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Fast and accurate microseismic locating methods, such as the virtual field optimization method (VFOM), are increasingly used by researchers and mine management personnel. The VFOM can accurately locate a microseismic source under a large picking error. However, due to the complexity of the objective function of the VFOM, especially when a large number of sensors are involved, this method may require substantial time for the locating process. To overcome this problem, heuristic algorithms were used to increase the locating speed of the VFOM, and the performances of two heuristic algorithms (particle swarm optimization algorithm (PSO) and genetic algorithm (GA)) for the VFOM were evaluated. In general, the performances of these algorithms are affected by many factors, such as the Number of generations (NG) and Number of populations (NP). To enhance the performance of heuristic algorithms, a parameter tuning method was used to determine the relevant parameters for these algorithms. In contrast to the traditional gradient-based algorithm, heuristic algorithms can greatly improve the location efficiency of the VFOM with almost no loss of accuracy and can avoid falling into the local optimal value. The results showed that the PSO can provide better location accuracy and computational efficiency for the VFOM than those obtained with the GA. Furthermore, the VFOM and traditional methods were compared to discuss the influence of the number of sensors and positioning of the source on the location identification and the superiority of the VFOM-based location identification was verified. Two heuristic algorithms (i.e., GA and PSO) were used to increase the location speed of the VFOM; Heuristic algorithms can effectively enhance the VFOM resistance to local optimal values; PSO achieves a higher location accuracy and computational efficiency for the VFOM than GA.
Jian Zhou; Xiaojie Shen; Yingui Qiu; Enming Li; Dijun Rao; Xiuzhi Shi. Improving the efficiency of microseismic source locating using a heuristic algorithm-based virtual field optimization method. Geomechanics and Geophysics for Geo-Energy and Geo-Resources 2021, 7, 1 -18.
AMA StyleJian Zhou, Xiaojie Shen, Yingui Qiu, Enming Li, Dijun Rao, Xiuzhi Shi. Improving the efficiency of microseismic source locating using a heuristic algorithm-based virtual field optimization method. Geomechanics and Geophysics for Geo-Energy and Geo-Resources. 2021; 7 (3):1-18.
Chicago/Turabian StyleJian Zhou; Xiaojie Shen; Yingui Qiu; Enming Li; Dijun Rao; Xiuzhi Shi. 2021. "Improving the efficiency of microseismic source locating using a heuristic algorithm-based virtual field optimization method." Geomechanics and Geophysics for Geo-Energy and Geo-Resources 7, no. 3: 1-18.
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.
Jian Zhou; Yong Dai; Manoj Khandelwal; Masoud Monjezi; Zhi Yu; Yingui Qiu. Performance of Hybrid SCA-RF and HHO-RF Models for Predicting Backbreak in Open-Pit Mine Blasting Operations. Natural Resources Research 2021, 1 -19.
AMA StyleJian Zhou, Yong Dai, Manoj Khandelwal, Masoud Monjezi, Zhi Yu, Yingui Qiu. Performance of Hybrid SCA-RF and HHO-RF Models for Predicting Backbreak in Open-Pit Mine Blasting Operations. Natural Resources Research. 2021; ():1-19.
Chicago/Turabian StyleJian Zhou; Yong Dai; Manoj Khandelwal; Masoud Monjezi; Zhi Yu; Yingui Qiu. 2021. "Performance of Hybrid SCA-RF and HHO-RF Models for Predicting Backbreak in Open-Pit Mine Blasting Operations." Natural Resources Research , no. : 1-19.
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.
Jian Zhou; Yingui Qiu; Manoj Khandelwal; Shuangli Zhu; Xiliang Zhang. Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations. International Journal of Rock Mechanics and Mining Sciences 2021, 145, 104856 .
AMA StyleJian Zhou, Yingui Qiu, Manoj Khandelwal, Shuangli Zhu, Xiliang Zhang. Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations. International Journal of Rock Mechanics and Mining Sciences. 2021; 145 ():104856.
Chicago/Turabian StyleJian Zhou; Yingui Qiu; Manoj Khandelwal; Shuangli Zhu; Xiliang Zhang. 2021. "Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations." International Journal of Rock Mechanics and Mining Sciences 145, no. : 104856.
