List of Titles

A case study of grinding coarse 5 mm particles into sand grade particles less than 2.36 mm

- Reed, Aaron, Koroznikova, Larissa, Khandelwal, Manoj

Authors: Reed, Aaron , Koroznikova, Larissa , Khandelwal, Manoj
Date: 2021
Type: Text , Journal article
Relation: Vietnam Journal of Earth Sciences Vol. 43, no. 1 (2021), p. 57-70
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Description: This paper presents the viability study of utilising a rod or ball mill to grind a ‘5 mm grit’ to 100% passing 2.36 mm and fit in with a desired particle size analysis. The aim is to introduce this grit into the concrete grade sand produced at the Hanson owned Axedale Sand & Gravel quarry to reduce generated waste and improve the process efficiency. A ball mill and rod mill were used to grind the samples at an interval of 5 and 10 minutes. From the laboratory experimental analysis, it was found that a ball mill with 5 minutes grinding time in closed-circuit using a classifier to remove undersize and reintroduce oversize to the mill would be a viable option in an industrial setting. A Bond Ball Mill Grindability Test was undertaken to determine the grindability of the 5 mm grit, which served to determine the power (kWh/t) required to grind it to 100% passing 2.36 mm. The bond ball mill grindability test showed that the grit had a work index value of 17.66 kWh/t. This work index gives an actual work input of 13.54 kWh/t, meaning that for every ton of feed material introduced to the mill, 13.54 kWh of work input is required to grind it to 150 microns. © 2021 Vietnam Academy of Science and Technology.

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.

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A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak

- Sayadi, Ahmad, Monjezi, Masoud, Talebi, Nemat, Khandelwal, Manoj

Authors: Sayadi, Ahmad , Monjezi, Masoud , Talebi, Nemat , Khandelwal, Manoj
Date: 2013
Type: Text , Journal article
Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 5, no. 4 (2013), p. 318-324
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Description: In blasting operation, the aim is to achieve proper fragmentation and to avoid undesirable events such as backbreak. Therefore, predicting rock fragmentation and backbreak is very important to arrive at a technically and economically successful outcome. Since many parameters affect the blasting results in a complicated mechanism, employment of robust methods such as artificial neural network may be very useful. In this regard, this paper attends to simultaneous prediction of rock fragmentation and backbreak in the blasting operation of Tehran Cement Company limestone mines in Iran. Back propagation neural network (BPNN) and radial basis function neural network (RBFNN) are adopted for the simulation. Also, regression analysis is performed between independent and dependent variables. For the BPNN modeling, a network with architecture 6-10-2 is found to be optimum whereas for the RBFNN, architecture 6-36-2 with spread factor of 0.79 provides maximum prediction aptitude. Performance comparison of the developed models is fulfilled using value account for (VAF), root mean square error (RMSE), determination coefficient (R2) and maximum relative error (MRE). As such, it is observed that the BPNN model is the most preferable model providing maximum accuracy and minimum error. Also, sensitivity analysis shows that inputs burden and stemming are the most effective parameters on the outputs fragmentation and backbreak, respectively. On the other hand, for both of the outputs, specific charge is the least effective parameter. © 2013 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences.

A correlation between Schmidt hammer rebound numbers with impact strength index, slake durability index and P-wave velocity

