- 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.
- Full Text: false
- 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**
Waveform features and failure patterns of hollow cylindrical sandstone specimens under repetitive impact and triaxial confinements
- Wang, Shiming, Liu, Yunsi, Du, Kun, Zhou, Jian, Khandelwal, Manoj
- Authors: Wang, Shiming , Liu, Yunsi , Du, Kun , Zhou, Jian , Khandelwal, Manoj
- Date: 2020
- Type: Text , Journal article
- Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 6, no. 4 (2020), p.
- Full Text:
- Reviewed:
- Description: In underground engineering practice, the surrounding rocks are subjected to a nonuniform stress field with various radial gradients. In this study, a series of conventional triaxial repetitive impact tests using hollow cylindrical sandstone (HOS) specimens were conducted to reveal the impact waveform features and failure properties of rocks under nonuniform stress conditions. The tests were conducted using a modified large diameter split Hopkinson pressure bar testing system. The confining pressure was set as 5, 10 and 12 MPa. The data of specimens under equilibrium stress states were chosen and analyzed, and the results showed that more applied numbers of cyclic impact loads were needed to break rocks with the increase of confining pressure. Three types of cracks, i.e., ring-shaped cracks around the hole in the center of specimens, axial cracks located in the outer cylindrical surface, and lateral cracks fracturing rock fragments into small pieces appeared in HOS specimens. The failure degrees of HOS specimens could be judged by the waveform features of the reflected wave, and the waveform features of reflected wave are similar in the same failure mode, regardless of the impact velocity and the number of impacts, which only affect the failure degree. © 2020, Springer Nature Switzerland AG.
- Description: The work reported here is supported by financial grants from both the National Natural Science Foundation of China (51774326, 41807259, 51604109 51704109).
- Authors: Wang, Shiming , Liu, Yunsi , Du, Kun , Zhou, Jian , Khandelwal, Manoj
- Date: 2020
- Type: Text , Journal article
- Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 6, no. 4 (2020), p.
- Full Text:
- Reviewed:
- Description: In underground engineering practice, the surrounding rocks are subjected to a nonuniform stress field with various radial gradients. In this study, a series of conventional triaxial repetitive impact tests using hollow cylindrical sandstone (HOS) specimens were conducted to reveal the impact waveform features and failure properties of rocks under nonuniform stress conditions. The tests were conducted using a modified large diameter split Hopkinson pressure bar testing system. The confining pressure was set as 5, 10 and 12 MPa. The data of specimens under equilibrium stress states were chosen and analyzed, and the results showed that more applied numbers of cyclic impact loads were needed to break rocks with the increase of confining pressure. Three types of cracks, i.e., ring-shaped cracks around the hole in the center of specimens, axial cracks located in the outer cylindrical surface, and lateral cracks fracturing rock fragments into small pieces appeared in HOS specimens. The failure degrees of HOS specimens could be judged by the waveform features of the reflected wave, and the waveform features of reflected wave are similar in the same failure mode, regardless of the impact velocity and the number of impacts, which only affect the failure degree. © 2020, Springer Nature Switzerland AG.
- Description: The work reported here is supported by financial grants from both the National Natural Science Foundation of China (51774326, 41807259, 51604109 51704109).
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
- Full Text:
- Reviewed:
- 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.
Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization
- Zhou, Jian, Qiu, Yingui, Zhu, Shuangli, Armaghani, Danial, Khandelwal, Manoj, Mohamad, Edy
- Authors: Zhou, Jian , Qiu, Yingui , Zhu, Shuangli , Armaghani, Danial , Khandelwal, Manoj , Mohamad, Edy
- Date: 2021
- Type: Text , Journal article
- Relation: Underground Space Vol. 6, no. 5 (Oct 2021), p. 506-515
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- Description: The advance rate (AR) of a tunnel boring machine (TBM) under hard rock conditions is a key parameter in the successful implementation of tunneling engineering. In this study, we improved the accuracy of prediction models by employing a hybrid model of extreme gradient boosting (XGBoost) with Bayesian optimization (BO) to model the TBM AR. To develop the proposed models, 1286 sets of data were collected from the Peng Selangor Raw Water Transfer tunnel project in Malaysia. The database consists of rock mass and intact rock features, including rock mass rating, rock quality designation, weathered zone, uniaxial compressive strength, and Brazilian tensile strength. Machine specifications, including revolution per minute and thrust force, were considered to predict the TBM AR. The accuracies of the predictive models were examined using the root mean squares error (RMSE) and the coefficient of determination (R-2) between the observed and predicted yield by employing a five-fold cross-validation procedure. Results showed that the BO algorithm can capture better hyper-parameters for the XGBoost prediction model than can the default XGBoost model. The robustness and generalization of the BO-XGBoost model yielded prominent results with RMSE and R-2 values of 0.0967 and 0.9806 (for the testing phase), respectively. The results demonstrated the merits of the proposed BO-XGBoost model. In addition, variable importance through mutual information tests was applied to interpret the XGBoost model and demonstrated that machine parameters have the greatest impact as compared to rock mass and material properties.