To reduce the workload and misjudgment of manually discriminating microseismic events and blasts in mines, an artificial intelligence model called PSO-ELM, based on the extreme learning machine (ELM) optimized by the particle swarm optimization (PSO) algorithm, was applied in this study. Firstly, based on the difference between microseismic events and mine blasts and previous research results, 22 seismic parameters were selected as the discrimination feature parameters and their correlation was analyzed. Secondly, 1600 events were randomly selected from the database of the microseismic monitoring system in Fankou Lead-Zinc Mine to form a sample dataset. Then, the optimal discrimination model was established by investigating the model parameters. Finally, the performance of the model was tested using the sample dataset, and it was compared with the performance of the original ELM model and other commonly used intelligent discrimination models. The results indicate that the discrimination performance of PSO-ELM is the best. The values of the six evaluation indicators are close to the optimal value, which shows that PSO-ELM has great potential for discriminating microseismic events and blasts. The research results obtained can provide a new method for discriminating microseismic events and blasts, and it is of great significance to ensure the safe and smooth operation of mines.
Dijun Rao; Xiuzhi Shi; Jian Zhou; Zhi Yu; Yonggang Gou; Zezhen Dong; Jinzhong Zhang. An Expert Artificial Intelligence Model for Discriminating Microseismic Events and Mine Blasts. Applied Sciences 2021, 11, 6474 .
AMA StyleDijun Rao, Xiuzhi Shi, Jian Zhou, Zhi Yu, Yonggang Gou, Zezhen Dong, Jinzhong Zhang. An Expert Artificial Intelligence Model for Discriminating Microseismic Events and Mine Blasts. Applied Sciences. 2021; 11 (14):6474.
Chicago/Turabian StyleDijun Rao; Xiuzhi Shi; Jian Zhou; Zhi Yu; Yonggang Gou; Zezhen Dong; Jinzhong Zhang. 2021. "An Expert Artificial Intelligence Model for Discriminating Microseismic Events and Mine Blasts." Applied Sciences 11, no. 14: 6474.
Shaft stability evaluation (SSE) is one of the most crucial and important tasks in view of the role of vertical shaft in mining engineering, the accuracy of which determines the safety of on-site workers and the production rate of target mine largely. Existing artificial methods are limited to the amount of data and complex process of modeling as well as rare consideration of comprehensive evaluation model in this field. In this way, this paper introduces a high-efficient model that incorporating the unascertained measurement (UM) and multiple weights (the analysis hierarchy process, entropy and the criteria importance through intercriteria correlation) to meet the engineering requirements. Simultaneously, the main parameters, including surface subsidence velocity, cumulative surface subsidence(CSS), loose strata thickness(LST), the water level drop in aquifer (WLD), shaft wall thickness, construction methods and shaft wall types, and diameter ratio of shaft and shaft lining quality, are prepared to analyze the shaft stability. Linear and nonlinear membership functions are utilized to investigate the index correlation belonging to different risk levels. The stability class is determined through the index measurement vectors and classic classification criteria considering the individual index importance. The confusion matrix-based results show that the ensemble model with optimal structure has inspired performance in SSE with 100% accuracy. Furthermore, the shaft is sensitive to the factors CSS, LST and WLD using the sensitivity analysis. Additionally, some parameters associated with the shaft stability are investigated from Daye Iron mine (China) to validate the applicability of target model, the results of which are consistent to the on-site conditions perfectly. Findings reveal that the constructed model has great potential in assessing the shaft stability, which is beneficial to eliminate the risk of shaft failure in time.
Chao Chen; Jian Zhou; Tao Zhou; Weixun Yong. Evaluation of vertical shaft stability in underground mines: comparison of three weight methods with uncertainty theory. Natural Hazards 2021, 1 -23.
AMA StyleChao Chen, Jian Zhou, Tao Zhou, Weixun Yong. Evaluation of vertical shaft stability in underground mines: comparison of three weight methods with uncertainty theory. Natural Hazards. 2021; ():1-23.
Chicago/Turabian StyleChao Chen; Jian Zhou; Tao Zhou; Weixun Yong. 2021. "Evaluation of vertical shaft stability in underground mines: comparison of three weight methods with uncertainty theory." Natural Hazards , no. : 1-23.