- Sharma, Pramod, Khandelwal, Manoj, Singh, Trilok

Authors: Sharma, Pramod , Khandelwal, Manoj , Singh, Trilok
Date: 2011
Type: Text , Journal article
Relation: International Journal of Earth Sciences Vol. 100, no. 1 (2011), p. 189-195
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Description: The main objective of this study was to establish statistical relationship between Schmidt hammer rebound numbers with impact strength index (ISI), slake durability index (SDI) and P-wave velocity. These are important properties to characterize a rock mass and are being widely used in geological and geotechnical engineering. Due to its importance, Schmidt hammer rebound number is considered as one of the most important property for the determination of other properties, like ISI, SDI and P-wave velocity. Determination of these properties in the laboratory is time consuming and tedious as well as requiring expertise, whereas Schmidt hammer rebound number can be easily obtained on site which in addition is non-destructive. So, in this study, an attempt has been made to determine these index properties in the laboratory and each index property was correlated with Schmidt hammer rebound values. Empirical equations have been developed to predict ISI, SDI and P-wave velocity using rebound values. It was found that Schmidt hammer rebound number shows linear relation with ISI and SDI, whereas exponential relation with P-wave velocity. To check the sensitivity of empirical relations, Student's t test was done to verify the correlation between rebound values and other rock index properties. © 2010 Springer-Verlag.

A dimensional analysis approach to study blast-induced ground vibration

- Khandelwal, Manoj, Saadat, Mahdi

Authors: Khandelwal, Manoj , Saadat, Mahdi
Date: 2014
Type: Text , Journal article
Relation: Rock Mechanics and Rock Engineering Vol. 48, no. 2 (2014), p. 727-735
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Description: The prediction of ground vibration is of great importance in the alleviation of the detrimental effects of blasting. Therefore, a vibration control study to minimize the harm of ground vibration and its influence on nearby structures can play an important role in the mining industry. In this paper, a dimensional analysis (DA) technique has been performed on various blast design parameters to propose a new formula for the prediction of the peak particle velocity (PPV). After obtaining the DA formula, 105 data sets were used to determine the unknown coefficients of the DA equation, as well as site constants of different conventional predictor equations. Then, 12 new blast data sets were used to compare the capability of the DA formula with conventional predictor equations. The results were compared based on the coefficient of determination and mean absolute error between measured and predicted values of the PPV. © 2014, Springer-Verlag Wien.

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A hybrid metaheuristic approach using random forest and particle swarm optimization to study and evaluate backbreak in open-pit blasting

- Dai, Yong, Khandelwal, Manoj, Qiu, Yingui, Zhou, Jian, Monjezi, Monjezi, Yang, Peixi

Authors: Dai, Yong , Khandelwal, Manoj , Qiu, Yingui , Zhou, Jian , Monjezi, Monjezi , Yang, Peixi
Date: 2022
Type: Text , Journal article
Relation: Neural Computing and Applications Vol. 34, no. 8 (2022), p. 6273-6288
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Description: Backbreak is a rock fracture problem that exceeds the limits of the last row of holes in an explosion operation. Excessive backbreak increases operational costs and also poses a threat to mine safety. In this regard, a new hybrid intelligence approach based on random forest (RF) and particle swarm optimization (PSO) is proposed for predicting backbreak with high accuracy to reduce the unsolicited phenomenon induced by backbreak in open-pit blasting. A data set of 234 samples with six input parameters including special drilling (SD), spacing (S), burden (B), hole length (L), stemming (T) and powder factor (PF) and one output parameter backbreak (BB) is set up in this study. Seven input combinations (one with six parameters, six with five parameters) are built to generate the optimal prediction model. The PSO algorithm is integrated with the RF algorithm to find the optimal hyper-parameters of each model and the fitness function, which is the mean absolute error (MAE) of ten cross-validations. The performance capacities of the optimal models are assessed using MAE, root-mean-square error (RMSE), Pearson correlation coefficient (R2) and mean absolute percentage error (MAPE). Findings demonstrated that the PSO–RF model combining L–S–B–T–PF with MAE of 0.0132 and 0.0568, RMSE of 0.0811 and 0.1686, R2 of 0.9990 and 0.9961 and MAPE of 0.0027 and 0.0116 in training and testing phases, respectively, has optimal prediction performance. The optimal PSO–RF models were compared with the classical artificial neural network, RF, genetic programming, support vector machine and convolutional neural network models and show that the PSO–RF model has superiority in predicting backbreak. The Gini index of each input variable has also been calculated in the RF model, which was 31.2 (L), 23.1 (S), 27.4 (B), 36.6 (T), 23.4 (PF) and 16.9 (SD), respectively. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

