be categorized into four aspects: Energy storage, Waste treatment, Ecological restoration, and carbon dioxide (CO2) sequestration. There are a number of applications and uses of abandoned mines, such as pumped storage, compressed air storage, salt cavern gas/oil storage construction, carbon dioxide storage and utilization, radioactive waste disposal and treatment, and tourism development. Various progress practices of abandoned mines are discussed in detail with emphasis on the national conditions of China. The basic rock mechanics problems and advances involved in the construction of the facilities related to the utilization of abandoned mines are discussed and evaluated. The establishment of relevant research and experimental platforms will contribute to the sustainable development of China’s mining industry and the improvement of clean technologies. © 2022 by the authors.
- Authors: Du, Kun , Xie, Junjie , Khandelwal, Manoj , Zhou, Jian
- Date: 2022
- Type: Text , Journal article , Review
- Relation: Minerals Vol. 12, no. 9 (2022), p.
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- Description: Governance of abandoned mines has become a pressing issue for China. The utilization of abandoned mines is a technology that can solve the problem of governance and recreate the value of mines, which is in line with the current strategic goals of ecological protection and double carbon in China. In this paper, the various utilization models and the advances in rock mechanics of abandoned mines across the globe are summarized and reviewed. The utilization models of abandoned mines can be categorized into four aspects: Energy storage, Waste treatment, Ecological restoration, and carbon dioxide (CO2) sequestration. There are a number of applications and uses of abandoned mines, such as pumped storage, compressed air storage, salt cavern gas/oil storage construction, carbon dioxide storage and utilization, radioactive waste disposal and treatment, and tourism development. Various progress practices of abandoned mines are discussed in detail with emphasis on the national conditions of China. The basic rock mechanics problems and advances involved in the construction of the facilities related to the utilization of abandoned mines are discussed and evaluated. The establishment of relevant research and experimental platforms will contribute to the sustainable development of China’s mining industry and the improvement of clean technologies. © 2022 by the authors.
Experimental investigations on mechanical performance of rocks under fatigue loads and biaxial confinements
- Du, Kun, Li, Xue-feng, Yang, Cheng-zhi, Zhou, Jian, Chen, Shao-jie, Manoj, Khandelwal
- Authors: Du, Kun , Li, Xue-feng , Yang, Cheng-zhi , Zhou, Jian , Chen, Shao-jie , Manoj, Khandelwal
- Date: 2020
- Type: Text , Journal article
- Relation: Journal of Central South University Vol. 27, no. 10 (2020), p. 2985-2998
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- Description: In this research, a series of biaxial compression and biaxial fatigue tests were conducted to investigate the mechanical behaviors of marble and sandstone under biaxial confinements. Experimental results demonstrate that the biaxial compressive strength of rocks under biaxial compression increases firstly, and subsequently decreases with increase of the intermediate principal stress. The fatigue failure characteristics of the rocks in biaxial fatigue tests are functions of the peak value of fatigue loads, the intermediate principal stress and the rock lithology. With the increase of the peak values of fatigue loads, the fatigue lives of rocks decrease. The intermediate principal stress strengthens the resistance ability of rocks to fatigue loads except considering the strength increasing under biaxial confinements. The fatigue lives of rocks increase with the increase of the intermediate principal stress under the same ratio of the fatigue load and their biaxial compressive strength. The acoustic emission (AE) and fragments studies showed that the sandstone has higher ability to resist the fatigue loads compared to the marble, and the marble generated a greater number of smaller fragments after fatigue failure compared to the sandstone. So, it can be inferred that the rock breaking efficiency and rock burst is higher or severer induced by fatigue loading than that induced by monotonous quasi-static loading, especially for hard rocks. © 2020, Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature.
- Authors: Du, Kun , Li, Xue-feng , Yang, Cheng-zhi , Zhou, Jian , Chen, Shao-jie , Manoj, Khandelwal
- Date: 2020
- Type: Text , Journal article
- Relation: Journal of Central South University Vol. 27, no. 10 (2020), p. 2985-2998
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- Description: In this research, a series of biaxial compression and biaxial fatigue tests were conducted to investigate the mechanical behaviors of marble and sandstone under biaxial confinements. Experimental results demonstrate that the biaxial compressive strength of rocks under biaxial compression increases firstly, and subsequently decreases with increase of the intermediate principal stress. The fatigue failure characteristics of the rocks in biaxial fatigue tests are functions of the peak value of fatigue loads, the intermediate principal stress and the rock lithology. With the increase of the peak values of fatigue loads, the fatigue lives of rocks decrease. The intermediate principal stress strengthens the resistance ability of rocks to fatigue loads except considering the strength increasing under biaxial confinements. The fatigue lives of rocks increase with the increase of the intermediate principal stress under the same ratio of the fatigue load and their biaxial compressive strength. The acoustic emission (AE) and fragments studies showed that the sandstone has higher ability to resist the fatigue loads compared to the marble, and the marble generated a greater number of smaller fragments after fatigue failure compared to the sandstone. So, it can be inferred that the rock breaking efficiency and rock burst is higher or severer induced by fatigue loading than that induced by monotonous quasi-static loading, especially for hard rocks. © 2020, Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature.