The prediction of the potential of soil liquefaction induced by the earthquake is a vital task in construction engineering and geotechnical engineering. To provide a possible solution to such problems, this paper proposes two support vector machine (SVM) models which are optimized by genetic algorithm (GA) and grey wolf optimizer (GWO) to predict the potential of soil liquefaction. Field observation data based on cone penetration test (CPT), standard penetration test (SPT) and shear wave velocity (VS) test (SWVT) are employed to verify the reliability of the GA–SVM model and the GWO–SVM model, the numbers of input variables of these three field testing data sets are 6, 12 and 8, respectively, and the output result is the potential of soil liquefaction. To verify whether the two optimization algorithms GA and GWO have significantly improved the performance of SVM model, an unoptimized SVM model is served as a reference in this study. And five performance metrics, including classification accuracy rate (ACC), precision rate (PRE), recall rate (REC), F1 score (F1) and AUC are used to evaluate the classification performance of the three models. Results of the study confirm that when CPT-based, SPT-based and SWVT-based test sets are input into three classification models, the highest classification accuracy of 0.9825, 0.9032 and 0.9231, respectively, is achieved with GWO–SVM. And based on these three data sets, the values of AUC obtained by GWO–SVM are all higher than those obtained by GA–SVM. Further, by comparing the other metrics of the three classification models, it is found that the classification performance of the two hybrid models is very similar and significantly better than the SVM, which indicates that GWO–SVM, like GA–SVM, can also be used as a reliable model for predicting soil liquefaction potential.
Jian Zhou; Shuai Huang; Mingzheng Wang; Yingui Qiu. Performance evaluation of hybrid GA–SVM and GWO–SVM models to predict earthquake-induced liquefaction potential of soil: a multi-dataset investigation. Engineering with Computers 2021, 1 -19.
AMA StyleJian Zhou, Shuai Huang, Mingzheng Wang, Yingui Qiu. Performance evaluation of hybrid GA–SVM and GWO–SVM models to predict earthquake-induced liquefaction potential of soil: a multi-dataset investigation. Engineering with Computers. 2021; ():1-19.
Chicago/Turabian StyleJian Zhou; Shuai Huang; Mingzheng Wang; Yingui Qiu. 2021. "Performance evaluation of hybrid GA–SVM and GWO–SVM models to predict earthquake-induced liquefaction potential of soil: a multi-dataset investigation." Engineering with Computers , no. : 1-19.
A 3D numerical model was presented to investigate the blast-induced damage characteristics of highly stressed rock mass. The RHT (Riedel, Hiermaier, and Thoma) model in LS-DYNA was used to simulate the blast-induced damage and its parameters were calibrated by a physical model test. Based on the calibrated numerical model, the influences of confining pressure and free surface span on the blast-induced damage characteristics were investigated. The results show that under uniaxial loading, the crater volume increases with confining pressure increases. The uniaxial static load can change the optimal burden and the critical embedding depth of charge. In stressed rock, the variation law of the crater shape affected by radial tensile fractures is opposite to that affected by reflected tensile fractures. Under the biaxial static load, the crater volume of the borehole placed on the side of the max static load is greater than the other side. The explosion crater can be improved by increasing the free surface span on the same side. Finally, it is suggested that the blasting efficiency can be improved by preferentially detonating the charge on the side of the max static load, and then the charge on the other side can be detonated with a wider free surface span.
Xiaofeng Huo; Xiuzhi Shi; Xianyang Qiu; Hui Chen; Jian Zhou; Shian Zhang; Dijun Rao. Study on Rock Damage Mechanism for Lateral Blasting under High In Situ Stresses. Applied Sciences 2021, 11, 4992 .
AMA StyleXiaofeng Huo, Xiuzhi Shi, Xianyang Qiu, Hui Chen, Jian Zhou, Shian Zhang, Dijun Rao. Study on Rock Damage Mechanism for Lateral Blasting under High In Situ Stresses. Applied Sciences. 2021; 11 (11):4992.
Chicago/Turabian StyleXiaofeng Huo; Xiuzhi Shi; Xianyang Qiu; Hui Chen; Jian Zhou; Shian Zhang; Dijun Rao. 2021. "Study on Rock Damage Mechanism for Lateral Blasting under High In Situ Stresses." Applied Sciences 11, no. 11: 4992.
In recent years, the strong development of urban areas and rapid population growth have contributed significantly to environmental pollution issues, especially SW. Of those, municipal solid waste (MSW) is considered a major concern of waste treatment plants. Nowadays, with the development of science and technology, MSW has been treated and recycled to recover energy. However, the issue of energy recovery and optimization from MSW remains a challenge for waste treatment plants. Therefore, a novel artificial intelligence approach was proposed in this study for predicting the gas yield (GY) generated by energy recovery from MSW with high accuracy. Accordingly, a deep neural network (DNN) was developed to predict GY from MSW. Subsequently, the Moth-Flame optimization (MFO) algorithm was applied to optimize the DNN model and improve its accuracy, called MFO-DNN model. The findings revealed that both the DNN and MFO-DNN models predicted GY very well. Of those, the proposed MFO-DNN model provided dominant performance than the DNN model. Based on the proposed MFO-DNN model, the toxic gases can be thoroughly controlled and optimized to recover the gas field from MSW for waste treatment plants, minimizing negative impacts on the surrounding environment.
