List of Titles

Effect of strain rate on strength properties of low-calcium fly-ash-based geopolymer mortar under dry condition

- Khandelwal, Manoj, Ranjith, Pathegama, Pan, Zhu, Sanjayan, Jay

Authors: Khandelwal, Manoj , Ranjith, Pathegama , Pan, Zhu , Sanjayan, Jay
Date: 2013
Type: Text , Journal article
Relation: Arabian Journal of Geosciences Vol. 6, no. 7 (2013), p. 2383-2389
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Description: This paper presents the mechanical and elastic properties of inorganic polymer mortar under varying strain rates. The study includes a determination of the compressive strength, modulus of elasticity and Poisson's ratio at 0.001, 0.005, 0.01 and 0.05 mm/s strain rate. A total of 21 cylindrical specimens having 100 mm length and 50 mm diameter were investigated, and all tests were carried out pursuant to the relevant Australian Standards. Although some variability between the mixes was observed, the results show that, in most cases, the engineering properties of geopolymer mortar compare favourably to those predicted by the relevant Australian Standards for concrete mixtures. It was found that the change in the strain rate causes different behaviour related to the percentage of the ultimate load. The ultimate strength, Young's modulus and Poisson's ratio of the geopolymer mortar depend on the strain rate. It was also found that as the strain rate increases, mechanical and elastic properties of geopolymer mortar substantially increase in logarithmic manner. © 2012 Saudi Society for Geosciences.

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.

Implementing an ANN model optimized by genetic algorithm for estimating cohesion of limestone samples

- Khandelwal, Manoj, Marto, Aminaton, Fatemi, Seyed, Ghoroqi, Mahyar, Armaghani, Danial, Singh, Trilok, Tabrizi, Omid

Authors: Khandelwal, Manoj , Marto, Aminaton , Fatemi, Seyed , Ghoroqi, Mahyar , Armaghani, Danial , Singh, Trilok , Tabrizi, Omid
Date: 2018
Type: Text , Journal article
Relation: Engineering with Computers Vol. 34, no. 2 (2018), p. 307-317
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Description: Shear strength parameters such as cohesion are the most significant rock parameters which can be utilized for initial design of some geotechnical engineering applications. In this study, evaluation and prediction of rock material cohesion is presented using different approaches i.e., simple and multiple regression, artificial neural network (ANN) and genetic algorithm (GA)-ANN. For this purpose, a database including three model inputs i.e., p-wave velocity, uniaxial compressive strength and Brazilian tensile strength and one output which is cohesion of limestone samples was prepared. A meaningful relationship was found for all of the model inputs with suitable performance capacity for prediction of rock cohesion. Additionally, a high level of accuracy (coefficient of determination, R2 of 0.925) was observed developing multiple regression equation. To obtain higher performance capacity, a series of ANN and GA-ANN models were built. As a result, hybrid GA-ANN network provides higher performance for prediction of rock cohesion compared to ANN technique. GA-ANN model results (R2 = 0.976 and 0.967 for train and test) were better compared to ANN model results (R2 = 0.949 and 0.948 for train and test). Therefore, this technique is introduced as a new one in estimating cohesion of limestone samples. © 2017, Springer-Verlag London Ltd., part of Springer Nature.

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Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting

- Armaghani, Danial, Momeni, Ehsan, Abad, Seyed, Khandelwal, Manoj

Authors: Armaghani, Danial , Momeni, Ehsan , Abad, Seyed , Khandelwal, Manoj
Date: 2015
Type: Text , Journal article
Relation: Environmental Earth Sciences Vol. 74, no. 4 (2015), p. 2845-2860
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Description: One of the most significant environmental issues of blasting operations is ground vibration, which can cause damage to the surrounding residents and structures. Hence, it is a major concern to predict and subsequently control the ground vibration due to blasting. This paper presents two artificial intelligence techniques, namely, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network for the prediction of ground vibration in quarry blasting site. For this purpose, blasting parameters as well as ground vibrations of 109 blasting operations were measured in ISB granite quarry, Johor, Malaysia. Moreover, an empirical equation was also proposed based on the measured data. Several AI-based models were trained and tested using the measured data to determine the optimum models. Each model involved two inputs (maximum charge per delay and distance from the blast-face) and one output (ground vibration). To control capacity performances of the predictive models, the values of root mean squared error (RMSE), value account for (VAF), and coefficient of determination (R2) were computed for each model. It was found that the ANFIS model can provide better performance capacity in predicting ground vibration in comparison with other predictive techniques. The values of 0.973, 0.987 and 97.345 for R2, RMSE and VAF, respectively, reveal that the ANFIS model is capable to predict ground vibration with high degree of accuracy. © 2015, Springer-Verlag Berlin Heidelberg.