Experimental investigation and theoretical analysis of indentations on cuboid hard rock using a conical pick under uniaxial lateral stress
- Wang, Shaofeng, Sun, Licheng, Li, Xibing, Zhou, Jian, Du, Kun, Wang, Shanyong, Khandelwal, Manoj
- Authors: Wang, Shaofeng , Sun, Licheng , Li, Xibing , Zhou, Jian , Du, Kun , Wang, Shanyong , Khandelwal, Manoj
- Date: 2022
- Type: Text , Journal article
- Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 8, no. 1 (2022), p.
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- Description: Abstract: Stress conditions are critical in deep hard rock mining and significantly influence hard rock cuttability. The peak cutting force (PCF), cutting work (CW), and specific energy (SE) can reflect rock cuttability and determine the feasibility and saving of mechanized mining to some extent. In this paper, the influence of uniaxial lateral stress on rock cuttability was investigated by an indentation experiment on cuboid rock using a conical pick, and a theoretical model was proposed to analyze the PCF and associated factors. The PCF, CW, and SE were used as indices to measure hard rock cuttability. The regression analyses show that rock cuttability presents as decreasing followed by increasing as uniaxial lateral stresses increases. The theoretical model was established by simplifying rock fragments into three-dimensional ellipse cones, and a formula was derived based on the elastic fracture mechanics theory. The error between the calculated and experimental values is 3.8%, which confirms the accuracy of the prediction formula. Finally, rock fragmentation by using conical picks was successfully applied on the field mining stope by inducing high geostresses to promote adjustments in stress and improve ore-rock cuttability. Highlights: (1)The influences of uniaxial lateral stress on rock cuttability have been investigated.(2)The peak cutting force, cutting work and specific energy can reflect the rock cuttability.(3)A new theoretical model has been proposed to analyze the peak cutting force.(4)The rock fragmentation using conical picks was successfully applied in deep hard rock mining. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
- Authors: Wang, Shaofeng , Sun, Licheng , Li, Xibing , Zhou, Jian , Du, Kun , Wang, Shanyong , Khandelwal, Manoj
- Date: 2022
- Type: Text , Journal article
- Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 8, no. 1 (2022), p.
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- Description: Abstract: Stress conditions are critical in deep hard rock mining and significantly influence hard rock cuttability. The peak cutting force (PCF), cutting work (CW), and specific energy (SE) can reflect rock cuttability and determine the feasibility and saving of mechanized mining to some extent. In this paper, the influence of uniaxial lateral stress on rock cuttability was investigated by an indentation experiment on cuboid rock using a conical pick, and a theoretical model was proposed to analyze the PCF and associated factors. The PCF, CW, and SE were used as indices to measure hard rock cuttability. The regression analyses show that rock cuttability presents as decreasing followed by increasing as uniaxial lateral stresses increases. The theoretical model was established by simplifying rock fragments into three-dimensional ellipse cones, and a formula was derived based on the elastic fracture mechanics theory. The error between the calculated and experimental values is 3.8%, which confirms the accuracy of the prediction formula. Finally, rock fragmentation by using conical picks was successfully applied on the field mining stope by inducing high geostresses to promote adjustments in stress and improve ore-rock cuttability. Highlights: (1)The influences of uniaxial lateral stress on rock cuttability have been investigated.(2)The peak cutting force, cutting work and specific energy can reflect the rock cuttability.(3)A new theoretical model has been proposed to analyze the peak cutting force.(4)The rock fragmentation using conical picks was successfully applied in deep hard rock mining. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
- Zhou, Jian, Shen, Xiaojie, Qiu, Yingui, Shi, Xiuzhi, Khandelwal, Manoj
- Authors: Zhou, Jian , Shen, Xiaojie , Qiu, Yingui , Shi, Xiuzhi , Khandelwal, Manoj
- Date: 2022
- Type: Text , Journal article
- Relation: Tunnelling and Underground Space Technology Vol. 126, no. (2022), p.