Libing Yang; Hoang Nguyen; Xuan-Nam Bui; Trung Nguyen-Thoi; Jian Zhou; Jianing Huang. Prediction of gas yield generated by energy recovery from municipal solid waste using deep neural network and moth-flame optimization algorithm. Journal of Cleaner Production 2021, 311, 127672 .
AMA StyleLibing Yang, Hoang Nguyen, Xuan-Nam Bui, Trung Nguyen-Thoi, Jian Zhou, Jianing Huang. Prediction of gas yield generated by energy recovery from municipal solid waste using deep neural network and moth-flame optimization algorithm. Journal of Cleaner Production. 2021; 311 ():127672.
Chicago/Turabian StyleLibing Yang; Hoang Nguyen; Xuan-Nam Bui; Trung Nguyen-Thoi; Jian Zhou; Jianing Huang. 2021. "Prediction of gas yield generated by energy recovery from municipal solid waste using deep neural network and moth-flame optimization algorithm." Journal of Cleaner Production 311, no. : 127672.
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.
Zhi Yu; Xiuzhi Shi; Xiaohu Miao; Jian Zhou; Manoj Khandelwal; Xin Chen; Yingui Qiu. Intelligent modeling of blast-induced rock movement prediction using dimensional analysis and optimized artificial neural network technique. International Journal of Rock Mechanics and Mining Sciences 2021, 143, 104794 .
AMA StyleZhi Yu, Xiuzhi Shi, Xiaohu Miao, Jian Zhou, Manoj Khandelwal, Xin Chen, Yingui Qiu. Intelligent modeling of blast-induced rock movement prediction using dimensional analysis and optimized artificial neural network technique. International Journal of Rock Mechanics and Mining Sciences. 2021; 143 ():104794.
Chicago/Turabian StyleZhi Yu; Xiuzhi Shi; Xiaohu Miao; Jian Zhou; Manoj Khandelwal; Xin Chen; Yingui Qiu. 2021. "Intelligent modeling of blast-induced rock movement prediction using dimensional analysis and optimized artificial neural network technique." International Journal of Rock Mechanics and Mining Sciences 143, no. : 104794.
Hard rock pillar is a crucial rock mass structure to maintain the stability of underground mine. It needs of special attention to analyze its stability from the point of rock mass quality. In this paper, the geological strength index (GSI) representing the rock mass quality of the hard rock pillar is examined as a new influence factor of stability, and combined with the conventional parameters (uniaxial compressive strength (UCS) of intact rock mass, width of pillar (w), height of pillar (h), the ratio of pillar width to its height (w/h)) to complete the stochastic assessment of the stability of hard rock pillar. The 47 actual cases of hard rock pillar improved by numerical simulation software Flac3D. An empirical formula fitted by the Least Square method and artificial intelligence prediction models are used to estimate the pillar strength combining with the pillar stress to conduct the probability and reliability analysis in the Monte Carlo simulation. The result of stochastic assessment showed that UCS and w still play a vital role in maintaining pillar stability, but the influence of GSI cannot be ignored. It found that the GSI has a greater influence on the sloughing pillars in comparison with stable and failed hard rock pillars. Concluding remarks is that GSI has crucial effects on the stability of hard rock pillars as well as UCS of the rock mass and shape of pillars (w and h). Thus, the GSI should be considered as one of input parameter for pillar design and stability assessment in underground mines. A new empirical formula and artificial intelligence models considering five parameters(GSI, UCS, w, h and H) are developed with the improved 47 actual cases of hard rock pillar to estimate the pillar strength. Two pillar performance functions are determined by the Monte Carlo Simulation technique to complete the stochastic assessment based on the probability and reliability analysis of stability conditions of hard rock pillars. The probability of different conditions of pillars can well indicate the relationship between the new (GSI) and traditional influencing factors (UCS, w, h and H) and the stability of pillars. The GSI index has a greater influence on the sloughing pillars in comparison with stable and failed hard rock pillars.
Chuanqi Li; Jian Zhou; Danial Jahed Armaghani; Wenzhuo Cao; Saffet Yagiz. Stochastic assessment of hard rock pillar stability based on the geological strength index system. Geomechanics and Geophysics for Geo-Energy and Geo-Resources 2021, 7, 1 -24.
