Showing items 1 - 20 of 39

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.

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.

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.

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 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.

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.

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.

Assessing cohesion of the rocks proposing a new intelligent technique namely group method of data handling

- Chen, Wusi, Khandelwal, Manoj, Murlidhar, Bhatawdekar, Bui, Dieu, Tahir, Mahmood, Katebi, Javad

  • Authors: Chen, Wusi , Khandelwal, Manoj , Murlidhar, Bhatawdekar , Bui, Dieu , Tahir, Mahmood , Katebi, Javad
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Engineering with Computers Vol. 36, no. 2 (2020), p. 783-793
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  • Description: In this study, evaluation and prediction of rock cohesion is assessed using multiple regression as well as group method of data handling (GMDH). It is a well-known fact that cohesion is the most crucial rock shear strength parameter, which is a key parameter for the stability evaluation of some geotechnical structures such as rock slope. To fulfill the aim of this study, a database of three model input parameters, i.e., p wave velocity, uniaxial compressive strength and Brazilian tensile strength and one model output, which is cohesion of limestone samples was prepared and utilized by GMDH. Different GMDH models with neurons and layers and selection pressure were tested and assessed. It was found that GMDH model number 4 (with 8 layers) shows the best performance among all of tested models between the input and output parameters for the prediction and assessment of rock cohesion with coefficient of determination (R2) values of 0.928 and 0.929, root mean square error values of 0.3545 and 0.3154 for training and testing datasets, respectively. Multiple regression analysis was also performed on the same database and R2 values were obtained as 0.8173 and 0.8313 between input and output parameters for the training and testing of the models, respectively. The GMDH technique developed in this study is introduced as a new model in field of rock shear strength parameters. © 2019, Springer-Verlag London Ltd., part of Springer Nature.

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.

Backbreak prediction in the Chadormalu iron mine using artificial neural network

- Monjezi, Masoud, Ahmadi, Zabiholla, Yazdian-Varjani, Ali, Khandelwal, Manoj

  • Authors: Monjezi, Masoud , Ahmadi, Zabiholla , Yazdian-Varjani, Ali , Khandelwal, Manoj
  • Date: 2013
  • Type: Text , Journal article
  • Relation: Neural Computing and Applications Vol. 23, no. 3-4 (2013), p. 1101-1107
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  • Description: Backbreak is one of the unfavorable blasting results, which can be defined as the unwanted rock breakage behind the last row of blast holes. Blast pattern parameters, like stemming, burden, delay timing, stiffness ratio (bench height/burden) and rock mass conditions (e.g., geo-mechanical properties and joints), are effective in backbreak intensity. Till date, with the exception of some qualitative guidelines, no specific method has been developed for predicting the phenomenon. In this paper, an effort has been made to apply artificial neural networks (ANNs) for predicting backbreak in the blasting operation of the Chadormalu iron mine (Iran). Number of ANN models with different hidden layers and neurons were tried, and it was found that a network with architecture 10-7-7-1 is the optimum model. A comparative study also approved the superiority of the ANN modeling over the conventional regression analysis. Mean square error (MSE), variance account for (VAF) and coefficient of determination (R 2) between measured and predicted backbreak for the ANN model were calculated and found 89.46 %, 0.714 and 90.02 %, respectively. Also, for the regression model, MSE, VAF and R 2 were computed and found 66.93 %, 1.46 and 68.10 %, respectively. Sensitivity analysis was also carried out to find out the influence of each input parameter on backbreak results, and it was revealed that burden is the most influencing parameter on the backbreak, whereas water content is the least effective parameter in this regard. © 2012 Springer-Verlag London Limited.

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.