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- Description: Microseismic location systems tend to be high-speed and precise. However, the requirement of high precision tends to slow down the calculation speed. Fortunately, metaheuristics are able to alleviate this problem. In this research, metaheuristic algorithms are used to improve the performance of cross-correlation stacking (CCS). CCS has able to provide excellent location accuracy as it uses more information in the entire waveform for location. However, this method often requires more calculation time due to its complex mathematical modeling. To overcome this problem, various metaheuristic algorithms (i.e. moth flame optimization (MFO), ant lion optimization (ALO) and grey wolf optimization (GWO)) have been used to improve CCS. It has been found that appropriate control parameters can improve the metaheuristic algorithm performance manyfold. So, these control parameters have been adjusted based on three different perspectives, i.e. success rate (SR), computational efficiency and convergence performance. The results show that these models are able to provide better location efficiency compared to the full grid search (FGS) and particle swarm optimization (PSO) based on ensuring good location accuracy. It is also found that MFO is significantly better than the other metaheuristic algorithms. In addition, the superiority of CCS over traditional location methods is verified through comprehensive tests, and the influence of the speed model and the number of sensors on the location performance of CCS was tested. © 2022 Elsevier Ltd
Stress–strain relationship of sandstone under confining pressure with repetitive impact
- Wang, Shiming, Xiong, Xianrui, Liu, Yunsi, Zhou, Jian, Khandelwal, Manoj
- Authors: Wang, Shiming , Xiong, Xianrui , Liu, Yunsi , Zhou, Jian , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 7, no. 2 (2021), p.
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- Description: Abstract: A series of triaxial repetitive impact tests were conducted on a 50-mm-diameter split Hopkinson pressure bar testing device to reveal the characteristics of dynamic stress–strain of sandstone under confining pressure, and the confining pressure in this study was set as 5 and 10 MPa. The results showed that sandstone is very sensitive to confining pressure and strain rate. As the confining pressure and strain rate increases, the dynamic strength, critical strain and absorbed energy also increases, however with the increases in number of impacts, they decrease. With impact numbers increases, the stress–strain curve of sandstone gradually transits from a Class I to a Class II. The dynamic statistical damage constitutive model used in the paper can describe the dynamic response of sandstone under confining pressure with repetitive impact. Various influencing factors, such as material characteristics, confining pressure, strain rate and damage on the dynamic mechanical behavior of sandstone are also fully considered in the model. The damage curve changes from concave to convex as the F/ F increase. When the F/ F exceed 0.5, the damage curve appears convex, and the damage is obvious. By comparing with the variation of the reflected wave waveform with the impact numbers, it is found that damage evolution law of the rock under confining pressure with the impact numbers is similar to that of the reflected wave waveform with the impact numbers, can reflect the damage degree of the rock specimen without other auxiliary equipment, which has been verified. Article Highlights: The stress-strain curve of sandstone under confining pressure with repeated impact changes from Class I to Class II, and it will become less obvious as the confining pressure increases.The constitutive model used in the article can well describe the dynamic mechanical properties, strain rate effect and its turning point of rock under confining pressure with repeated impact.The damage curve changes from concave to convex, and the damage evolution law is similar to that of the reflected wave waveform with the impact numbers. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Manoj Khandelwal” is provided in this record**
- Authors: Wang, Shiming , Xiong, Xianrui , Liu, Yunsi , Zhou, Jian , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 7, no. 2 (2021), p.
