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.
- 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
- Full Text:
- Reviewed:
- 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.
Effects of a proper feature selection on prediction and optimization of drilling rate using intelligent techniques
- Liao, Xiufeng, Khandelwal, Manoj, Yang, Haiqing, Koopialipoor, Mohammadreza, Murlidhar, Bhatawdekar
- Authors: Liao, Xiufeng , Khandelwal, Manoj , Yang, Haiqing , Koopialipoor, Mohammadreza , Murlidhar, Bhatawdekar
- Date: 2020
- Type: Text , Journal article
- Relation: Engineering with Computers Vol. 36, no. 2 (Apr 2020), p. 499-510
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- Description: One of the important factors during drilling times is the rate of penetration (ROP), which is controlled based on different variables. Factors affecting different drillings are of paramount importance. In the current research, an attempt was made to better recognize drilling parameters and optimize them based on an optimization algorithm. For this purpose, 618 data sets, including RPM, flushing media, and compressive strength parameters, were measured and collected. After an initial investigation, the compressive strength feature of samples, which is an important parameter from the rocks, was used as a proper criterion for classification. Then using intelligent systems, three different levels of the rock strength and all data were modeled. The results showed that systems which were classified based on compressive strength showed a better performance for ROP assessment due to the proximity of features. Therefore, these three levels were used for classification. A new artificial bee colony algorithm was used to solve this problem. Optimizations were applied to the selected models under different optimization conditions, and optimal states were determined. As determining drilling machine parameters is important, these parameters were determined based on optimal conditions. The obtained results showed that this intelligent system can well improve drilling conditions and increase the ROP value for three strength levels of the rocks. This modeling system can be used in different drilling operations.
- Authors: Liao, Xiufeng , Khandelwal, Manoj , Yang, Haiqing , Koopialipoor, Mohammadreza , Murlidhar, Bhatawdekar
- Date: 2020
- Type: Text , Journal article
- Relation: Engineering with Computers Vol. 36, no. 2 (Apr 2020), p. 499-510
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- Description: One of the important factors during drilling times is the rate of penetration (ROP), which is controlled based on different variables. Factors affecting different drillings are of paramount importance. In the current research, an attempt was made to better recognize drilling parameters and optimize them based on an optimization algorithm. For this purpose, 618 data sets, including RPM, flushing media, and compressive strength parameters, were measured and collected. After an initial investigation, the compressive strength feature of samples, which is an important parameter from the rocks, was used as a proper criterion for classification. Then using intelligent systems, three different levels of the rock strength and all data were modeled. The results showed that systems which were classified based on compressive strength showed a better performance for ROP assessment due to the proximity of features. Therefore, these three levels were used for classification. A new artificial bee colony algorithm was used to solve this problem. Optimizations were applied to the selected models under different optimization conditions, and optimal states were determined. As determining drilling machine parameters is important, these parameters were determined based on optimal conditions. The obtained results showed that this intelligent system can well improve drilling conditions and increase the ROP value for three strength levels of the rocks. This modeling system can be used in different drilling operations.
Optimization of blasting design in open pit limestone mines with the aim of reducing ground vibration using robust techniques
- Rezaeineshat, Afsaneh, Monjezi, Masoud, Mehrdanesh, Amirhossein, Khandelwal, Manoj
- Authors: Rezaeineshat, Afsaneh , Monjezi, Masoud , Mehrdanesh, Amirhossein , Khandelwal, Manoj
- Date: 2020
- Type: Text , Journal article
- Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 6, no. 2 (2020), p.
