Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique
- Authors: Khandelwal, Manoj , Armaghani, Danial
- Date: 2016
- Type: Text , Journal article
- Relation: Geotechnical and Geological Engineering Vol. 34, no. 2 (2016), p. 605-620
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- Description: The purpose of this paper is to provide a proper, practical and convenient drilling rate index (DRI) prediction model based on rock material properties. In order to obtain this purpose, 47 DRI tests were used. In addition, the relevant strength properties i.e. uniaxial compressive strength and Brazilian tensile strength were also used and selected as input parameters to predict DRI. Examined simple regression analysis showed that the relationships between the DRI and predictors are statistically meaningful but not good enough for DRI estimation in practice. Moreover, multiple regression, artificial neural network (ANN) and hybrid genetic algorithm (GA)-ANN models were constructed to estimate DRI. Several performance indices i.e. coefficient of determination (R2), root mean square error and variance account for were used for evaluation of performance prediction the proposed methods. Based on these results and the use of simple ranking procedure, the best models were chosen. It was found that the hybrid GA-ANN technique can performed better in predicting DRI compared to other developed models. This is because of the fact that the proposed hybrid model can update the biases and weights of the network connection to train by ANN.
- Description: The purpose of this paper is to provide a proper, practical and convenient drilling rate index (DRI) prediction model based on rock material properties. In order to obtain this purpose, 47 DRI tests were used. In addition, the relevant strength properties i.e. uniaxial compressive strength and Brazilian tensile strength were also used and selected as input parameters to predict DRI. Examined simple regression analysis showed that the relationships between the DRI and predictors are statistically meaningful but not good enough for DRI estimation in practice. Moreover, multiple regression, artificial neural network (ANN) and hybrid genetic algorithm (GA)-ANN models were constructed to estimate DRI. Several performance indices i.e. coefficient of determination (R2), root mean square error and variance account for were used for evaluation of performance prediction the proposed methods. Based on these results and the use of simple ranking procedure, the best models were chosen. It was found that the hybrid GA-ANN technique can performed better in predicting DRI compared to other developed models. This is because of the fact that the proposed hybrid model can update the biases and weights of the network connection to train by ANN. © 2015 Springer International Publishing Switzerland
Artificial neural network as a tool for backbreak prediction
- 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 comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak
- 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.
Application of an expert system to predict maximum explosive charge used per delay in surface mining
- 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 for the assessment of blast vibration
- Authors: Khandelwal, Manoj
- Date: 2012
- Type: Text , Journal article
- Relation: Geotechnical and Geological Engineering Vol. 30, no. 1 (2012), p. 205-217
- Full Text: false
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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.
Artificial neural networks for the prediction of the trapping efficiency of a new sewer overflow screening device
- Authors: Phillips, David , Imteaz, Monzur , Aziz, Mubashir , Choudhury, Tanveer
- Date: 2011
- Type: Text , Conference paper
- Relation: 19th International Congress on Modelling and Simulation - Sustaining Our Future: Understanding and Living with Uncertainty, MODSIM2011 p. 3476-3482
- Full Text: false
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- Description: Some of the major concerns regarding sewer overflows to receiving water bodies include serious environmental, aesthetic and public health problems. Water management authorities are increasingly receiving public complaints that have led engineers to focus on means of retaining the entrained sewer solids within the sewer system during overflow events. During wet weather conditions, sewer overflows to receiving water bodies raise serious concern to environmental and community health concerns. To address these problems, different types of screening devices are used. Moreover, floatable control is preferred by most of the proposed and existing environmental regulations. This requirement triggers the need to research the different types of screening devices and screenings handling systems to select the most appropriate for a particular installation especially at unmanned locations. In the present study the sewer overflow device consists of a rectangular tank and a sharp crested weir that are followed by series of vertical parallel combs to separate entrained sewer solids from the overflow. The device does not require electrical or mechanical power for the self-cleansing mechanism, enabling the device to work efficiently in unmanned locations. Extensive laboratory investigations are underway to assess the effectiveness of a novel self-cleansing sewer overflow screening device. A series of laboratory tests to determine trapping efficiencies for common sewer solids were conducted for different flow conditions, number of combs layers, spacing of combs and weir crest lengths. Sewer solids from different density materials make sewer flow to analyze in complex Non-Newtonian fluid system with huge computational cost and complicity using physical law based modeling. On the flipside artificial neural model has the capacity to accurately predict the outcome of complex, non-linear physical systems with relatively poorly understood physicochemical processes which makes them highly desirable in the present study. Artificial Neural Networks (ANN) have already been successfully used to simulate flood forecasting in urban drainage system, real time control in combined sewer system, real time water level predictions of sewerage systems covering gauged and un-gauged sites etc. In case of sewer solid capture efficiency: neural network modeling is able to recognize nonlinear input output relations with adapting approach for changing circumstances. In the present study, feed forward artificial neural networks using back propagation algorithms were used, as such networks have been used almost exclusively in environmental modeling. A series of forty seven (47) sets of experimental data were collected to train (calibrate) the ANN model. In addition to these, eight (8) sets of experimental data were collected to validate the trained ANN network to be used in wider prospective of urban drainage conditions. The major areas covered in the ANN modeling include selection of input and output variables, optimization of the model, consideration of different learning algorithms, designing ANN's training & cross training processes and model validation. In the studied case, complex physical characteristics of different sewer solids, together with multi-fluid sewer system with variable flow phenomena makes it difficult to model with physical considerations. In case of sewer solid capture efficiency; artificial neural network modeling is able to learn the complex input-output relations with adapting approach for changing circumstances. Model considered different learning algorithms, diverse hidden layer structure with varied training samples to optimize the network. It is found that the model can successfully predict the experimental results with average absolute percentage errors varying from 4 to 7 percent.