- Authors: Zhou, Jian , Qiu, Yingui , Zhu, Shuangli , Armaghani, Danial , Khandelwal, Manoj , Mohamad, Edy
- Date: 2021
- Type: Text , Journal article
- Relation: Underground Space Vol. 6, no. 5 (Oct 2021), p. 506-515
- Full Text:
- Reviewed:
- Description: The advance rate (AR) of a tunnel boring machine (TBM) under hard rock conditions is a key parameter in the successful implementation of tunneling engineering. In this study, we improved the accuracy of prediction models by employing a hybrid model of extreme gradient boosting (XGBoost) with Bayesian optimization (BO) to model the TBM AR. To develop the proposed models, 1286 sets of data were collected from the Peng Selangor Raw Water Transfer tunnel project in Malaysia. The database consists of rock mass and intact rock features, including rock mass rating, rock quality designation, weathered zone, uniaxial compressive strength, and Brazilian tensile strength. Machine specifications, including revolution per minute and thrust force, were considered to predict the TBM AR. The accuracies of the predictive models were examined using the root mean squares error (RMSE) and the coefficient of determination (R-2) between the observed and predicted yield by employing a five-fold cross-validation procedure. Results showed that the BO algorithm can capture better hyper-parameters for the XGBoost prediction model than can the default XGBoost model. The robustness and generalization of the BO-XGBoost model yielded prominent results with RMSE and R-2 values of 0.0967 and 0.9806 (for the testing phase), respectively. The results demonstrated the merits of the proposed BO-XGBoost model. In addition, variable importance through mutual information tests was applied to interpret the XGBoost model and demonstrated that machine parameters have the greatest impact as compared to rock mass and material properties.
Proposing a novel comprehensive evaluation model for the coal burst liability in underground coal mines considering uncertainty factors
- Zhou, Jian, Chen, Chao, Wang, Mingzheng, Khandelwal, Manoj
- Authors: Zhou, Jian , Chen, Chao , Wang, Mingzheng , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: International Journal of Mining Science and Technology Vol. 31, no. 5 (2021), p. 799-812
- Full Text:
- Reviewed:
- Description: Coal burst is a severe hazard that can result in fatalities and damage of facilities in underground coal mines. To address this issue, a robust unascertained combination model is proposed to study the coal burst hazard based on an updated database. Four assessment indexes are used in the model, which are the dynamic failure duration (DT), elastic energy index (WET), impact energy index (KE) and uniaxial compressive strength (RC). Four membership functions, including linear (L), parabolic (P), S and Weibull (W) functions, are proposed to measure the uncertainty level of individual index. The corresponding weights are determined through information entropy (EN), analysis hierarchy process (AHP) and synthetic weights (CW). Simultaneously, the classification criteria, including unascertained cluster (UC) and credible identification principle (CIP), are analyzed. The combination algorithm, consisting of P function, CW and CIP (P-CW-CIP), is selected as the optimal classification model in function of theory analysis and to train the samples. Ultimately, the established ensemble model is further validated through test samples with 100% accuracy. The results reveal that the hybrid model has a great potential in the coal burst hazard evaluation in underground coal mines. © 2021
- Authors: Zhou, Jian , Chen, Chao , Wang, Mingzheng , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: International Journal of Mining Science and Technology Vol. 31, no. 5 (2021), p. 799-812
- Full Text:
- Reviewed:
- Description: Coal burst is a severe hazard that can result in fatalities and damage of facilities in underground coal mines. To address this issue, a robust unascertained combination model is proposed to study the coal burst hazard based on an updated database. Four assessment indexes are used in the model, which are the dynamic failure duration (DT), elastic energy index (WET), impact energy index (KE) and uniaxial compressive strength (RC). Four membership functions, including linear (L), parabolic (P), S and Weibull (W) functions, are proposed to measure the uncertainty level of individual index. The corresponding weights are determined through information entropy (EN), analysis hierarchy process (AHP) and synthetic weights (CW). Simultaneously, the classification criteria, including unascertained cluster (UC) and credible identification principle (CIP), are analyzed. The combination algorithm, consisting of P function, CW and CIP (P-CW-CIP), is selected as the optimal classification model in function of theory analysis and to train the samples. Ultimately, the established ensemble model is further validated through test samples with 100% accuracy. The results reveal that the hybrid model has a great potential in the coal burst hazard evaluation in underground coal mines. © 2021
- 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.