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A method to improve transparency of electronic election process without identification

- Alamuti, Roghayeh, Barjini, Hassan, Khandelwal, Manoj, Jafarabad, Mohammad

Authors: Alamuti, Roghayeh , Barjini, Hassan , Khandelwal, Manoj , Jafarabad, Mohammad
Date: 2015
Type: Text , Conference proceedings
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Description: Transparency of bank accounts, nowadays, is an undeniable necessity, but no one denies that definite transparency throughout election process is not realized thus far in the world. This calls for fundamental changes in traditional electronic election methods. The new method must close the way for any complaints by the candidate as to the voting process as the public completely trusts in the voting mechanism. Synchronizing voting and votes counting improves the public's trust in the results of election. The proposed secure room-corridor of electronic voting employs election watchers and reports real time results of election along with observance of confidentiality of the votes. © 2015 The Authors.

A new model based on gene expression programming to estimate air flow in a single rock joint

- Khandelwal, Manoj, Armaghani, Danial, Faradonbeh, Roohollah, Ranjith, Pathegama, Ghoraba, Saber

Authors: Khandelwal, Manoj , Armaghani, Danial , Faradonbeh, Roohollah , Ranjith, Pathegama , Ghoraba, Saber
Date: 2016
Type: Text , Journal article
Relation: Environmental Earth Sciences Vol. 75, no. 9 (2016), p.
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Description: This paper is aimed to introduce and validate a gene expression programming (GEP) model to estimate the rate of air flow in triaxial conditions at various confining pressures incorporating cell pressure, air inlet pressure and air outlet pressure. To achieve the aim of this study, a series of laboratory experiments were designed and carried out and then a database comprising 47 datasets was prepared to develop new predictive models. A gene expression programming (GEP) model for prediction of air flow was proposed using the prepared datasets. In this regard, a series of sensitivity analyses were performed to choose the best GEP model. For comparison purposes, multiple regression (MR) analysis was also employed for air flow estimation. Several performance indices, i.e., coefficient of determination (CoD), mean absolute error (MAE), root mean square error (RMSE) and variance account for (VAF) were considered and calculated to evaluate the performance prediction of the developed models. Considering both training and testing datasets, the developed GEP model can provide higher performance prediction of rate of air flow in comparison to the MR model. © 2016, Springer-Verlag Berlin Heidelberg.

A study on environmental issues of blasting using advanced support vector machine algorithms

- Chen, Lihua, Armaghani, Danial, Fakharian, Pouyan, Bhatawdekar, Ramesh, Samui, P., Khandelwal, Manoj, Khedher, Khaled

Authors: Chen, Lihua , Armaghani, Danial , Fakharian, Pouyan , Bhatawdekar, Ramesh , Samui, P. , Khandelwal, Manoj , Khedher, Khaled
Date: 2022
Type: Text , Journal article
Relation: International Journal of Environmental Science and Technology Vol. 19, no. 7 (2022), p. 6221-6240
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Description: Air overpressure is a critical negative effect of blasting in construction or production sites and projects. So far, many attempts have been made to prevent or reduce this negative effect on the nearby construction, equipment, or people. While various experiential equations have been proposed to forecast the air overpressure value for determining the blasting area, these models are typically inaccurate and impractical. Due to the recent efforts to predict the air overpressure by employing artificial intelligence techniques, this study developed five support vector machine-based models optimized by some praised optimization techniques, including the moth flame optimization, particle swarm optimization, grey wolf optimization, cuckoo optimization algorithm, and whale optimization algorithm. These algorithms optimize the most important parameters of the support vector machine, including “C” and “gamma”, and improve the performance of this model for air overpressure prediction. The findings showed that the moth flame optimization algorithm is the best optimizer for support vector machine and is suitable for air overpressure prediction. The support vector machine–moth flame optimization model achieved the best R2 (train: 0.9939; test: 0.9941) and comprehensive score (34). On the other hand, the worst model was the support vector machine–particle swarm optimization, which achieved the lowest comprehensive score (13). In addition, all optimization techniques improved the performance of the single support vector machine model. The findings of this study imply that all optimization techniques successfully enhanced the performance of the support vector machine model; however, the moth flame optimization optimizer was the most effective one. The support vector machine–moth flame optimization technique can be employed to solve other mining-related issues. © 2022, Islamic Azad University (IAU). Correction to: A study on environmental issues of blasting using advanced support vector machine algorithms (International Journal of Environmental Science and Technology, (2022), 19, 7, (6221-6240), 10.1007/s13762-022-03999-y): The original version of this article unfortunately contains two mistakes. The spelling of the third author's name was incorrect. The correct name is Pouyan Fakharian (P. Fakharian). Another error was in the acknowledgment section. The correct Grant No. is KJQN202103415. © The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University 2022

