Effects of a proper feature selection on prediction and optimization of drilling rate using intelligent techniques
- Authors: Liao, Xiufeng , Khandelwal, Manoj , Yang, Haiqing , Koopialipoor, Mohammadreza , Murlidhar, Bhatawdekar
- Date: 2020
- Type: Text , Journal article
- Relation: Engineering with Computers Vol. 36, no. 2 (Apr 2020), p. 499-510
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- Description: One of the important factors during drilling times is the rate of penetration (ROP), which is controlled based on different variables. Factors affecting different drillings are of paramount importance. In the current research, an attempt was made to better recognize drilling parameters and optimize them based on an optimization algorithm. For this purpose, 618 data sets, including RPM, flushing media, and compressive strength parameters, were measured and collected. After an initial investigation, the compressive strength feature of samples, which is an important parameter from the rocks, was used as a proper criterion for classification. Then using intelligent systems, three different levels of the rock strength and all data were modeled. The results showed that systems which were classified based on compressive strength showed a better performance for ROP assessment due to the proximity of features. Therefore, these three levels were used for classification. A new artificial bee colony algorithm was used to solve this problem. Optimizations were applied to the selected models under different optimization conditions, and optimal states were determined. As determining drilling machine parameters is important, these parameters were determined based on optimal conditions. The obtained results showed that this intelligent system can well improve drilling conditions and increase the ROP value for three strength levels of the rocks. This modeling system can be used in different drilling operations.
Waveform features and failure patterns of hollow cylindrical sandstone specimens under repetitive impact and triaxial confinements
- 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).
Rock-burst occurrence prediction based on optimized naïve bayes models
- Authors: Ke, Bo , Khandelwal, Manoj , Asteris, Panagiotis , Skentou, Athanasia , Mamou, Anna , Armaghani, Danial
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 91347-91360
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- Description: Rock-burst is a common failure in hard rock related projects in civil and mining construction and therefore, proper classification and prediction of this phenomenon is of interest. This research presents the development of optimized naïve Bayes models, in predicting rock-burst failures in underground projects. The naïve Bayes models were optimized using four weight optimization techniques including forward, backward, particle swarm optimization, and evolutionary. An evolutionary random forest model was developed to identify the most significant input parameters. The maximum tangential stress, elastic energy index, and uniaxial tensile stress were then selected by the feature selection technique (i.e., evolutionary random forest) to develop the optimized naïve Bayes models. The performance of the models was assessed using various criteria as well as a simple ranking system. The results of this research showed that particle swarm optimization was the most effective technique in improving the accuracy of the naïve Bayes model for rock-burst prediction (cumulative ranking = 21), while the backward technique was the worst weight optimization technique (cumulative ranking = 11). All the optimized naïve Bayes models identified the maximum tangential stress as the most significant parameter in predicting rock-burst failures. The results of this research demonstrate that particle swarm optimization technique may improve the accuracy of naïve Bayes algorithms in predicting rock-burst occurrence. © 2013 IEEE.
Optimization of blasting design in open pit limestone mines with the aim of reducing ground vibration using robust techniques
- Authors: Rezaeineshat, Afsaneh , Monjezi, Masoud , Mehrdanesh, Amirhossein , Khandelwal, Manoj
- Date: 2020
- Type: Text , Journal article
- Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 6, no. 2 (2020), p.
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- Description: Blasting operations create significant problems to residential and other structures located in the close proximity of the mines. Blast vibration is one of the most crucial nuisances of blasting, which should be accurately estimated to minimize its effect. In this paper, an attempt has been made to apply various models to predict ground vibrations due to mine blasting. To fulfill this aim, 112 blast operations were precisely measured and collected in one the limestone mines of Iran. These blast operation data were utilized to construct the artificial neural network (ANN) model to predict the peak particle velocity (PPV). The input parameters used in this study were burden, spacing, maximum charge per delay, distance from blast face to monitoring point and rock quality designation and output parameter was the PPV. The conventional empirical predictors and multivariate regression analysis were also performed on the same data sets to study the PPV. Accordingly, it was observed that the ANN model is more accurate as compared to the other employed predictors. Moreover, it was also revealed that the most influential parameters on the ground vibration are distance from the blast and maximum charge per delay, whereas the least effective parameters are burden, spacing and rock quality designation. Finally, in order to minimize PPV, the developed ANN model was used as an objective function for imperialist competitive algorithm (ICA). Eventually, it was found that the ICA algorithm is able to decrease PPV up to 59% by considering burden of 2.9 m, spacing of 4.4 m and charge per delay of 627 Kg. © 2020, Springer Nature Switzerland AG.