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- Description: Blasting operations create significant problems to residential and other structures located in the close proximity of the mines. Blast vibration is one of the most crucial nuisances of blasting, which should be accurately estimated to minimize its effect. In this paper, an attempt has been made to apply various models to predict ground vibrations due to mine blasting. To fulfill this aim, 112 blast operations were precisely measured and collected in one the limestone mines of Iran. These blast operation data were utilized to construct the artificial neural network (ANN) model to predict the peak particle velocity (PPV). The input parameters used in this study were burden, spacing, maximum charge per delay, distance from blast face to monitoring point and rock quality designation and output parameter was the PPV. The conventional empirical predictors and multivariate regression analysis were also performed on the same data sets to study the PPV. Accordingly, it was observed that the ANN model is more accurate as compared to the other employed predictors. Moreover, it was also revealed that the most influential parameters on the ground vibration are distance from the blast and maximum charge per delay, whereas the least effective parameters are burden, spacing and rock quality designation. Finally, in order to minimize PPV, the developed ANN model was used as an objective function for imperialist competitive algorithm (ICA). Eventually, it was found that the ICA algorithm is able to decrease PPV up to 59% by considering burden of 2.9 m, spacing of 4.4 m and charge per delay of 627 Kg. © 2020, Springer Nature Switzerland AG.
- Authors: Rezaeineshat, Afsaneh , Monjezi, Masoud , Mehrdanesh, Amirhossein , Khandelwal, Manoj
- Date: 2020
- Type: Text , Journal article
- Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 6, no. 2 (2020), p.
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- Description: Blasting operations create significant problems to residential and other structures located in the close proximity of the mines. Blast vibration is one of the most crucial nuisances of blasting, which should be accurately estimated to minimize its effect. In this paper, an attempt has been made to apply various models to predict ground vibrations due to mine blasting. To fulfill this aim, 112 blast operations were precisely measured and collected in one the limestone mines of Iran. These blast operation data were utilized to construct the artificial neural network (ANN) model to predict the peak particle velocity (PPV). The input parameters used in this study were burden, spacing, maximum charge per delay, distance from blast face to monitoring point and rock quality designation and output parameter was the PPV. The conventional empirical predictors and multivariate regression analysis were also performed on the same data sets to study the PPV. Accordingly, it was observed that the ANN model is more accurate as compared to the other employed predictors. Moreover, it was also revealed that the most influential parameters on the ground vibration are distance from the blast and maximum charge per delay, whereas the least effective parameters are burden, spacing and rock quality designation. Finally, in order to minimize PPV, the developed ANN model was used as an objective function for imperialist competitive algorithm (ICA). Eventually, it was found that the ICA algorithm is able to decrease PPV up to 59% by considering burden of 2.9 m, spacing of 4.4 m and charge per delay of 627 Kg. © 2020, Springer Nature Switzerland AG.
Stability prediction of Himalayan residual soil slope using artificial neural network
- Ray, Arunava, Kumar, Vikash, Kumar, Amit, Rai, Rajesh, Khandelwal, Manoj, Singh, T.
- Authors: Ray, Arunava , Kumar, Vikash , Kumar, Amit , Rai, Rajesh , Khandelwal, Manoj , Singh, T.
- Date: 2020
- Type: Text , Journal article
- Relation: Natural Hazards Vol. 103, no. 3 (2020), p. 3523-3540
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- Description: In the past decade, advances in machine learning (ML) techniques have resulted in developing sophisticated models that are capable of modelling extremely complex multi-factorial problems like slope stability analysis. The literature review indicates that considerable works have been done in slope stability using ML, but none of them covers the analysis of residual soil slope. The present study aims to develop an artificial neural network (ANN) model that can be employed for evaluating the factor of safety of Shiwalik Slopes in the Himalayan Region. Data obtained from numerical analysis of a residual soil slope were used to develop two ANN models (ANN1 and ANN2 utilising eleven input parameters, and scaled-down number of parameters based on correlation coefficient, respectively). A four-layer, feed-forward back-propagation neural network having the optimum number of hidden neurons is developed based on trial-and-error method. The results derived from ANN models were compared with those achieved from numerical analysis. Additionally, several performance indices such as coefficient of determination (R2), root mean square error, variance account for, and residual error were employed to evaluate the predictive performance of the developed ANN models. Both the ANN models have shown good prediction performance; however, the overall performance of the ANN2 model is better than the ANN1 model. It is concluded that the ANN models are reliable, valid, and straightforward computational tools that can be employed for slope stability analysis during the preliminary stage of designing infrastructure projects in residual soil slope. © 2020, Springer Nature B.V.
