Mineral texture identification using local binary patterns equipped with a Classification and Recognition Updating System (CARUS)
- Aligholi, Saeed, Khajavi, Reza, Khandelwal, Manoj, Armaghani, Danial
- Authors: Aligholi, Saeed , Khajavi, Reza , Khandelwal, Manoj , Armaghani, Danial
- Date: 2022
- Type: Text , Journal article
- Relation: Sustainability (Switzerland) Vol. 14, no. 18 (2022), p.
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- Description: In this paper, a rotation-invariant local binary pattern operator equipped with a local contrast measure (riLBPc) is employed to characterize the type of mineral twinning by inspecting the texture properties of crystals. The proposed method uses photomicrographs of minerals and produces LBP histograms, which might be compared with those included in a predefined database using the Kullback–Leibler divergence-based metric. The paper proposes a new LBP-based scheme for concurrent classification and recognition tasks, followed by a novel online updating routine to enhance the locally developed mineral LBP database. The discriminatory power of the proposed Classification and Recognition Updating System (CARUS) for texture identification scheme is verified for plagioclase, orthoclase, microcline, and quartz minerals with sensitivity (TPR) near 99.9%, 87%, 99.9%, and 96%, and accuracy (ACC) equal to about 99%, 97%, 99%, and 99%, respectively. According to the results, the introduced CARUS system is a promising approach that can be applied in a variety of different fields dealing with classification and feature recognition tasks. © 2022 by the authors.
- Authors: Aligholi, Saeed , Khajavi, Reza , Khandelwal, Manoj , Armaghani, Danial
- Date: 2022
- Type: Text , Journal article
- Relation: Sustainability (Switzerland) Vol. 14, no. 18 (2022), p.
- Full Text:
- Reviewed:
- Description: In this paper, a rotation-invariant local binary pattern operator equipped with a local contrast measure (riLBPc) is employed to characterize the type of mineral twinning by inspecting the texture properties of crystals. The proposed method uses photomicrographs of minerals and produces LBP histograms, which might be compared with those included in a predefined database using the Kullback–Leibler divergence-based metric. The paper proposes a new LBP-based scheme for concurrent classification and recognition tasks, followed by a novel online updating routine to enhance the locally developed mineral LBP database. The discriminatory power of the proposed Classification and Recognition Updating System (CARUS) for texture identification scheme is verified for plagioclase, orthoclase, microcline, and quartz minerals with sensitivity (TPR) near 99.9%, 87%, 99.9%, and 96%, and accuracy (ACC) equal to about 99%, 97%, 99%, and 99%, respectively. According to the results, the introduced CARUS system is a promising approach that can be applied in a variety of different fields dealing with classification and feature recognition tasks. © 2022 by the authors.
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
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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.
A study on environmental issues of blasting using advanced support vector machine algorithms
- Chen, Lihua, Armaghani, Danial, Fakharian, Pouyan, Bhatawdekar, Ramesh, Samui, P., Khandelwal, Manoj, Khedher, Khaled
- Authors: Chen, Lihua , Armaghani, Danial , Fakharian, Pouyan , Bhatawdekar, Ramesh , Samui, P. , Khandelwal, Manoj , Khedher, Khaled
- Date: 2022
- Type: Text , Journal article
- Relation: International Journal of Environmental Science and Technology Vol. 19, no. 7 (2022), p. 6221-6240
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- Description: Air overpressure is a critical negative effect of blasting in construction or production sites and projects. So far, many attempts have been made to prevent or reduce this negative effect on the nearby construction, equipment, or people. While various experiential equations have been proposed to forecast the air overpressure value for determining the blasting area, these models are typically inaccurate and impractical. Due to the recent efforts to predict the air overpressure by employing artificial intelligence techniques, this study developed five support vector machine-based models optimized by some praised optimization techniques, including the moth flame optimization, particle swarm optimization, grey wolf optimization, cuckoo optimization algorithm, and whale optimization algorithm. These algorithms optimize the most important parameters of the support vector machine, including “C” and “gamma”, and improve the performance of this model for air overpressure prediction. The findings showed that the moth flame optimization algorithm is the best optimizer for support vector machine and is suitable for air overpressure prediction. The support vector machine–moth flame optimization model achieved the best R2 (train: 0.9939; test: 0.9941) and comprehensive score (34). On the other hand, the worst model was the support vector machine–particle swarm optimization, which achieved the lowest comprehensive score (13). In addition, all optimization techniques improved the performance of the single support vector machine model. The findings of this study imply that all optimization techniques successfully enhanced the performance of the support vector machine model; however, the moth flame optimization optimizer was the most effective one. The support vector machine–moth flame optimization technique can be employed to solve other mining-related issues. © 2022, Islamic Azad University (IAU). Correction to: A study on environmental issues of blasting using advanced support vector machine algorithms (International Journal of Environmental Science and Technology, (2022), 19, 7, (6221-6240), 10.1007/s13762-022-03999-y): The original version of this article unfortunately contains two mistakes. The spelling of the third author's name was incorrect. The correct name is Pouyan Fakharian (P. Fakharian). Another error was in the acknowledgment section. The correct Grant No. is KJQN202103415. © The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University 2022