Evaluation and assessment of blast-induced ground vibrations in an underground gold mine : a case study
- Authors: Tribe, Jarryd , Koroznikova, Larissa , Khandelwal, Manoj , Giri, Jason
- Date: 2021
- Type: Text , Journal article
- Relation: Natural Resources Research Vol. 30, no. 6 (2021), p. 4673-4694
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- Description: Ground vibrations induced during rock fragmentation by blasting remain a potential source of hazard for the stability of nearby structures. In this paper, to forecast the effect of blast-induced ground vibrations, dimensional analysis (DA) is proposed to predict peak particle velocity (PPV). In conventional predictor equations, the major and critical parameter for the estimation of PPV is square root scaled distance. The new formula based on DA was obtained considering various blast design parameters in order to improve the capability of PPV prediction. After obtaining the new DA equation for the prediction of PPV, 360 data sets were used to determine the unknown coefficients of the new equation as well as site constants of different conventional predictor equations. Then, ten additional randomly selected data sets were used to compare the capability of the new model with conventional predictor equations. The results were compared based on coefficient of determination (R2) and mean absolute error (MAE) between measured and predicted values of PPV. The proposed formula with the greatest R2 and the lowest MAE was the better option for predicting the PPV of induced vibrations for the measured field data. © 2021, International Association for Mathematical Geosciences.
Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization
- 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.
Proposing a novel comprehensive evaluation model for the coal burst liability in underground coal mines considering uncertainty factors
- 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
Stability prediction of Himalayan residual soil slope using artificial neural network
- Authors: Ray, Arunava , Kumar, Vikash , Kumar, Amit , Rai, Rajesh , Khandelwal, Manoj , Singh, T.
- Date: 2020
- Type: Text , Journal article
- Relation: Natural Hazards Vol. 103, no. 3 (2020), p. 3523-3540
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- Description: In the past decade, advances in machine learning (ML) techniques have resulted in developing sophisticated models that are capable of modelling extremely complex multi-factorial problems like slope stability analysis. The literature review indicates that considerable works have been done in slope stability using ML, but none of them covers the analysis of residual soil slope. The present study aims to develop an artificial neural network (ANN) model that can be employed for evaluating the factor of safety of Shiwalik Slopes in the Himalayan Region. Data obtained from numerical analysis of a residual soil slope were used to develop two ANN models (ANN1 and ANN2 utilising eleven input parameters, and scaled-down number of parameters based on correlation coefficient, respectively). A four-layer, feed-forward back-propagation neural network having the optimum number of hidden neurons is developed based on trial-and-error method. The results derived from ANN models were compared with those achieved from numerical analysis. Additionally, several performance indices such as coefficient of determination (R2), root mean square error, variance account for, and residual error were employed to evaluate the predictive performance of the developed ANN models. Both the ANN models have shown good prediction performance; however, the overall performance of the ANN2 model is better than the ANN1 model. It is concluded that the ANN models are reliable, valid, and straightforward computational tools that can be employed for slope stability analysis during the preliminary stage of designing infrastructure projects in residual soil slope. © 2020, Springer Nature B.V.