An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine, Iran

- Saadat, Mahdi, Khandelwal, Manoj, Monjezi, Masoud

Authors: Saadat, Mahdi , Khandelwal, Manoj , Monjezi, Masoud
Date: 2014
Type: Text , Journal article
Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 6, no. 1 (2014), p. 67-76
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Description: Blast-induced ground vibration is one of the inevitable outcomes of blasting in mining projects and may cause substantial damage to rock mass as well as nearby structures and human beings. In this paper, an attempt has been made to present an application of artificial neural network (ANN) to predict the blast-induced ground vibration of the Gol-E-Gohar (GEG) iron mine, Iran. A four-layer feed-forward back propagation multi-layer perceptron (MLP) was used and trained with Levenberg-Marquardt algorithm. To construct ANN models, the maximum charge per delay, distance from blasting face to monitoring point, stemming and hole depth were taken as inputs, whereas peak particle velocity (PPV) was considered as an output parameter. A database consisting of 69 data sets recorded at strategic and vulnerable locations of GEG iron mine was used to train and test the generalization capability of ANN models. Coefficient of determination (R2) and mean square error (MSE) were chosen as the indicators of the performance of the networks. A network with architecture 4-11-5-1 and R2 of 0.957 and MSE of 0.000722 was found to be optimum. To demonstrate the supremacy of ANN approach, the same 69 data sets were used for the prediction of PPV with four common empirical models as well as multiple linear regression (MLR) analysis. The results revealed that the proposed ANN approach performs better than empirical and MLR models. © 2013 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences.

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An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass

- Parsajoo, Maryama, Mohammed, Ahmed, Yagiz, Saffet, Armaghani, Danial, Khandelwal, Manoj

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

An expert system based on hybrid ICA-ANN technique to estimate macerals contents of Indian coals

- Khandelwal, Manoj, Mahdiyar, Amir, Armaghani, Danial, Singh, Trilok, Fahimifar, Ahmad, Faradonbeh, Roohollah