- Full Text:
- Reviewed:
- Description: Abstract: A series of triaxial repetitive impact tests were conducted on a 50-mm-diameter split Hopkinson pressure bar testing device to reveal the characteristics of dynamic stress–strain of sandstone under confining pressure, and the confining pressure in this study was set as 5 and 10 MPa. The results showed that sandstone is very sensitive to confining pressure and strain rate. As the confining pressure and strain rate increases, the dynamic strength, critical strain and absorbed energy also increases, however with the increases in number of impacts, they decrease. With impact numbers increases, the stress–strain curve of sandstone gradually transits from a Class I to a Class II. The dynamic statistical damage constitutive model used in the paper can describe the dynamic response of sandstone under confining pressure with repetitive impact. Various influencing factors, such as material characteristics, confining pressure, strain rate and damage on the dynamic mechanical behavior of sandstone are also fully considered in the model. The damage curve changes from concave to convex as the F/ F increase. When the F/ F exceed 0.5, the damage curve appears convex, and the damage is obvious. By comparing with the variation of the reflected wave waveform with the impact numbers, it is found that damage evolution law of the rock under confining pressure with the impact numbers is similar to that of the reflected wave waveform with the impact numbers, can reflect the damage degree of the rock specimen without other auxiliary equipment, which has been verified. Article Highlights: The stress-strain curve of sandstone under confining pressure with repeated impact changes from Class I to Class II, and it will become less obvious as the confining pressure increases.The constitutive model used in the article can well describe the dynamic mechanical properties, strain rate effect and its turning point of rock under confining pressure with repeated impact.The damage curve changes from concave to convex, and the damage evolution law is similar to that of the reflected wave waveform with the impact numbers. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Manoj Khandelwal” is provided in this record**
A combination of expert-based system and advanced decision-tree algorithms to predict air-overpressure resulting from quarry blasting
- He, Ziguang, Armaghani, Danial, Masoumnezhad, Mojtaba, Khandelwal, Manoj, Zhou, Jian, Murlidhar, Bhatawdekar
- Authors: He, Ziguang , Armaghani, Danial , Masoumnezhad, Mojtaba , Khandelwal, Manoj , Zhou, Jian , Murlidhar, Bhatawdekar
- Date: 2021
- Type: Text , Journal article
- Relation: Natural Resources Research Vol. 30, no. 2 (2021), p. 1889-1903
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- Description: This study combined a fuzzy Delphi method (FDM) and two advanced decision-tree algorithms to predict air-overpressure (AOp) caused by mine blasting. The FDM was used for input selection. Thus, the panel of experts selected four inputs, including powder factor, max charge per delay, stemming length, and distance from the blast face. Once the input selection was completed, two decision-tree algorithms, namely extreme gradient boosting tree (XGBoost-tree) and random forest (RF), were applied using the inputs selected by the experts. The models are evaluated with the following criteria: correlation coefficient, mean absolute error, gains chart, and Taylor diagram. The applied models were compared with the XGBoost-tree and RF models using the full set of data without input selection results. The results of hybridization showed that the XGBoost-tree model outperformed the RF model. Concerning the gains, the XGBoost-tree again outperformed the RF model. In comparison with the single decision-tree models, the single models had slightly better correlation coefficients; however, the hybridized models were simpler and easier to understand, analyze and implement. In addition, the Taylor diagram showed that the models applied outperformed some other conventional machine learning models, including support vector machine, k-nearest neighbors, and artificial neural network. Overall, the findings of this study suggest that combining expert opinion and advanced decision-tree algorithms can result in accurate and easy to understand predictions of AOp resulting from blasting in quarry sites. © 2020, International Association for Mathematical Geosciences.
- Authors: He, Ziguang , Armaghani, Danial , Masoumnezhad, Mojtaba , Khandelwal, Manoj , Zhou, Jian , Murlidhar, Bhatawdekar
- Date: 2021
- Type: Text , Journal article
- Relation: Natural Resources Research Vol. 30, no. 2 (2021), p. 1889-1903
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- Description: This study combined a fuzzy Delphi method (FDM) and two advanced decision-tree algorithms to predict air-overpressure (AOp) caused by mine blasting. The FDM was used for input selection. Thus, the panel of experts selected four inputs, including powder factor, max charge per delay, stemming length, and distance from the blast face. Once the input selection was completed, two decision-tree algorithms, namely extreme gradient boosting tree (XGBoost-tree) and random forest (RF), were applied using the inputs selected by the experts. The models are evaluated with the following criteria: correlation coefficient, mean absolute error, gains chart, and Taylor diagram. The applied models were compared with the XGBoost-tree and RF models using the full set of data without input selection results. The results of hybridization showed that the XGBoost-tree model outperformed the RF model. Concerning the gains, the XGBoost-tree again outperformed the RF model. In comparison with the single decision-tree models, the single models had slightly better correlation coefficients; however, the hybridized models were simpler and easier to understand, analyze and implement. In addition, the Taylor diagram showed that the models applied outperformed some other conventional machine learning models, including support vector machine, k-nearest neighbors, and artificial neural network. Overall, the findings of this study suggest that combining expert opinion and advanced decision-tree algorithms can result in accurate and easy to understand predictions of AOp resulting from blasting in quarry sites. © 2020, International Association for Mathematical Geosciences.
Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms
- Li, Enming, Yang, Fenghao, Ren, Meiheng, Zhang, Xiliang, Zhou, Jian, Khandelwal, Manoj
- Authors: Li, Enming , Yang, Fenghao , Ren, Meiheng , Zhang, Xiliang , Zhou, Jian , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 13, no. 6 (2021), p. 1380-1397
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- Description: The main purpose of blasting operation is to produce desired and optimum mean size rock fragments. Smaller or fine fragments cause the loss of ore during loading and transportation, whereas large or coarser fragments need to be further processed, which enhances production cost. Therefore, accurate prediction of rock fragmentation is crucial in blasting operations. Mean fragment size (MFS) is a crucial index that measures the goodness of blasting designs. Over the past decades, various models have been proposed to evaluate and predict blasting fragmentation. Among these models, artificial intelligence (AI)-based models are becoming more popular due to their outstanding prediction results for multi-influential factors. In this study, support vector regression (SVR) techniques are adopted as the basic prediction tools, and five types of optimization algorithms, i.e. grid search (GS), grey wolf optimization (GWO), particle swarm optimization (PSO), genetic algorithm (GA) and salp swarm algorithm (SSA), are implemented to improve the prediction performance and optimize the hyper-parameters. The prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI techniques. Among all the models, the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting operation. Three types of mathematical indices, i.e. mean square error (MSE), coefficient of determination (R2) and variance accounted for (VAF), are utilized for evaluating the performance of different prediction models. The R2, MSE and VAF values for the training set are 0.8355, 0.00138 and 80.98, respectively, whereas 0.8353, 0.00348 and 82.41, respectively for the testing set. Finally, sensitivity analysis is performed to understand the influence of input parameters on MFS. It shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength. © 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences
- Authors: Li, Enming , Yang, Fenghao , Ren, Meiheng , Zhang, Xiliang , Zhou, Jian , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 13, no. 6 (2021), p. 1380-1397
- Full Text:
- Reviewed:
- Description: The main purpose of blasting operation is to produce desired and optimum mean size rock fragments. Smaller or fine fragments cause the loss of ore during loading and transportation, whereas large or coarser fragments need to be further processed, which enhances production cost. Therefore, accurate prediction of rock fragmentation is crucial in blasting operations. Mean fragment size (MFS) is a crucial index that measures the goodness of blasting designs. Over the past decades, various models have been proposed to evaluate and predict blasting fragmentation. Among these models, artificial intelligence (AI)-based models are becoming more popular due to their outstanding prediction results for multi-influential factors. In this study, support vector regression (SVR) techniques are adopted as the basic prediction tools, and five types of optimization algorithms, i.e. grid search (GS), grey wolf optimization (GWO), particle swarm optimization (PSO), genetic algorithm (GA) and salp swarm algorithm (SSA), are implemented to improve the prediction performance and optimize the hyper-parameters. The prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI techniques. Among all the models, the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting operation. Three types of mathematical indices, i.e. mean square error (MSE), coefficient of determination (R2) and variance accounted for (VAF), are utilized for evaluating the performance of different prediction models. The R2, MSE and VAF values for the training set are 0.8355, 0.00138 and 80.98, respectively, whereas 0.8353, 0.00348 and 82.41, respectively for the testing set. Finally, sensitivity analysis is performed to understand the influence of input parameters on MFS. It shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength. © 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences
Intelligent modeling of blast-induced rock movement prediction using dimensional analysis and optimized artificial neural network technique
- Yu, Zhi, Shi, Xiaohu, Miao, Xiaohu, Zhou, Jian, Khandelwal, Manoj
- Authors: Yu, Zhi , Shi, Xiaohu , Miao, Xiaohu , Zhou, Jian , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: International Journal of Rock Mechanics and Mining Sciences Vol. 143, no. (2021), p.
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- Description: For maximum metal recovery, considering the movement of ore and waste during the blasting process in loading design is meaningful for reducing ore loss and ore dilution in an open-pit mine. The blast-induced rock movement (BIRM) can be directly measured; nevertheless, it is time-consuming and relative expensive. To solve this problem, a novel intelligent prediction model was proposed by using dimensional analysis and optimized artificial neural network technique in this paper based on the BIRM monitoring test in Husab Uranium Mine, Namibia and Phoenix Mine, USA. After using dimensional analysis, five input variables and one output variable were determined with both considering the dimension and physical meaning of each dimensionless variable. Then, artificial neural network technique (ANN) technique was utilized to develop an accurate prediction model, and a metaheuristic algorithm namely the Equilibrium Optimizer (EO) algorithm was applied to search the optimal hyper-parameter combination. For comparison aims, a linear model and a non-linear regression model were also performed, and the comparison results show that the provided hybrid ANN-based model can yield better prediction performance. As a result, it can be concluded that the developed intelligent model in this article has the potential to predict BIRM during bench blasting, and the analysis method and modeling process in this paper can provide a reference for solving other engineering problems. © 2021 Elsevier Ltd. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Manoj Khandelwal” is provided in this record**
- Authors: Yu, Zhi , Shi, Xiaohu , Miao, Xiaohu , Zhou, Jian , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: International Journal of Rock Mechanics and Mining Sciences Vol. 143, no. (2021), p.