AMA StyleChuanqi Li, Jian Zhou, Danial Jahed Armaghani, Wenzhuo Cao, Saffet Yagiz. Stochastic assessment of hard rock pillar stability based on the geological strength index system. Geomechanics and Geophysics for Geo-Energy and Geo-Resources. 2021; 7 (2):1-24.
Chicago/Turabian StyleChuanqi Li; Jian Zhou; Danial Jahed Armaghani; Wenzhuo Cao; Saffet Yagiz. 2021. "Stochastic assessment of hard rock pillar stability based on the geological strength index system." Geomechanics and Geophysics for Geo-Energy and Geo-Resources 7, no. 2: 1-24.
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 \({F \mathord{\left/ {\vphantom {F {F_{0} }}} \right. \kern-\nulldelimiterspace} {F_{0} }}\) increase. When the \({F \mathord{\left/ {\vphantom {F {F_{0} }}} \right. \kern-\nulldelimiterspace} {F_{0} }}\) 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. 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.
Shiming Wang; Xianrui Xiong; Yunsi Liu; Jian Zhou; Kun Du; Yan Cui; Manoj Khandelwal. Stress–strain relationship of sandstone under confining pressure with repetitive impact. Geomechanics and Geophysics for Geo-Energy and Geo-Resources 2021, 7, 1 -16.
AMA StyleShiming Wang, Xianrui Xiong, Yunsi Liu, Jian Zhou, Kun Du, Yan Cui, Manoj Khandelwal. Stress–strain relationship of sandstone under confining pressure with repetitive impact. Geomechanics and Geophysics for Geo-Energy and Geo-Resources. 2021; 7 (2):1-16.
Chicago/Turabian StyleShiming Wang; Xianrui Xiong; Yunsi Liu; Jian Zhou; Kun Du; Yan Cui; Manoj Khandelwal. 2021. "Stress–strain relationship of sandstone under confining pressure with repetitive impact." Geomechanics and Geophysics for Geo-Energy and Geo-Resources 7, no. 2: 1-16.
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.
Yingui Qiu; Jian Zhou; Manoj Khandelwal; Haitao Yang; Peixi Yang; Chuanqi Li. Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration. Engineering with Computers 2021, 1 -18.
AMA StyleYingui Qiu, Jian Zhou, Manoj Khandelwal, Haitao Yang, Peixi Yang, Chuanqi Li. Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration. Engineering with Computers. 2021; ():1-18.
Chicago/Turabian StyleYingui Qiu; Jian Zhou; Manoj Khandelwal; Haitao Yang; Peixi Yang; Chuanqi Li. 2021. "Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration." Engineering with Computers , no. : 1-18.
The stability of cemented paste backfill (CPB) is threatened by dynamic disturbance, but the conventional low strain rate laboratory pressure test has difficulty achieving this research purpose. Therefore, a split Hopkinson pressure bar (SHPB) was utilized to investigate the high strain rate compressive behavior of CPB with dynamic loads of 0.4, 0.8, and 1.2 MPa. And the failure modes were determined by macro and micro analysis. CPB with different cement-to-tailings ratios, solid mass concentrations, and curing ages was prepared to conduct the SHPB test. The results showed that increasing the cement content, tailings content, and curing age can improve the dynamic compressive strength and elastic modulus. Under an impact load, a higher strain rate can lead to larger increasing times of the dynamic compressive strength when compared with static loading. And the dynamic compressive strength of CPB has an exponential correlation with the strain rate. The macroscopic failure modes indicated that CPB is more seriously damaged under dynamic loading. The local damage was enhanced, and fine cracks were formed in the interior of the CPB. This is because the CPB cannot dissipate the energy of the high strain rate stress wave in a short loading period.
Xin Chen; Xiuzhi Shi; Jian Zhou; Enming Li; Peiyong Qiu; Yonggang Gou. High strain rate compressive strength behavior of cemented paste backfill using split Hopkinson pressure bar. International Journal of Mining Science and Technology 2021, 31, 387 -399.
AMA StyleXin Chen, Xiuzhi Shi, Jian Zhou, Enming Li, Peiyong Qiu, Yonggang Gou. High strain rate compressive strength behavior of cemented paste backfill using split Hopkinson pressure bar. International Journal of Mining Science and Technology. 2021; 31 (3):387-399.