Authors: Khandelwal, Manoj , Mahdiyar, Amir , Armaghani, Danial , Singh, Trilok , Fahimifar, Ahmad , Faradonbeh, Roohollah
Date: 2017
Type: Text , Journal article
Relation: Environmental Earth Sciences Vol. 76, no. 11 (2017), p. 1-14
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Description: Coal, as an initial source of energy, requires a detailed investigation in terms of ultimate analysis, proximate analysis, and its biological constituents (macerals). The rank and calorific value of each type of coal are managed by the mentioned properties. In contrast to ultimate and proximate analyses, determining the macerals in coal requires sophisticated microscopic instrumentation and expertise. This study emphasizes the estimation of the concentration of macerals of Indian coals based on a hybrid imperialism competitive algorithm (ICA)–artificial neural network (ANN). Here, ICA is utilized to adjust the weight and bias of ANNs for enhancing their performance capacity. For comparison purposes, a pre-developed ANN model is also proposed. Checking the performance prediction of the developed models is performed through several performance indices, i.e., coefficient of determination (R2), root mean square error and variance account for. The obtained results revealed higher accuracy of the proposed hybrid ICA-ANN model in estimating macerals contents of Indian coals compared to the pre-developed ANN technique. Results of the developed ANN model based on R2 values of training datasets were obtained as 0.961, 0.955, and 0.961 for predicting vitrinite, liptinite, and inertinite, respectively, whereas these values were achieved as 0.948, 0.947, and 0.957, respectively, for testing datasets. Similarly, R2 values of 0.988, 0.983, and 0.991 for training datasets and 0.989, 0.982, and 0.985 for testing datasets were obtained from developed ICA-ANN model. © 2017, Springer-Verlag Berlin Heidelberg.

An intelligent approach to evaluate drilling performance

- Bhatnagar, Anupam, Khandelwal, Manoj

Authors: Bhatnagar, Anupam , Khandelwal, Manoj
Date: 2012
Type: Text , Journal article
Relation: Neural Computing and Applications Vol. 21, no. 4 (2012), p. 763-770
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Description: In this paper, an attempt has been made to predict the rate of penetration (ROP) of rocks by incorporating thrust, revolutions per minute (rpm), flushing media and compressive strength of rocks using artificial neural network (ANN) technique. A three-layer feed-forward back-propagation neural network with 4-7-1 architecture was trained using 472 experimental data sets of sandstone, limestone, rock phosphate, dolomite, marble and quartz-chlorite-schist rocks. A total of 146 new data sets were used for the testing and comparison of the ROP by ANN. Multivariate regression analysis (MVRA) has also been done with same data sets of ANN. ANN and MVRA results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between experimental and predicted values of ROP. The coefficient of determination by ANN was 0. 985, while coefficient of determination was 0. 629 for rate of penetration. The mean absolute error (MAE) for rate of penetration by ANN was 0. 3547, whereas MAE by MVRA was 1. 7499. © 2010 Springer-Verlag London Limited.

Application of an expert system for the assessment of blast vibration

- Khandelwal, Manoj

Authors: Khandelwal, Manoj
Date: 2012
Type: Text , Journal article
Relation: Geotechnical and Geological Engineering Vol. 30, no. 1 (2012), p. 205-217
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Description: The purpose of this article is to evaluate and predict the blast induced ground vibration using different conventional vibration predictors and artificial neural network (ANN) at a surface coal mine of India. Ground Vibration is a seismic wave that spread out from the blast hole when detonated in a confined manner. 128 blast vibrations were recorded and monitored in and around the surface coal mine at different strategic and vulnerable locations. Among these, 103 blast vibrations data sets were used for the training of the ANN network as well as to determine site constants of various conventional vibration predictors, whereas rest 25 blast vibration data sets were used for the validation and comparison by ANN and empirical formulas. Two types of ANN model based on two parameters (maximum charge per delay and distance between blast face to monitoring point) and multiple parameters (burden, spacing, charge length, maximum charge per delay and distance between blast face to monitoring point) were used in the present study to predict the peak particle velocity. Finally, it is found that the ANN model based on multiple input parameters have better prediction capability over two input parameters ANN model and conventional vibration predictors. © 2011 Springer Science+Business Media B.V.