- Authors: Ray, Arunava , Kumar, Vikash , Kumar, Amit , Rai, Rajesh , Khandelwal, Manoj , Singh, T.
- Date: 2020
- Type: Text , Journal article
- Relation: Natural Hazards Vol. 103, no. 3 (2020), p. 3523-3540
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- Description: In the past decade, advances in machine learning (ML) techniques have resulted in developing sophisticated models that are capable of modelling extremely complex multi-factorial problems like slope stability analysis. The literature review indicates that considerable works have been done in slope stability using ML, but none of them covers the analysis of residual soil slope. The present study aims to develop an artificial neural network (ANN) model that can be employed for evaluating the factor of safety of Shiwalik Slopes in the Himalayan Region. Data obtained from numerical analysis of a residual soil slope were used to develop two ANN models (ANN1 and ANN2 utilising eleven input parameters, and scaled-down number of parameters based on correlation coefficient, respectively). A four-layer, feed-forward back-propagation neural network having the optimum number of hidden neurons is developed based on trial-and-error method. The results derived from ANN models were compared with those achieved from numerical analysis. Additionally, several performance indices such as coefficient of determination (R2), root mean square error, variance account for, and residual error were employed to evaluate the predictive performance of the developed ANN models. Both the ANN models have shown good prediction performance; however, the overall performance of the ANN2 model is better than the ANN1 model. It is concluded that the ANN models are reliable, valid, and straightforward computational tools that can be employed for slope stability analysis during the preliminary stage of designing infrastructure projects in residual soil slope. © 2020, Springer Nature B.V.
Waveform features and failure patterns of hollow cylindrical sandstone specimens under repetitive impact and triaxial confinements
- Wang, Shiming, Liu, Yunsi, Du, Kun, Zhou, Jian, Khandelwal, Manoj
- Authors: Wang, Shiming , Liu, Yunsi , Du, Kun , Zhou, Jian , Khandelwal, Manoj
- Date: 2020
- Type: Text , Journal article
- Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 6, no. 4 (2020), p.
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- Description: In underground engineering practice, the surrounding rocks are subjected to a nonuniform stress field with various radial gradients. In this study, a series of conventional triaxial repetitive impact tests using hollow cylindrical sandstone (HOS) specimens were conducted to reveal the impact waveform features and failure properties of rocks under nonuniform stress conditions. The tests were conducted using a modified large diameter split Hopkinson pressure bar testing system. The confining pressure was set as 5, 10 and 12 MPa. The data of specimens under equilibrium stress states were chosen and analyzed, and the results showed that more applied numbers of cyclic impact loads were needed to break rocks with the increase of confining pressure. Three types of cracks, i.e., ring-shaped cracks around the hole in the center of specimens, axial cracks located in the outer cylindrical surface, and lateral cracks fracturing rock fragments into small pieces appeared in HOS specimens. The failure degrees of HOS specimens could be judged by the waveform features of the reflected wave, and the waveform features of reflected wave are similar in the same failure mode, regardless of the impact velocity and the number of impacts, which only affect the failure degree. © 2020, Springer Nature Switzerland AG.
- Description: The work reported here is supported by financial grants from both the National Natural Science Foundation of China (51774326, 41807259, 51604109 51704109).
- Authors: Wang, Shiming , Liu, Yunsi , Du, Kun , Zhou, Jian , Khandelwal, Manoj
- Date: 2020
- Type: Text , Journal article
- Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 6, no. 4 (2020), p.