- Full Text:
- Reviewed:
- Description: For maximum metal recovery, considering the movement of ore and waste during the blasting process in loading design is meaningful for reducing ore loss and ore dilution in an open-pit mine. The blast-induced rock movement (BIRM) can be directly measured; nevertheless, it is time-consuming and relative expensive. To solve this problem, a novel intelligent prediction model was proposed by using dimensional analysis and optimized artificial neural network technique in this paper based on the BIRM monitoring test in Husab Uranium Mine, Namibia and Phoenix Mine, USA. After using dimensional analysis, five input variables and one output variable were determined with both considering the dimension and physical meaning of each dimensionless variable. Then, artificial neural network technique (ANN) technique was utilized to develop an accurate prediction model, and a metaheuristic algorithm namely the Equilibrium Optimizer (EO) algorithm was applied to search the optimal hyper-parameter combination. For comparison aims, a linear model and a non-linear regression model were also performed, and the comparison results show that the provided hybrid ANN-based model can yield better prediction performance. As a result, it can be concluded that the developed intelligent model in this article has the potential to predict BIRM during bench blasting, and the analysis method and modeling process in this paper can provide a reference for solving other engineering problems. © 2021 Elsevier Ltd. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Manoj Khandelwal” is provided in this record**
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.
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
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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 (
- 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
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- 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.
Low amplitude fatigue performance of sandstone, marble, and granite under high static stress
- Du, Kun, Su, Rui, Zhou, Jian, Wang, Shaofeng, Khandelwal, Manoj
- Authors: Du, Kun , Su, Rui , Zhou, Jian , Wang, Shaofeng , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 7, no. 3 (2021), p.
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- Description: Abstract: Fatigue tests under high static pre-stress loads can provide meaningful results to better understand the time-dependent failure characteristics of rock and rock-like materials. However, fatigue tests under high static pre-stress loads are rarely reported in previous literature. In this study, the rock specimens were loaded with a high static pre-stress representing 70% and 80% of the uniaxial compressive strength (UCS), and cyclic fatigue loads with a low amplitude (i.e., 5%, 7.5% and 10% of the UCS) were applied. The results demonstrate that the fatigue life decreased as the static pre-stress level or amplitude of fatigue loads increased for different rock types. The high static pre-stress affected the fatigue life greatly when the static pre-stress was larger than the damage stress of rocks in uniaxial compression tests. The accumulative fatigue damage exhibited three stages during the fatigue failure process, i.e., crack initiation, uniform velocity, and acceleration, and the fatigue modulus showed an “S-type” change trend. The lateral and volumetric strains had a much higher sensitivity to the cyclic loading and could be used to predict fatigue failure characteristics. It was observed that volumetric strain εv = 0 is a threshold for microcracks coalescence and is an important value for estimating the fatigue life. Article highlights: Fatigue mechanical performance of high static pre-stressed rocks were evaluated.The results demonstrate that the fatigue life decreased as the static pre-stress level increased and the static pre-stress affected the fatigue life more than the amplitude of fatigue loads.The volumetric strain of zero before fatigue loading is a threshold for fatigue failure of rocks under high static stress. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Manoj Khandelwal” is provided in this record**
- Authors: Du, Kun , Su, Rui , Zhou, Jian , Wang, Shaofeng , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 7, no. 3 (2021), p.
- Full Text:
- Reviewed:
- Description: Abstract: Fatigue tests under high static pre-stress loads can provide meaningful results to better understand the time-dependent failure characteristics of rock and rock-like materials. However, fatigue tests under high static pre-stress loads are rarely reported in previous literature. In this study, the rock specimens were loaded with a high static pre-stress representing 70% and 80% of the uniaxial compressive strength (UCS), and cyclic fatigue loads with a low amplitude (i.e., 5%, 7.5% and 10% of the UCS) were applied. The results demonstrate that the fatigue life decreased as the static pre-stress level or amplitude of fatigue loads increased for different rock types. The high static pre-stress affected the fatigue life greatly when the static pre-stress was larger than the damage stress of rocks in uniaxial compression tests. The accumulative fatigue damage exhibited three stages during the fatigue failure process, i.e., crack initiation, uniform velocity, and acceleration, and the fatigue modulus showed an “S-type” change trend. The lateral and volumetric strains had a much higher sensitivity to the cyclic loading and could be used to predict fatigue failure characteristics. It was observed that volumetric strain εv = 0 is a threshold for microcracks coalescence and is an important value for estimating the fatigue life. Article highlights: Fatigue mechanical performance of high static pre-stressed rocks were evaluated.The results demonstrate that the fatigue life decreased as the static pre-stress level increased and the static pre-stress affected the fatigue life more than the amplitude of fatigue loads.The volumetric strain of zero before fatigue loading is a threshold for fatigue failure of rocks under high static stress. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Manoj Khandelwal” is provided in this record**
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.