Application of an expert system to predict maximum explosive charge used per delay in surface mining

- Khandelwal, Manoj, Singh, Trilok

Authors: Khandelwal, Manoj , Singh, Trilok
Date: 2013
Type: Text , Journal article
Relation: Rock Mechanics and Rock Engineering Vol. 46, no. 6 (2013), p. 1551-1558
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Description: The present paper mainly deals with the prediction of maximum explosive charge used per delay (Q MAX) using an artificial neural network (ANN) incorporating peak particle velocity (PPV) and distance between blast face to monitoring point (D). One hundred and fifty blast vibration data sets were monitored at different vulnerable and strategic locations in and around major coal producing opencast coal mines in India. One hundred and twenty-four blast vibrations records were used for the training of the ANN model vis-à-vis to determine site constants of various conventional vibration predictors. The other 26 new randomly selected data sets were used to test, evaluate and compare the ANN prediction results with widely used conventional predictors. Results were compared based on coefficient of correlation (R), mean absolute error and mean squared between measured and predicted values of Q MAX. It was found that coefficient of correlation between measured and predicted Q MAX by ANN was 0.985, whereas it ranged from 0.316 to 0.762 by different conventional predictor equations. Mean absolute error and mean squared error was also very small by ANN, whereas it was very high for different conventional predictor equations. © 2013 Springer-Verlag Wien.

Application of an expert system to predict thermal conductivity of rocks

- Khandelwal, Manoj

Authors: Khandelwal, Manoj
Date: 2012
Type: Text , Journal article
Relation: Neural Computing and Applications Vol. 21, no. 6 (2012), p. 1341-1347
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Description: In this paper, an attempt has been made to predict the thermal conductivity (TC) of rocks by incorporating uniaxial compressive strength, density, porosity, and P-wave velocity using support vector machine (SVM). Training of the SVM network was carried out using 102 experimental data sets of various rocks, whereas 25 new data sets were used for the testing of the TC by SVM model. Multivariate regression analysis (MVRA) has also been carried out with same data sets that were used for the training of SVM model. SVM and MVRA results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between experimental and predicted values of TC. It was found that CoD between measured and predicted values of TC by SVM and MVRA was 0. 994 and 0. 918, respectively, whereas MAE was 0. 0453 and 0. 2085 for SVM and MVRA, respectively. © 2011 Springer-Verlag London Limited.

Application of geogrids in waste dump stability : A numerical modeling approach

- Rai, Rajesh, Khandelwal, Manoj, Jaiswal, Ashok

Authors: Rai, Rajesh , Khandelwal, Manoj , Jaiswal, Ashok
Date: 2012
Type: Text , Journal article
Relation: Environmental Earth Sciences Vol. 66, no. 5 (2012), p. 1459-1465
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Description: Geosynthetic is widely used to reinforce the weak rock mass, mine waste dump, soil slopes road cut slopes, etc. The present paper discusses the effect of geogrids on the stability of mine waste dump. The stability of mine waste dump has been done by Fast Langrage Analysis of Continua (FLAC) slope software, which is based on finite difference method. Reinforcement by geogrids mainly depends on the tensile strength, aperture size of geogrids, and particle size distribution of dump rock mass. Different permutations and combinations of spacing between two geogrid sheets have been taken into consideration to study the stability of mine waste dump. The factor of safety is calculated to quantify the effect of geogrids on waste dump slope. It has been observed from numerical modeling that the maximum slope angle is 45° at a height of 10 m. The scope of increasing slope angle from 45 to 60° is evaluated using geogrids. It has been found from the study that the factor of safety increases as the spacing between geogrids decreases. Maximum strain is also plotted of each case to identify the slip circle. The positions of geogrids modify the probable slip circle or failure plane of mine waste dump. Using ten geogrids at a spacing of 1 m, the slope angle can be increased up to 60° with factor of safety of 1.4. © 2011 Springer-Verlag.