- Full Text: false
- Reviewed:
- 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
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.
- 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**
- 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**
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
- 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
- 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
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.
- 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**
- 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**
Mineral composition and grain size effects on the fracture and Acoustic Emission (AE) characteristics of rocks under compressive and tensile stress
- Du, Kun, Sun, Yu, Zhou, Jian, Khandelwal, Manoj, Gong, Fengqiang
- Authors: Du, Kun , Sun, Yu , Zhou, Jian , Khandelwal, Manoj , Gong, Fengqiang
- Date: 2022
- Type: Text , Journal article
- Relation: Rock Mechanics and Rock Engineering Vol. 55, no. 10 (2022), p. 6445-6474
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- Description: The influence of rock mineral composition and mineral grain size on basic rock strength performance and AE characteristics have been studied, 13 different rocks microstructures are analyzed in an optical microscope thin section using petrographic image analysis, making it possible to determine the mineral composition and mineral texture characteristics of rocks. Then, the basic strength parameters of rock and AE signals generated during fracture propagation were obtained by UCT (uniaxial compression test) and BIT (Brazilian intension test). Finally, the relationship between basic strength parameters and AE characteristics of rock with mineral composition and grain size was analyzed. The results showed that different mineral constituents have significant effects on rock strength. The positive influence of plagioclase content on igneous strength was obtained. Sedimentary rocks strength increases initially and then decreases with the increase of plagioclase content. Besides, with the increase in quartz and K-feldspar content, the strength of the rock was weakened obviously. It is also found that the greater the dimensional deviation of mineral grain, the greater the strength of the rock. The strength of igneous rocks was inversely proportional to the mineral grain size, but there is no correlation between the sedimentary rocks strength and the mineral grain size. Furthermore, the tension–shear crack propagation of rock can effectively distinguish by judging that the data set of the AF–RA density graph was nearby the AF axis or RA axis and the peak frequency data sets of below 100 kHz or more than. Alterations in the rock nature are the main key reasons for the differences between AE hit rate, AE count rate, AE energy, and cumulative energy. The plagioclase content and grain size play a decisive role in AE signal characteristics and failure mode. © 2022, The Author(s).
- Authors: Du, Kun , Sun, Yu , Zhou, Jian , Khandelwal, Manoj , Gong, Fengqiang
- Date: 2022
- Type: Text , Journal article
- Relation: Rock Mechanics and Rock Engineering Vol. 55, no. 10 (2022), p. 6445-6474
- Full Text:
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- Description: The influence of rock mineral composition and mineral grain size on basic rock strength performance and AE characteristics have been studied, 13 different rocks microstructures are analyzed in an optical microscope thin section using petrographic image analysis, making it possible to determine the mineral composition and mineral texture characteristics of rocks. Then, the basic strength parameters of rock and AE signals generated during fracture propagation were obtained by UCT (uniaxial compression test) and BIT (Brazilian intension test). Finally, the relationship between basic strength parameters and AE characteristics of rock with mineral composition and grain size was analyzed. The results showed that different mineral constituents have significant effects on rock strength. The positive influence of plagioclase content on igneous strength was obtained. Sedimentary rocks strength increases initially and then decreases with the increase of plagioclase content. Besides, with the increase in quartz and K-feldspar content, the strength of the rock was weakened obviously. It is also found that the greater the dimensional deviation of mineral grain, the greater the strength of the rock. The strength of igneous rocks was inversely proportional to the mineral grain size, but there is no correlation between the sedimentary rocks strength and the mineral grain size. Furthermore, the tension–shear crack propagation of rock can effectively distinguish by judging that the data set of the AF–RA density graph was nearby the AF axis or RA axis and the peak frequency data sets of below 100 kHz or more than. Alterations in the rock nature are the main key reasons for the differences between AE hit rate, AE count rate, AE energy, and cumulative energy. The plagioclase content and grain size play a decisive role in AE signal characteristics and failure mode. © 2022, The Author(s).