Stress–strain relationship of sandstone under confining pressure with repetitive impact
- 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**
Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms
- Authors: Li, Enming , Yang, Fenghao , Ren, Meiheng , Zhang, Xiliang , Zhou, Jian , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 13, no. 6 (2021), p. 1380-1397
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- Description: The main purpose of blasting operation is to produce desired and optimum mean size rock fragments. Smaller or fine fragments cause the loss of ore during loading and transportation, whereas large or coarser fragments need to be further processed, which enhances production cost. Therefore, accurate prediction of rock fragmentation is crucial in blasting operations. Mean fragment size (MFS) is a crucial index that measures the goodness of blasting designs. Over the past decades, various models have been proposed to evaluate and predict blasting fragmentation. Among these models, artificial intelligence (AI)-based models are becoming more popular due to their outstanding prediction results for multi-influential factors. In this study, support vector regression (SVR) techniques are adopted as the basic prediction tools, and five types of optimization algorithms, i.e. grid search (GS), grey wolf optimization (GWO), particle swarm optimization (PSO), genetic algorithm (GA) and salp swarm algorithm (SSA), are implemented to improve the prediction performance and optimize the hyper-parameters. The prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI techniques. Among all the models, the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting operation. Three types of mathematical indices, i.e. mean square error (MSE), coefficient of determination (R2) and variance accounted for (VAF), are utilized for evaluating the performance of different prediction models. The R2, MSE and VAF values for the training set are 0.8355, 0.00138 and 80.98, respectively, whereas 0.8353, 0.00348 and 82.41, respectively for the testing set. Finally, sensitivity analysis is performed to understand the influence of input parameters on MFS. It shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength. © 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences
Low amplitude fatigue performance of sandstone, marble, and granite under high static stress
- 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**
Mineral composition and grain size effects on the fracture and Acoustic Emission (AE) characteristics of rocks under compressive and tensile stress
- 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).
A combination of expert-based system and advanced decision-tree algorithms to predict air-overpressure resulting from quarry blasting
- 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.
Development of the scaled boundary finite element method for image-based slope stability analysis
- Authors: Wijesinghe, Dakshith , Dyson, Ashley , You, Greg , Khandelwal, Manoj , Song, Chongmin , Ooi, Ean Tat
- Date: 2022
- Type: Text , Journal article
- Relation: Computers and Geotechnics Vol. 143, no. (2022), p.
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- Description: This paper presents a numerical technique for geotechnical slope stability analysis, integrating digital image meshing with the scaled boundary finite element method, allowing site conditions such as complex stratigraphies, surface and internal geometry evolution to be simulated in a robust and straightforward procedure. The quadtree decomposition technique is used to automatically discretise the geometry directly from digital images using pixel information to accurately capture boundaries with fine-scale elements. The process allows complex numerical models to be generated from cross-section images of slopes, capitalising on the combination of the scaled boundary finite element method and quadtree meshing. The spatial distribution of the soil material properties can be represented by the colour of each pixel. A mapping technique is developed to integrate these parameters into the computational mesh. The feasibility of the proposed method is presented through case study simulations of an active large Australian open-pit mine, considering various aspects of complex features such as geometry, stratigraphy and material behaviour. © 2021
Investigating the slurry fluidity and strength characteristics of cemented backfill and strength prediction models by developing hybrid GA-SVR and PSO-SVR
- 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
- 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
- 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).
An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass
- Authors: Parsajoo, Maryama , Mohammed, Ahmed , Yagiz, Saffet , Armaghani, Danial , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 13, no. 6 (2021), p. 1290-1299
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- Description: Field penetration index (FPI) is one of the representative key parameters to examine the tunnel boring machine (TBM) performance. Lack of accurate FPI prediction can be responsible for numerous disastrous incidents associated with rock mechanics and engineering. This study aims to predict TBM performance (i.e. FPI) by an efficient and improved adaptive neuro-fuzzy inference system (ANFIS) model. This was done using an evolutionary algorithm, i.e. artificial bee colony (ABC) algorithm mixed with the ANFIS model. The role of ABC algorithm in this system is to find the optimum membership functions (MFs) of ANFIS model to achieve a higher degree of accuracy. The procedure and modeling were conducted on a tunnelling database comprising of more than 150 data samples where brittleness index (BI), fracture spacing, α angle between the plane of weakness and the TBM driven direction, and field single cutter load were assigned as model inputs to approximate FPI values. According to the results obtained by performance indices, the proposed ANFIS_ABC model was able to receive the highest accuracy level in predicting FPI values compared with ANFIS model. In terms of coefficient of determination (R2), the values of 0.951 and 0.901 were obtained for training and testing stages of the proposed ANFIS_ABC model, respectively, which confirm its power and capability in solving TBM performance problem. The proposed model can be used in the other areas of rock mechanics and underground space technologies with similar conditions. © 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences
Cross-correlation stacking-based microseismic source location using three metaheuristic optimization algorithms
- 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
COSMA-RF : new intelligent model based on chaos optimized slime mould algorithm and random forest for estimating the peak cutting force of conical picks
- 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