Chicago/Turabian StyleXin Chen; Xiuzhi Shi; Jian Zhou; Enming Li; Peiyong Qiu; Yonggang Gou. 2021. "High strain rate compressive strength behavior of cemented paste backfill using split Hopkinson pressure bar." International Journal of Mining Science and Technology 31, no. 3: 387-399.
The accurate determination of blast-induced ground vibration has an important significance in protecting human activities and the surrounding environment. For evaluating the peak particle velocity resulting from the quarry blast, a robust artificial intelligence system combined with the salp swarm algorithm (SSA) and Gaussian process (GP) was proposed, and the SSA was used to find the optimal hyperparameters of the GP here. In this regard, 88 datasets with 9 variables including the ratio of bench height to burden (H/B) and the ratio of spacing to burden (S/B) were selected as the input variables, while peak particle velocity (PPV) was measured. Then, an ANN model, an SVR model, a GP model, an SSA-GP model, and three empirical models were established, and the predictive performance was evaluated by using the root-mean-square error (RMSE), determination coefficient (R2), value account for (VAF), Akaike Information Criterion (AIC), Schwarz Bayesian Criterion (SBC), and the run time. After comparing, it is found that the proposed SSA-GP yielded a superior performance and the ratio of bench height to burden (H/B) was the most sensitive variable.
Zhaoxin Jiang; Hongyan Xu; Hui Chen; Bei Gao; Shijie Jia; Zhi Yu; Jian Zhou. Indirect Determination Approach of Blast-Induced Ground Vibration Based on a Hybrid SSA-Optimized GP-Based Technique. Advances in Civil Engineering 2021, 2021, 1 -14.
AMA StyleZhaoxin Jiang, Hongyan Xu, Hui Chen, Bei Gao, Shijie Jia, Zhi Yu, Jian Zhou. Indirect Determination Approach of Blast-Induced Ground Vibration Based on a Hybrid SSA-Optimized GP-Based Technique. Advances in Civil Engineering. 2021; 2021 ():1-14.
Chicago/Turabian StyleZhaoxin Jiang; Hongyan Xu; Hui Chen; Bei Gao; Shijie Jia; Zhi Yu; Jian Zhou. 2021. "Indirect Determination Approach of Blast-Induced Ground Vibration Based on a Hybrid SSA-Optimized GP-Based Technique." Advances in Civil Engineering 2021, no. : 1-14.
To detect areas with the potential for landslides, slopes are routinely subjected to stability analyses. To this end, there is a need to adopt appropriate mitigation techniques. In general, the stability of slopes with circular failure mode is defined as the factor of safety (FOS). The literature includes a variety of numerical/analytical models proposed in different studies to compute the FOS values of slopes. However, the main challenge is to propose a model for solving a non-linear relationship between independent parameters (which have a great impact on slope stability) and FOS values of slopes. This creates a problem with a high level of complexity and with multiple variables. To resolve the problem, this study proposes a new hybrid intelligent model for FOS evaluation and analysis of slopes in two different phases: simulation and optimization. In the simulation phase, different support vector regression (SVR) kernels were built to predict FOS values. The results showed that the radius basis function (RBF) kernel produces more accurate performance prediction compared with the other applied kernels. The prediction accuracy of this kernel was obtained as coefficient of determination = 0.94, which indicates a high prediction capacity during the simulation phase. Then, in the optimization phase, the proposed SVR model was optimized through the use of two well-known techniques, namely, the whale optimization algorithm (WOA) and Harris hawks optimization (HHO), and the optimum input parameters were obtained. The optimal results confirmed that both optimization techniques are able to achieve a high value for FOS of slopes; however, the HHO shows a more powerful process in FOS maximization compared with the WOA technique. In addition, the developed model was also successfully validated using new data with nine data samples.
Wei Wei; Xibing Li; Jingzhi Liu; Yaodong Zhou; Lu Li; Jian Zhou. Performance Evaluation of Hybrid WOA-SVR and HHO-SVR Models with Various Kernels to Predict Factor of Safety for Circular Failure Slope. Applied Sciences 2021, 11, 1922 .
AMA StyleWei Wei, Xibing Li, Jingzhi Liu, Yaodong Zhou, Lu Li, Jian Zhou. Performance Evaluation of Hybrid WOA-SVR and HHO-SVR Models with Various Kernels to Predict Factor of Safety for Circular Failure Slope. Applied Sciences. 2021; 11 (4):1922.