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
- Full Text:
- Reviewed:
- 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.
- Du, Kun, Liu, Minghui, Zhou, Jian, Khandelwal, Manoj
- Authors: Du, Kun , Liu, Minghui , Zhou, Jian , Khandelwal, Manoj
- Date: 2022
- Type: Text , Journal article
- Relation: Mining, Metallurgy and Exploration Vol. 39, no. 2 (2022), p. 433-452
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- Description: The waste rock and tailings backfill into the mined-out areas are the most effective method for solving the environmental pollution and surface disasters for nonferrous metals mines. In practice, the success and availability of backfill operations are dependent on the slurry fluidity and the strength properties of cement backfill. The transport of the slurry through the pipeline to the designated backfilling area relies on its eximious flow properties, while the appropriate strength of the filling body ensures the safe operation of the stope. In this paper, the effects of cement and aggregate types on the slurry fluidity and strength characteristics of cemented backfill are studied in detail, which are often ignored in other pieces of literature. Diffusivity is used as an indicator to evaluate the slurry fluidity. Various slurries whose concentrations ranging from 70%, 73%, 75%, 78%, and 80% are made with different aggregate ratios and cement-sand ratios are tested. It has been shown that slurry fluidity is inversely related to its concentration, but 78% is the “stopping point” for the deterioration of fluidity. The addition of rod-milled sand improves or worsens the cemented backfill (CB) strength depending on the amount of rob-milled sand. The uniaxial compression experiment results on 216 CB specimens produced by different combinations of influencing variables showed that CB specimens made from cement with superior mechanical properties have a higher uniaxial compressive strength (σucs). It has been also found that the effect of aggregate ratio on the CB strength is not singular, but works in conjunction with the curing time and the cement-sand ratio. The longer the curing time and the higher the cement content, the higher the CB’s σucs. To avoid the time-consuming and costly problem of obtaining the strength of the CB from indoor experiments, an SVR model capable of predicting the uniaxial compression strength of CB specimens is proposed, which is optimized by genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The results of the three performance indexes (MAPE, MSE, and R2) show the superior performance of the GA-SVR and PSO-SVR models and the agreement of the predicted results with the experimental results, which indicate that these two models can accurately predict the σucs of CB. © 2022, Society for Mining, Metallurgy & Exploration Inc.
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
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- 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
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- 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.
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
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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).
- 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
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.
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:
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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.
- Zhou, Jian, Dai, Yong, Du, Kun, Khandelwal, Manoj, Li, Chuanqi, Qiu, Yingui
- Authors: Zhou, Jian , Dai, Yong , Du, Kun , Khandelwal, Manoj , Li, Chuanqi , Qiu, Yingui
- Date: 2022
- Type: Text , Journal article
- Relation: Transportation Geotechnics Vol. 36, no. (2022), p.
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- Description: Since conical pick cutting is a complex process of multi-factor coupling effects, theoretical model construction for cutting force prediction is a quite difficult task. In this paper, various novel intelligent models based on chaos-optimized slime mould algorithm (COSMA) and random forest (RF) are proposed for this task. In the proposed COSMA-RF methods, the chaos algorithms with the ergodicity and randomness are introduced to chaotically determine the initial position to form a COSMA, and the SMA and COSMA are used to tune the hyperparameters of RF and mean square error are assigned as a fitness function. Consequently, 205 data samples having seven variables (tensile strength of the rock
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