- Authors: Chen, Lihua , Armaghani, Danial , Fakharian, Pouyan , Bhatawdekar, Ramesh , Samui, P. , Khandelwal, Manoj , Khedher, Khaled
- Date: 2022
- Type: Text , Journal article
- Relation: International Journal of Environmental Science and Technology Vol. 19, no. 7 (2022), p. 6221-6240
- Full Text:
- Reviewed:
- Description: Air overpressure is a critical negative effect of blasting in construction or production sites and projects. So far, many attempts have been made to prevent or reduce this negative effect on the nearby construction, equipment, or people. While various experiential equations have been proposed to forecast the air overpressure value for determining the blasting area, these models are typically inaccurate and impractical. Due to the recent efforts to predict the air overpressure by employing artificial intelligence techniques, this study developed five support vector machine-based models optimized by some praised optimization techniques, including the moth flame optimization, particle swarm optimization, grey wolf optimization, cuckoo optimization algorithm, and whale optimization algorithm. These algorithms optimize the most important parameters of the support vector machine, including “C” and “gamma”, and improve the performance of this model for air overpressure prediction. The findings showed that the moth flame optimization algorithm is the best optimizer for support vector machine and is suitable for air overpressure prediction. The support vector machine–moth flame optimization model achieved the best R2 (train: 0.9939; test: 0.9941) and comprehensive score (34). On the other hand, the worst model was the support vector machine–particle swarm optimization, which achieved the lowest comprehensive score (13). In addition, all optimization techniques improved the performance of the single support vector machine model. The findings of this study imply that all optimization techniques successfully enhanced the performance of the support vector machine model; however, the moth flame optimization optimizer was the most effective one. The support vector machine–moth flame optimization technique can be employed to solve other mining-related issues. © 2022, Islamic Azad University (IAU). Correction to: A study on environmental issues of blasting using advanced support vector machine algorithms (International Journal of Environmental Science and Technology, (2022), 19, 7, (6221-6240), 10.1007/s13762-022-03999-y): The original version of this article unfortunately contains two mistakes. The spelling of the third author's name was incorrect. The correct name is Pouyan Fakharian (P. Fakharian). Another error was in the acknowledgment section. The correct Grant No. is KJQN202103415. © The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University 2022
A combination of expert-based system and advanced decision-tree algorithms to predict air-overpressure resulting from quarry blasting
- He, Ziguang, Armaghani, Danial, Masoumnezhad, Mojtaba, Khandelwal, Manoj, Zhou, Jian, Murlidhar, Bhatawdekar
- Authors: He, Ziguang , Armaghani, Danial , Masoumnezhad, Mojtaba , Khandelwal, Manoj , Zhou, Jian , Murlidhar, Bhatawdekar
- Date: 2021
- Type: Text , Journal article
- Relation: Natural Resources Research Vol. 30, no. 2 (2021), p. 1889-1903
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- Description: This study combined a fuzzy Delphi method (FDM) and two advanced decision-tree algorithms to predict air-overpressure (AOp) caused by mine blasting. The FDM was used for input selection. Thus, the panel of experts selected four inputs, including powder factor, max charge per delay, stemming length, and distance from the blast face. Once the input selection was completed, two decision-tree algorithms, namely extreme gradient boosting tree (XGBoost-tree) and random forest (RF), were applied using the inputs selected by the experts. The models are evaluated with the following criteria: correlation coefficient, mean absolute error, gains chart, and Taylor diagram. The applied models were compared with the XGBoost-tree and RF models using the full set of data without input selection results. The results of hybridization showed that the XGBoost-tree model outperformed the RF model. Concerning the gains, the XGBoost-tree again outperformed the RF model. In comparison with the single decision-tree models, the single models had slightly better correlation coefficients; however, the hybridized models were simpler and easier to understand, analyze and implement. In addition, the Taylor diagram showed that the models applied outperformed some other conventional machine learning models, including support vector machine, k-nearest neighbors, and artificial neural network. Overall, the findings of this study suggest that combining expert opinion and advanced decision-tree algorithms can result in accurate and easy to understand predictions of AOp resulting from blasting in quarry sites. © 2020, International Association for Mathematical Geosciences.