Correlating P-wave velocity with the physico-mechanical properties of different rocks

- Khandelwal, Manoj

Authors: Khandelwal, Manoj
Date: 2013
Type: Text , Journal article
Relation: Pure and Applied Geophysics Vol. 170, no. 4 (2013), p. 507-514
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Description: In mining and civil engineering projects, physico-mechanical properties of the rock affect both the project design and the construction operation. Determination of various physico-mechanical properties of rocks is expensive and time consuming, and sometimes it is very difficult to get cores to perform direct tests to evaluate the rock mass. The purpose of this work is to investigate the relationships between the different physico-mechanical properties of the various rock types with the P-wave velocity. Measurement of P-wave velocity is relatively cheap, non-destructive and easy to carry out. In this study, representative rock mass samples of igneous, sedimentary, and metamorphic rocks were collected from the different locations of India to obtain an empirical relation between P-wave velocity and uniaxial compressive strength, tensile strength, punch shear, density, slake durability index, Young's modulus, Poisson's ratio, impact strength index and Schmidt hammer rebound number. A very strong correlation was found between the P-wave velocity and different physico-mechanical properties of various rock types with very high coefficients of determination. To check the sensitivity of the empirical equations, Students t test was also performed, which confirmed the validity of the proposed correlations. © 2012 Springer Basel AG.

Assessment of maximum explosive charge used per delay in surface mines

- Khandelwal, Manoj, Mastorakis, Nikos

Authors: Khandelwal, Manoj , Mastorakis, Nikos
Date: 2014
Type: Text , Conference paper
Relation: Advances in neural networks, fuzzy systems and artifical intelligence : Proceedings of the 13th International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED '14) p. 100-105
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Description: The present paper mainly with deals the prediction of safe charge of explosive used per delay (Qmax) using artificial neural network (ANN) incorporating peak particle velocity (PPV) and distance between blast face to monitoring point (D). 120 blast vibration data sets were monitored at different vulnerable and strategic locations in and around a major coal producing opencast coal mines in India. 100 blast vibrations records were used for the training of the ANN model vis-à-vis to determine site constants of various conventional vibration predictors. Rest 20 new randomly selected data sets were used to compare the ANN prediction results with widely used conventional predictors. Results were compared based on coefficient of determination (R2) between measured and predicted values of Qmax. It was found that coefficient of determination between measured and predicted Qmax by ANN was 0.894, whereas it was ranging from 0.023 to 0.417 by different conventional predictor equations.

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.

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 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.

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 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.

Behaviour of brittle material in multiple loading rates under uniaxial compression

- Khandelwal, Manoj, Ranjith, Pathegama

Authors: Khandelwal, Manoj , Ranjith, Pathegama
Date: 2013
Type: Text , Journal article
Relation: Geotechnical and Geological Engineering Vol. 31, no. 4 (2013), p. 1305-1315
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Description: It is of great importance to investigate the effect of loading rate on the behaviour of brittle material such as concrete and rock because engineering structures are subjected to multiple loading conditions. Although material behaviour under single loading mode has been extensively studied, very limited research has been conducted to investigate the performance of brittle materials subjected to varying loading conditions. This paper presents an experimental study of the effects of single and multiple strain rates (ε) on cement mortar samples. The first set of samples was loaded at constant strain rates until failure. For the remaining samples, the first strain rate (0.005 mm/s) was applied to the sample up to a predetermined load, and then the second strain was initiated immediately by using the specially-designed gear system in place in the compression rig. As expected, the increase in strain rate showed an increase in peak strength of the sample with reduced ultimate strain. For multiple strain modes, it was observed that the highest peak strength occurred when the second strain was applied at 50 % of the peak strength of the first strain. © 2013 Springer Science+Business Media Dordrecht.