Six novel hybrid extreme learning machine–swarm intelligence optimization (ELM–SIO) models for predicting backbreak in open-pit blasting
- Li, Chuanqi, Zhou, Jian, Khandelwal, Manoj, Zhang, Xiliang, Monjezi, Masoud, Qiu, Yingui
- Authors: Li, Chuanqi , Zhou, Jian , Khandelwal, Manoj , Zhang, Xiliang , Monjezi, Masoud , Qiu, Yingui
- Date: 2022
- Type: Text , Journal article
- Relation: Natural Resources Research Vol. 31, no. 5 (2022), p. 3017-3039
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- Description: Backbreak (BB) is one of the serious adverse blasting consequences in open-pit mines, because it frequently reduces economic benefits and seriously affects the safety of mines. Therefore, rapid and accurate prediction of BB is of great significance to mine blasting design and other production activities. For this purpose, six different swarm intelligence optimization (SIO) algorithms were proposed to optimize the extreme learning machine (ELM) model for BB prediction, i.e., ELM-based particle swarm optimization (ELM–PSO), ELM-based fruit fly optimization (ELM–FOA), ELM-based whale optimization algorithm (ELM–WOA), ELM-based lion swarm optimization (ELM–LOA), ELM-based seagull optimization algorithm (ELM–SOA) and ELM-based sparrow search algorithm (ELM–SSA). In total, 234 data records from blasting operations in the Sungun mine in Iran were used in this study, including six input parameters (special drilling, spacing, burden, hole length, stemming, powder factor) and one output parameter (i.e., BB). To evaluate the predictive performance of the different optimization models and initial models, six performance indicators including the root mean square error (RMSE), Pearson correlation coefficient (R), determination coefficient (R2), variance accounted for (VAF), mean absolute error (MAE) and sum of square error (SSE) were used to evaluate the models in the training and testing phases. The results show that the ELM–LSO was the best model to predict BB with RMSE of 0.1129 (R: 0.9991, R2: 0.9981, VAF: 99.8135%, MAE: 0.0706 and SSE: 2.0917) in the training phase and 0.2441 in the testing phase (R: 0.9949, R2: 0.9891, VAF: 98.9806%, MAE: 0.1669 and SSE: 4.1710). Hence, ELM techniques combined with SIO algorithms are an effective method to predict BB. © 2022, The Author(s).
- Authors: Li, Chuanqi , Zhou, Jian , Khandelwal, Manoj , Zhang, Xiliang , Monjezi, Masoud , Qiu, Yingui
- Date: 2022
- Type: Text , Journal article
- Relation: Natural Resources Research Vol. 31, no. 5 (2022), p. 3017-3039
- Full Text:
- Reviewed:
- Description: Backbreak (BB) is one of the serious adverse blasting consequences in open-pit mines, because it frequently reduces economic benefits and seriously affects the safety of mines. Therefore, rapid and accurate prediction of BB is of great significance to mine blasting design and other production activities. For this purpose, six different swarm intelligence optimization (SIO) algorithms were proposed to optimize the extreme learning machine (ELM) model for BB prediction, i.e., ELM-based particle swarm optimization (ELM–PSO), ELM-based fruit fly optimization (ELM–FOA), ELM-based whale optimization algorithm (ELM–WOA), ELM-based lion swarm optimization (ELM–LOA), ELM-based seagull optimization algorithm (ELM–SOA) and ELM-based sparrow search algorithm (ELM–SSA). In total, 234 data records from blasting operations in the Sungun mine in Iran were used in this study, including six input parameters (special drilling, spacing, burden, hole length, stemming, powder factor) and one output parameter (i.e., BB). To evaluate the predictive performance of the different optimization models and initial models, six performance indicators including the root mean square error (RMSE), Pearson correlation coefficient (R), determination coefficient (R2), variance accounted for (VAF), mean absolute error (MAE) and sum of square error (SSE) were used to evaluate the models in the training and testing phases. The results show that the ELM–LSO was the best model to predict BB with RMSE of 0.1129 (R: 0.9991, R2: 0.9981, VAF: 99.8135%, MAE: 0.0706 and SSE: 2.0917) in the training phase and 0.2441 in the testing phase (R: 0.9949, R2: 0.9891, VAF: 98.9806%, MAE: 0.1669 and SSE: 4.1710). Hence, ELM techniques combined with SIO algorithms are an effective method to predict BB. © 2022, The Author(s).