- Authors: He, Ziguang , Armaghani, Danial , Masoumnezhad, Mojtaba , Khandelwal, Manoj , Zhou, Jian , Murlidhar, Bhatawdekar
- Date: 2021
- Type: Text , Journal article
- Relation: Natural Resources Research Vol. 30, no. 2 (2021), p. 1889-1903
- Full Text:
- Reviewed:
- Description: This study combined a fuzzy Delphi method (FDM) and two advanced decision-tree algorithms to predict air-overpressure (AOp) caused by mine blasting. The FDM was used for input selection. Thus, the panel of experts selected four inputs, including powder factor, max charge per delay, stemming length, and distance from the blast face. Once the input selection was completed, two decision-tree algorithms, namely extreme gradient boosting tree (XGBoost-tree) and random forest (RF), were applied using the inputs selected by the experts. The models are evaluated with the following criteria: correlation coefficient, mean absolute error, gains chart, and Taylor diagram. The applied models were compared with the XGBoost-tree and RF models using the full set of data without input selection results. The results of hybridization showed that the XGBoost-tree model outperformed the RF model. Concerning the gains, the XGBoost-tree again outperformed the RF model. In comparison with the single decision-tree models, the single models had slightly better correlation coefficients; however, the hybridized models were simpler and easier to understand, analyze and implement. In addition, the Taylor diagram showed that the models applied outperformed some other conventional machine learning models, including support vector machine, k-nearest neighbors, and artificial neural network. Overall, the findings of this study suggest that combining expert opinion and advanced decision-tree algorithms can result in accurate and easy to understand predictions of AOp resulting from blasting in quarry sites. © 2020, International Association for Mathematical Geosciences.
Rock-burst occurrence prediction based on optimized naïve bayes models
- Ke, Bo, Khandelwal, Manoj, Asteris, Panagiotis, Skentou, Athanasia, Mamou, Anna, Armaghani, Danial
- Authors: Ke, Bo , Khandelwal, Manoj , Asteris, Panagiotis , Skentou, Athanasia , Mamou, Anna , Armaghani, Danial
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 91347-91360
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- Description: Rock-burst is a common failure in hard rock related projects in civil and mining construction and therefore, proper classification and prediction of this phenomenon is of interest. This research presents the development of optimized naïve Bayes models, in predicting rock-burst failures in underground projects. The naïve Bayes models were optimized using four weight optimization techniques including forward, backward, particle swarm optimization, and evolutionary. An evolutionary random forest model was developed to identify the most significant input parameters. The maximum tangential stress, elastic energy index, and uniaxial tensile stress were then selected by the feature selection technique (i.e., evolutionary random forest) to develop the optimized naïve Bayes models. The performance of the models was assessed using various criteria as well as a simple ranking system. The results of this research showed that particle swarm optimization was the most effective technique in improving the accuracy of the naïve Bayes model for rock-burst prediction (cumulative ranking = 21), while the backward technique was the worst weight optimization technique (cumulative ranking = 11). All the optimized naïve Bayes models identified the maximum tangential stress as the most significant parameter in predicting rock-burst failures. The results of this research demonstrate that particle swarm optimization technique may improve the accuracy of naïve Bayes algorithms in predicting rock-burst occurrence. © 2013 IEEE.