Blast-induced ground vibration prediction using support vector machine

- Khandelwal, Manoj

Authors: Khandelwal, Manoj
Date: 2011
Type: Text , Journal article
Relation: Engineering with Computers Vol. 27, no. 3 (2011), p. 193-200
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Description: Ground vibrations induced by blasting are one of the fundamental problems in the mining industry and may cause severe damage to structures and plants nearby. Therefore, a vibration control study plays an important role in the minimization of environmental effects of blasting in mines. In this paper, an attempt has been made to predict the peak particle velocity using support vector machine (SVM) by taking into consideration of maximum charge per delay and distance between blast face to monitoring point. To investigate the suitability of this approach, the predictions by SVM have been compared with conventional vibration predictor equations. Coefficient of determination (CoD) and mean absolute error were taken as a performance measure. © 2010 Springer-Verlag London Limited.

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.

Evaluation of safe explosive charge in surface mines using artificial neural network

- Khandelwal, Manoj

Authors: Khandelwal, Manoj
Date: 2014
Type: Text , Conference paper
Relation: 6th International Conference on Agents and Artificial Intelligence, ICAART 2014 p. 366-371
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Description: The present paper mainly deals with the prediction of maximum explosive charge used per delay (QMAX) using artificial neural network (ANN) incorporating peak particle velocity (PPV) and distance between blast face to monitoring point (D). 150 blast vibration data sets were monitored at different vulnerable and strategic locations in and around major coal producing opencast coal mines in India. 124 blast vibrations records were used for the training of the ANN model vis-à-vis to determine site constants of various conventional vibration predictors. Rest 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) and mean absolute error (MAE) between calculated and predicted values of QMAX.

Artificial neural network as a tool for backbreak prediction

- Monjezi, Masoud, Hashemi Rizi, S, Majd, Vahdi, Khandelwal, Manoj

Authors: Monjezi, Masoud , Hashemi Rizi, S , Majd, Vahdi , Khandelwal, Manoj
Date: 2014
Type: Text , Journal article
Relation: Geotechnical and Geological Engineering Vol. 32, no. 1 (2014), p. 21-30
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Description: Backbreak is one of the destructive side effects of the blasting operation. Reducing of this event is very important for economic of a mining project. Involvement of various parameters has made the backbreak analyzing difficult. Currently there is no any specific method to predict or control the phenomenon considering all the effective parameters. In this paper, artificial neural network (ANN) as a powerful tool for solving such complicated problems is used to predict backbreak in blasting operation of the Sangan iron mine, Iran. Network training was fulfilled using a collected database of the practiced operation including blast design details and rock condition. Trying various types of the networks, a network with two hidden layers was found to be optimum. Performance of the ANN model was compared with statistical analysis using datasets which were kept apart from the original database. According to the obtained results, for the ANN model there existed a higher correlation (R2 = 0.868) and lesser error (RMSE = 0.495) between the predicted and measured backbreak as compared to the regression model. Also, sensitivity analysis revealed that the inputs rock factor and number of rows are the most and the least sensitive parameters on the output backbreak, respectively. © 2013 Springer Science+Business Media Dordrecht.

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.

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.

Artificial neural network for prediction of air flow in a single rock joint

- Ranjith, Pathegama, Khandelwal, Manoj

Authors: Ranjith, Pathegama , Khandelwal, Manoj
Date: 2012
Type: Text , Journal article
Relation: Neural Computing and Applications Vol. 21, no. 6 (2012), p. 1413-1422
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Description: In this paper, an attempt has been made to evaluate and predict the air flow rate in triaxial conditions at various confining pressures incorporating cell pressure, air inlet pressure, and air outlet pressure using artificial neural network (ANN) technique. A three-layer feed forward back propagation neural network having 3-7-1 architecture network was trained using 37 data sets measured from laboratory investigation. Ten new data sets were used for the, validation and comparison of the air flow rate by ANN and multi-variate regression analysis (MVRA) to develop more confidence on the proposed method. Results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between measured and predicted values of air flow rate. It was found that CoD between measured and predicted air flow rate was 0. 995 and 0. 758 by ANN and MVRA, respectively, whereas MAE was 0. 0413 and 0. 1876. © 2011 Springer-Verlag London Limited.

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