Application of slope mass rating and kinematic analysis along road cut slopes in the Himalayan terrain

- Siddique, Tariq, Sazid, Mohammed, Khandelwal, Manoj, Varshney, Harsh, Irshad, Sayem

Authors: Siddique, Tariq , Sazid, Mohammed , Khandelwal, Manoj , Varshney, Harsh , Irshad, Sayem
Date: 2022
Type: Text , Conference paper
Relation: International Conference on Geotechnical challenges in Mining, Tunneling and Underground structures, ICGMTU 2021, Virtual, Online, 20-21 December 2021, Lecture Notes in Civil Engineering Vol. 228, p. 697-708
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Description: Hundreds of fatalities are being reported every year due to rampant slope failures along road-cut engineered slopes in the Himalayan region. Prevailing perilous conditions of cut slopes are mainly due to adverse geological attributes and ever-rising anthropogenic factors. To procure a landslide resilient design along roads in mountainous regions, a systematic geotechnical investigation is required. In this regard, the characterization of vulnerable slopes through empirical classification systems is of paramount importance for geotechnical appraisal. Eleven vulnerable road cut slopes along national highway-5 (NH-5), from Solan to Shimla have been studied herein. The present study incorporates the application of Slope Mass Rating (SMR) and its extensions, including Continuous Slope Mass Rating (CSMR) and Chinese Slope Mass Rating (ChSMR) to study various slopes. The outcomes of SMR and its extensions are used to classify cut slopes into different stability grades. In addition, prevailing structurally controlled failures have also been assessed by kinematic analysis. The majority of slopes are liable to undergo planar and wedge failures. The outcomes obtained by kinematic analysis, SMR and its extensions are used to propose adequate remedial measures. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Application of soft computing in predicting rock fragmentation to reduce environmental blasting side effects

- Monjezi, Masoud, Mohamadi, Hasan, Barati, Bahare, Khandelwal, Manoj

Authors: Monjezi, Masoud , Mohamadi, Hasan , Barati, Bahare , Khandelwal, Manoj
Date: 2014
Type: Text , Journal article
Relation: Arabian Journal of Geosciences Vol. 7, no. 2 (2014), p. 505-511
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Description: In the blasting operation, risk of facing with undesirable environmental phenomena such as ground vibration, air blast, and flyrock is very high. Blasting pattern should properly be designed to achieve better fragmentation to guarantee the successfulness of the process. A good fragmentation means that the explosive energy has been applied in a right direction. However, many studies indicate that only 20-30 % of the available energy is actually utilized for rock fragmentation. Involvement of various effective parameters has made the problem complicated, advocating application of new approaches such as artificial intelligence-based techniques. In this paper, artificial neural network (ANN) method is used to predict rock fragmentation in the blasting operation of the Sungun copper mine, Iran. The predictive model is developed using eight and three input and output parameters, respectively. Trying various types of the networks, it was found that a trained model with back-propagation algorithm having architecture 8-15-8-3 is the optimum network. Also, performance comparison of the ANN modeling with that of the statistical method was confirmed robustness of the neural networks to predict rock fragmentation in the blasting operation. Finally, sensitivity analysis showed that the most influential parameters on fragmentation are powder factor, burden, and bench height. © 2012 Saudi Society for Geosciences.

Application of soft computing to predict blast-induced ground vibration

- Khandelwal, Manoj, Kumar, Lalit, Yellishetty, Mohan

Authors: Khandelwal, Manoj , Kumar, Lalit , Yellishetty, Mohan
Date: 2011
Type: Text , Journal article
Relation: Engineering with Computers Vol. 27, no. 2 (2011), p. 117-125
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Description: In this study, an attempt has been made to evaluate and predict the blast-induced ground vibration by incorporating explosive charge per delay and distance from the blast face to the monitoring point using artificial neural network (ANN) technique. A three-layer feed-forward back-propagation neural network with 2-5-1 architecture was trained and tested using 130 experimental and monitored blast records from the surface coal mines of Singareni Collieries Company Limited, Kothagudem, Andhra Pradesh, India. Twenty new blast data sets were used for the validation and comparison of the peak particle velocity (PPV) by ANN and conventional vibration predictors. Results were compared based on coefficient of determination and mean absolute error between monitored and predicted values of PPV. © 2009 Springer-Verlag London Limited.

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