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- Description: In underground engineering practice, the surrounding rocks are subjected to a nonuniform stress field with various radial gradients. In this study, a series of conventional triaxial repetitive impact tests using hollow cylindrical sandstone (HOS) specimens were conducted to reveal the impact waveform features and failure properties of rocks under nonuniform stress conditions. The tests were conducted using a modified large diameter split Hopkinson pressure bar testing system. The confining pressure was set as 5, 10 and 12 MPa. The data of specimens under equilibrium stress states were chosen and analyzed, and the results showed that more applied numbers of cyclic impact loads were needed to break rocks with the increase of confining pressure. Three types of cracks, i.e., ring-shaped cracks around the hole in the center of specimens, axial cracks located in the outer cylindrical surface, and lateral cracks fracturing rock fragments into small pieces appeared in HOS specimens. The failure degrees of HOS specimens could be judged by the waveform features of the reflected wave, and the waveform features of reflected wave are similar in the same failure mode, regardless of the impact velocity and the number of impacts, which only affect the failure degree. © 2020, Springer Nature Switzerland AG.
- Description: The work reported here is supported by financial grants from both the National Natural Science Foundation of China (51774326, 41807259, 51604109 51704109).
Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique
- Khandelwal, Manoj, Armaghani, Danial
- 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
- Authors: Khandelwal, Manoj , Armaghani, Danial
- Date: 2016
- Type: Text , Journal article
- Relation: Geotechnical and Geological Engineering Vol. 34, no. 2 (2016), p. 605-620
- Full Text:
- Reviewed:
- 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
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.
- Authors: Alamuti, Roghayeh , Barjini, Hassan , Khandelwal, Manoj , Jafarabad, Mohammad
- Date: 2015
- Type: Text , Conference proceedings
- Full Text:
- 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.
Differential evolution algorithm for predicting blast induced ground vibrations
- Saadat, Mahdi, Hasanzade, Ali, Khandelwal, Manoj
- Authors: Saadat, Mahdi , Hasanzade, Ali , Khandelwal, Manoj
- Date: 2015
- Type: Text , Journal article
- Relation: International Journal of Rock Mechanics and Mining Sciences Vol. 77, no. (2015), p. 97-104
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- Description: 1. Introduction One of the most crucial problems in construction blasting is to predict and then mitigate the ground vibration [1]. Blast-induced ground vibration is considered as one of the most important environmental hazards of mining operations and civil engineering projects. Intense vibration can cause critical damage to structures and plants nearby the open-pit mines, dams, and mine slopes, etc [2] and [3]. Researchers who deals with this undesirable phenomenon take into account various range of parameters in order to mitigate the detrimental effects of blasting. Blast influencing parameters can be divided into two categories [2]: (a) Uncontrollable parameters, such as geological and geotechnical characteristics of the rockmass. (b) Controllable parameters, such as burden, spacing, stemming, sub-drilling, delay time, etc.
- Authors: Saadat, Mahdi , Hasanzade, Ali , Khandelwal, Manoj
- Date: 2015
- Type: Text , Journal article
- Relation: International Journal of Rock Mechanics and Mining Sciences Vol. 77, no. (2015), p. 97-104
- Full Text:
- Reviewed:
- Description: 1. Introduction One of the most crucial problems in construction blasting is to predict and then mitigate the ground vibration [1]. Blast-induced ground vibration is considered as one of the most important environmental hazards of mining operations and civil engineering projects. Intense vibration can cause critical damage to structures and plants nearby the open-pit mines, dams, and mine slopes, etc [2] and [3]. Researchers who deals with this undesirable phenomenon take into account various range of parameters in order to mitigate the detrimental effects of blasting. Blast influencing parameters can be divided into two categories [2]: (a) Uncontrollable parameters, such as geological and geotechnical characteristics of the rockmass. (b) Controllable parameters, such as burden, spacing, stemming, sub-drilling, delay time, etc.
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.
- 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
- Full Text:
- Reviewed:
- 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.
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.
- 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
- Full Text:
- Reviewed:
- 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.