Performance of hybrid SCA-RF and HHO-RF models for predicting backbreak in open-pit mine blasting operations
- Zhou, Jian, Dai, Yong, Khandelwal, Manoj, Monjezi, Masoud, Yu, Zhi, Qiu, Yingui
- Authors: Zhou, Jian , Dai, Yong , Khandelwal, Manoj , Monjezi, Masoud , Yu, Zhi , Qiu, Yingui
- Date: 2021
- Type: Text , Journal article
- Relation: Natural Resources Research Vol. 30, no. 6 (2021), p. 4753-4771
- Full Text:
- Reviewed:
- Description: Backbreak is an adverse phenomenon in blasting operation, which can cause, among others, mine walls instability, falling down of machinery, drilling efficiency reduction and stripping ratio enhancement. Therefore, this research aimed to develop two-hybrid RF (Random Forest) prediction models of random forest, which are optimized by Harris hawks optimizer (HHO) and sine cosine algorithm (SCA), for estimation of the backbreak distance. The HHO and SCA algorithms were adopted to determine two hyper-parameters (mtry and ntree) in the RF models, in which root mean square error (RMSE) was utilized as a fitness function. A database with 234 samples was established, in which six variables [i.e., hole length (L), burden (B), spacing (S), stemming (T), special drilling (SD) and powder factor (PF)] were used as input variables, and backbreak was defined as output variable. Additionally, three classical regression models (i.e., extreme learning machine, radial basis function network and general regression neural network) were adopted to verify the superiority of the hybrid RF prediction models. The predictive reliability of the proposed models was assessed by the combination of mean absolute error (MAE), RMSE, variance accounted for (VAF) and Pearson correlation coefficient (R2). The results revealed that the SCA-RF model outperformed all the other prediction models with MAE of (0.0444 and 0.0470), RMSE of (0.0816 and 0.0996), VAF of (96.82 and 95.88) and R2 of (0.9876 and 0.9829) in training and testing stages, respectively. A Gini index generated internally in the RF model showed that backbreak was significantly more sensitive to L and T than to SD. © 2021, International Association for Mathematical Geosciences.
- Authors: Zhou, Jian , Dai, Yong , Khandelwal, Manoj , Monjezi, Masoud , Yu, Zhi , Qiu, Yingui
- Date: 2021
- Type: Text , Journal article
- Relation: Natural Resources Research Vol. 30, no. 6 (2021), p. 4753-4771
- Full Text:
- Reviewed:
- Description: Backbreak is an adverse phenomenon in blasting operation, which can cause, among others, mine walls instability, falling down of machinery, drilling efficiency reduction and stripping ratio enhancement. Therefore, this research aimed to develop two-hybrid RF (Random Forest) prediction models of random forest, which are optimized by Harris hawks optimizer (HHO) and sine cosine algorithm (SCA), for estimation of the backbreak distance. The HHO and SCA algorithms were adopted to determine two hyper-parameters (mtry and ntree) in the RF models, in which root mean square error (RMSE) was utilized as a fitness function. A database with 234 samples was established, in which six variables [i.e., hole length (L), burden (B), spacing (S), stemming (T), special drilling (SD) and powder factor (PF)] were used as input variables, and backbreak was defined as output variable. Additionally, three classical regression models (i.e., extreme learning machine, radial basis function network and general regression neural network) were adopted to verify the superiority of the hybrid RF prediction models. The predictive reliability of the proposed models was assessed by the combination of mean absolute error (MAE), RMSE, variance accounted for (VAF) and Pearson correlation coefficient (R2). The results revealed that the SCA-RF model outperformed all the other prediction models with MAE of (0.0444 and 0.0470), RMSE of (0.0816 and 0.0996), VAF of (96.82 and 95.88) and R2 of (0.9876 and 0.9829) in training and testing stages, respectively. A Gini index generated internally in the RF model showed that backbreak was significantly more sensitive to L and T than to SD. © 2021, International Association for Mathematical Geosciences.