Chicago/Turabian StyleWei Wei; Xibing Li; Jingzhi Liu; Yaodong Zhou; Lu Li; Jian Zhou. 2021. "Performance Evaluation of Hybrid WOA-SVR and HHO-SVR Models with Various Kernels to Predict Factor of Safety for Circular Failure Slope." Applied Sciences 11, no. 4: 1922.
Rockburst prediction is of vital significance to the design and construction of underground hard rock mines. A rockburst database consisting of 102 case histories, i.e., 1998–2011 period data from 14 hard rock mines was examined for rockburst prediction in burst-prone mines by three tree-based ensemble methods. The dataset was examined with six widely accepted indices which are: the maximum tangential stress around the excavation boundary (MTS), uniaxial compressive strength (UCS) and uniaxial tensile strength (UTS) of the intact rock, stress concentration factor (SCF), rock brittleness index (BI), and strain energy storage index (EEI). Two boosting (AdaBoost.M1, SAMME) and bagging algorithms with classification trees as baseline classifier on ability to learn rockburst were evaluated. The available dataset was randomly divided into training set (2/3 of whole datasets) and testing set (the remaining datasets). Repeated 10-fold cross validation (CV) was applied as the validation method for tuning the hyper-parameters. The margin analysis and the variable relative importance were employed to analyze some characteristics of the ensembles. According to 10-fold CV, the accuracy analysis of rockburst dataset demonstrated that the best prediction method for the potential of rockburst is bagging when compared to AdaBoost.M1, SAMME algorithms and empirical criteria methods.
Shi-Ming Wang; Jian Zhou; Chuan-Qi Li; Danial Jahed Armaghani; Xi-Bing Li; Hani S. Mitri. Rockburst prediction in hard rock mines developing bagging and boosting tree-based ensemble techniques. Journal of Central South University 2021, 28, 527 -542.
AMA StyleShi-Ming Wang, Jian Zhou, Chuan-Qi Li, Danial Jahed Armaghani, Xi-Bing Li, Hani S. Mitri. Rockburst prediction in hard rock mines developing bagging and boosting tree-based ensemble techniques. Journal of Central South University. 2021; 28 (2):527-542.
Chicago/Turabian StyleShi-Ming Wang; Jian Zhou; Chuan-Qi Li; Danial Jahed Armaghani; Xi-Bing Li; Hani S. Mitri. 2021. "Rockburst prediction in hard rock mines developing bagging and boosting tree-based ensemble techniques." Journal of Central South University 28, no. 2: 527-542.
A proper planning schedule for tunnel boring machine (TBM) construction is considered as a necessary and difficult task in tunneling projects. Therefore, prediction of TBM performance with high degree of accuracy is needed to prepare a suitable planning schedule. This study aims to predict the advance rate of TBMs using optimized extreme learning machine (ELM) model with six particles swam optimization (PSO) techniques. Hence, six deterministically adaptive models, including time-varying acceleration (TAC)–PSO–ELM, improved PSO–ELM, Modified PSO–ELM, TAC–MeanPSO–ELM, improved MeanPSO–ELM, and Modified MeanPSO–ELM were developed. A number of performance criteria along with ranking system were used to identify the best model. The results showed that modified MeanPSO–ELM achieved the highest cumulative ranking (56), while the modified PSO–ELM achieved the lowest cumulative ranking (51). For training phase, improved PSO–ELM and TAC–PSO–ELM achieved the highest ranking (30) for each. The TAC–MeanPSO–ELM obtained the lowest ranking in the testing phase (29). Concerning the coefficient of determination (R2), modified PSO–ELM, improved PSO–ELM, TAC–PSO–ELM, and modified MeanPSO–ELM showed a similar behavior and achieved 0.97 for training and 0.96 for testing phases. Two models, including improved MeanPSO–ELM and TAC–MeanPSO–ELM achieved the same R2 of 0.96 for both training and testing phases. The findings of this study suggest that the hybridization of ELM and PSO may result in more accurate results than single ELM model to predict the TBM advance rate.
Jie Zeng; Bishwajit Roy; Deepak Kumar; Ahmed Salih Mohammed; Danial Jahed Armaghani; Jian Zhou; Edy Tonnizam Mohamad. Proposing several hybrid PSO-extreme learning machine techniques to predict TBM performance. Engineering with Computers 2021, 1 -17.
AMA StyleJie Zeng, Bishwajit Roy, Deepak Kumar, Ahmed Salih Mohammed, Danial Jahed Armaghani, Jian Zhou, Edy Tonnizam Mohamad. Proposing several hybrid PSO-extreme learning machine techniques to predict TBM performance. Engineering with Computers. 2021; ():1-17.