- Authors: Ke, Bo , Khandelwal, Manoj , Asteris, Panagiotis , Skentou, Athanasia , Mamou, Anna , Armaghani, Danial
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 91347-91360
- Full Text:
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- Description: Rock-burst is a common failure in hard rock related projects in civil and mining construction and therefore, proper classification and prediction of this phenomenon is of interest. This research presents the development of optimized naïve Bayes models, in predicting rock-burst failures in underground projects. The naïve Bayes models were optimized using four weight optimization techniques including forward, backward, particle swarm optimization, and evolutionary. An evolutionary random forest model was developed to identify the most significant input parameters. The maximum tangential stress, elastic energy index, and uniaxial tensile stress were then selected by the feature selection technique (i.e., evolutionary random forest) to develop the optimized naïve Bayes models. The performance of the models was assessed using various criteria as well as a simple ranking system. The results of this research showed that particle swarm optimization was the most effective technique in improving the accuracy of the naïve Bayes model for rock-burst prediction (cumulative ranking = 21), while the backward technique was the worst weight optimization technique (cumulative ranking = 11). All the optimized naïve Bayes models identified the maximum tangential stress as the most significant parameter in predicting rock-burst failures. The results of this research demonstrate that particle swarm optimization technique may improve the accuracy of naïve Bayes algorithms in predicting rock-burst occurrence. © 2013 IEEE.
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
An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass
- Parsajoo, Maryama, Mohammed, Ahmed, Yagiz, Saffet, Armaghani, Danial, Khandelwal, Manoj
- Authors: Parsajoo, Maryama , Mohammed, Ahmed , Yagiz, Saffet , Armaghani, Danial , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 13, no. 6 (2021), p. 1290-1299
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- Description: Field penetration index (FPI) is one of the representative key parameters to examine the tunnel boring machine (TBM) performance. Lack of accurate FPI prediction can be responsible for numerous disastrous incidents associated with rock mechanics and engineering. This study aims to predict TBM performance (i.e. FPI) by an efficient and improved adaptive neuro-fuzzy inference system (ANFIS) model. This was done using an evolutionary algorithm, i.e. artificial bee colony (ABC) algorithm mixed with the ANFIS model. The role of ABC algorithm in this system is to find the optimum membership functions (MFs) of ANFIS model to achieve a higher degree of accuracy. The procedure and modeling were conducted on a tunnelling database comprising of more than 150 data samples where brittleness index (BI), fracture spacing, α angle between the plane of weakness and the TBM driven direction, and field single cutter load were assigned as model inputs to approximate FPI values. According to the results obtained by performance indices, the proposed ANFIS_ABC model was able to receive the highest accuracy level in predicting FPI values compared with ANFIS model. In terms of coefficient of determination (R2), the values of 0.951 and 0.901 were obtained for training and testing stages of the proposed ANFIS_ABC model, respectively, which confirm its power and capability in solving TBM performance problem. The proposed model can be used in the other areas of rock mechanics and underground space technologies with similar conditions. © 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences
- Authors: Parsajoo, Maryama , Mohammed, Ahmed , Yagiz, Saffet , Armaghani, Danial , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 13, no. 6 (2021), p. 1290-1299
- Full Text:
- Reviewed:
- Description: Field penetration index (FPI) is one of the representative key parameters to examine the tunnel boring machine (TBM) performance. Lack of accurate FPI prediction can be responsible for numerous disastrous incidents associated with rock mechanics and engineering. This study aims to predict TBM performance (i.e. FPI) by an efficient and improved adaptive neuro-fuzzy inference system (ANFIS) model. This was done using an evolutionary algorithm, i.e. artificial bee colony (ABC) algorithm mixed with the ANFIS model. The role of ABC algorithm in this system is to find the optimum membership functions (MFs) of ANFIS model to achieve a higher degree of accuracy. The procedure and modeling were conducted on a tunnelling database comprising of more than 150 data samples where brittleness index (BI), fracture spacing, α angle between the plane of weakness and the TBM driven direction, and field single cutter load were assigned as model inputs to approximate FPI values. According to the results obtained by performance indices, the proposed ANFIS_ABC model was able to receive the highest accuracy level in predicting FPI values compared with ANFIS model. In terms of coefficient of determination (R2), the values of 0.951 and 0.901 were obtained for training and testing stages of the proposed ANFIS_ABC model, respectively, which confirm its power and capability in solving TBM performance problem. The proposed model can be used in the other areas of rock mechanics and underground space technologies with similar conditions. © 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences
Estimation of the TBM advance rate under hard rock conditions using XGBoost and Bayesian optimization
- Zhou, Jian, Qiu, Yingui, Zhu, Shuangli, Armaghani, Danial, Khandelwal, Manoj, Mohamad, Edy
- Authors: Zhou, Jian , Qiu, Yingui , Zhu, Shuangli , Armaghani, Danial , Khandelwal, Manoj , Mohamad, Edy
- Date: 2021
- Type: Text , Journal article
- Relation: Underground Space Vol. 6, no. 5 (Oct 2021), p. 506-515
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- Description: The advance rate (AR) of a tunnel boring machine (TBM) under hard rock conditions is a key parameter in the successful implementation of tunneling engineering. In this study, we improved the accuracy of prediction models by employing a hybrid model of extreme gradient boosting (XGBoost) with Bayesian optimization (BO) to model the TBM AR. To develop the proposed models, 1286 sets of data were collected from the Peng Selangor Raw Water Transfer tunnel project in Malaysia. The database consists of rock mass and intact rock features, including rock mass rating, rock quality designation, weathered zone, uniaxial compressive strength, and Brazilian tensile strength. Machine specifications, including revolution per minute and thrust force, were considered to predict the TBM AR. The accuracies of the predictive models were examined using the root mean squares error (RMSE) and the coefficient of determination (R-2) between the observed and predicted yield by employing a five-fold cross-validation procedure. Results showed that the BO algorithm can capture better hyper-parameters for the XGBoost prediction model than can the default XGBoost model. The robustness and generalization of the BO-XGBoost model yielded prominent results with RMSE and R-2 values of 0.0967 and 0.9806 (for the testing phase), respectively. The results demonstrated the merits of the proposed BO-XGBoost model. In addition, variable importance through mutual information tests was applied to interpret the XGBoost model and demonstrated that machine parameters have the greatest impact as compared to rock mass and material properties.
- Authors: Zhou, Jian , Qiu, Yingui , Zhu, Shuangli , Armaghani, Danial , Khandelwal, Manoj , Mohamad, Edy
- Date: 2021
- Type: Text , Journal article
- Relation: Underground Space Vol. 6, no. 5 (Oct 2021), p. 506-515
- Full Text:
- Reviewed:
- Description: The advance rate (AR) of a tunnel boring machine (TBM) under hard rock conditions is a key parameter in the successful implementation of tunneling engineering. In this study, we improved the accuracy of prediction models by employing a hybrid model of extreme gradient boosting (XGBoost) with Bayesian optimization (BO) to model the TBM AR. To develop the proposed models, 1286 sets of data were collected from the Peng Selangor Raw Water Transfer tunnel project in Malaysia. The database consists of rock mass and intact rock features, including rock mass rating, rock quality designation, weathered zone, uniaxial compressive strength, and Brazilian tensile strength. Machine specifications, including revolution per minute and thrust force, were considered to predict the TBM AR. The accuracies of the predictive models were examined using the root mean squares error (RMSE) and the coefficient of determination (R-2) between the observed and predicted yield by employing a five-fold cross-validation procedure. Results showed that the BO algorithm can capture better hyper-parameters for the XGBoost prediction model than can the default XGBoost model. The robustness and generalization of the BO-XGBoost model yielded prominent results with RMSE and R-2 values of 0.0967 and 0.9806 (for the testing phase), respectively. The results demonstrated the merits of the proposed BO-XGBoost model. In addition, variable importance through mutual information tests was applied to interpret the XGBoost model and demonstrated that machine parameters have the greatest impact as compared to rock mass and material properties.
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