List of Titles

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.

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

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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.

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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.

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**

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**

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.

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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
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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

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**

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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.
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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).

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