Chicago/Turabian StyleJie Zeng; Bishwajit Roy; Deepak Kumar; Ahmed Salih Mohammed; Danial Jahed Armaghani; Jian Zhou; Edy Tonnizam Mohamad. 2021. "Proposing several hybrid PSO-extreme learning machine techniques to predict TBM performance." Engineering with Computers , no. : 1-17.
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 (Rη) 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 Rη. 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.
Jian Zhou; Chao Chen; Manoj Khandelwal; Ming Tao; Chuanqi Li. Novel approach to evaluate rock mass fragmentation in block caving using unascertained measurement model and information entropy with flexible credible identification criterion. Engineering with Computers 2021, 1 -21.
AMA StyleJian Zhou, Chao Chen, Manoj Khandelwal, Ming Tao, Chuanqi Li. Novel approach to evaluate rock mass fragmentation in block caving using unascertained measurement model and information entropy with flexible credible identification criterion. Engineering with Computers. 2021; ():1-21.
Chicago/Turabian StyleJian Zhou; Chao Chen; Manoj Khandelwal; Ming Tao; Chuanqi Li. 2021. "Novel approach to evaluate rock mass fragmentation in block caving using unascertained measurement model and information entropy with flexible credible identification criterion." Engineering with Computers , no. : 1-21.
This paper attempts to examine whether socioeconomic volatility produces differentiated effects on road traffic accident indicators. Adopting the Autoregressive distributed lag error-correction model (ARDL-ECM), this paper explores the long-term equilibrium and short-term interactions between five common economic indicators, namely, average salaries (AS), employment (EM), unemployment (UE), total mileage of highway (TMH), and private vehicle ownership (PVO), as well as road traffic-related indicators including the number of road traffic accidents (RTA), injuries (IN), fatalities (FA), and direct economic losses (DEL), using data of road traffic accidents spanning from 1999 to 2018 in China. The study found that all economic indicators except average salaries showed a long-term equilibrium with road traffic accident indicators. The Granger causality test showed that, over the short term, an increase in employment could lead to an increase in injuries, and an increase in private vehicle ownership could cause a rise in fatalities. This study demonstrates that the volatility in economic indicators indeed produces differentiated effects on road traffic accident indicators, providing a theoretical basis for improving road safety performance and formulating relevant policies.
Xibing Li; Jiao Liu; Jian Zhou; Xiling Liu; Lei Zhou; Wei Wei. The Effects of Macroeconomic Factors on Road Traffic Safety: A Study Based on the ARDL-ECM Model. Sustainability 2020, 12, 10262 .
AMA StyleXibing Li, Jiao Liu, Jian Zhou, Xiling Liu, Lei Zhou, Wei Wei. The Effects of Macroeconomic Factors on Road Traffic Safety: A Study Based on the ARDL-ECM Model. Sustainability. 2020; 12 (24):10262.
Chicago/Turabian StyleXibing Li; Jiao Liu; Jian Zhou; Xiling Liu; Lei Zhou; Wei Wei. 2020. "The Effects of Macroeconomic Factors on Road Traffic Safety: A Study Based on the ARDL-ECM Model." Sustainability 12, no. 24: 10262.
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.
Ziguang He; Danial Jahed Armaghani; Mojtaba Masoumnezhad; Manoj Khandelwal; Jian Zhou; Bhatawdekar Ramesh Murlidhar. A Combination of Expert-Based System and Advanced Decision-Tree Algorithms to Predict Air-Overpressure Resulting from Quarry Blasting. Natural Resources Research 2020, 30, 1889 -1903.
AMA StyleZiguang He, Danial Jahed Armaghani, Mojtaba Masoumnezhad, Manoj Khandelwal, Jian Zhou, Bhatawdekar Ramesh Murlidhar. A Combination of Expert-Based System and Advanced Decision-Tree Algorithms to Predict Air-Overpressure Resulting from Quarry Blasting. Natural Resources Research. 2020; 30 (2):1889-1903.
Chicago/Turabian StyleZiguang He; Danial Jahed Armaghani; Mojtaba Masoumnezhad; Manoj Khandelwal; Jian Zhou; Bhatawdekar Ramesh Murlidhar. 2020. "A Combination of Expert-Based System and Advanced Decision-Tree Algorithms to Predict Air-Overpressure Resulting from Quarry Blasting." Natural Resources Research 30, no. 2: 1889-1903.