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This research provides an innovative combination of an adaptive neuro-fuzzy inference system (ANFIS) model for solving a nonlinear and complex problem related to soil shear strength prediction. The new hybrid model is optimized by an optimization technique i.e., Henry gas solubility optimization (HGSO), called as HGSO-ANFIS. In predicting soil shear strength, the results of liquid limit, specific gravity, clay content, moisture content, void ratio, and plastic limit were considered and used as the model predictors. The HGSO-ANFIS model is implemented based on Henry’s law and can be used in engineering issues. The HGSO algorithm is developed based on the huddling behavior of gas to find the main answers and to avoid being trapped in the local minima. The search space in this model can be presented with a better performance than the base model. The performance of the new hybrid HGSO-ANFIS model was tested with real data to compare the other ANFIS-based models. The performance of the best HGSO-ANFIS model for the testing data was 0.954 and 0.1891 for coefficient of determination (R2) and root mean square error (RMSE), respectively. The model results showed that the new hybrid HGSO-ANFIS model can get higher level of accuracy compared to the other ANFIS-based models and it can be applied for various prediction and optimization problems.
Wangfei Ding; Manh Duc Nguyen; Ahmed Salih Mohammed; Danial Jahed Armaghani; Mahdi Hasanipanah; Loi Van Bui; Binh Thai Pham. A new development of ANFIS-Based Henry gas solubility optimization technique for prediction of soil shear strength. Transportation Geotechnics 2021, 29, 100579 .
AMA StyleWangfei Ding, Manh Duc Nguyen, Ahmed Salih Mohammed, Danial Jahed Armaghani, Mahdi Hasanipanah, Loi Van Bui, Binh Thai Pham. A new development of ANFIS-Based Henry gas solubility optimization technique for prediction of soil shear strength. Transportation Geotechnics. 2021; 29 ():100579.
Chicago/Turabian StyleWangfei Ding; Manh Duc Nguyen; Ahmed Salih Mohammed; Danial Jahed Armaghani; Mahdi Hasanipanah; Loi Van Bui; Binh Thai Pham. 2021. "A new development of ANFIS-Based Henry gas solubility optimization technique for prediction of soil shear strength." Transportation Geotechnics 29, no. : 100579.
Liquefaction has caused many catastrophes during earthquakes in the past. When an earthquake is occurring, saturated granular soils may be subjected to the liquefaction phenomenon that can result in significant hazards. Therefore, a valid and reliable prediction of soil liquefaction potential is of high importance, especially when designing civil engineering projects. This study developed the least squares support vector machine (LSSVM) and radial basis function neural network (RBFNN) in combination with the optimization algorithms, i.e., the grey wolves optimization (GWO), differential evolution (DE), and genetic algorithm (GA) to predict the soil liquefaction potential. Afterwards, statistical scores such as root mean square error were applied to evaluate the developed models. The computational results showed that the proposed RBFNN-GWO and LSSVM-GWO, with Coefficient of Determination (R2) = 1 and Root Mean Square Error (RMSE) = 0, produced better results than other models proposed previously in the literature for the prediction of the soil liquefaction potential. It is an efficient and effective alternative for the soil liquefaction potential prediction. Furthermore, the results of this study confirmed the effectiveness of the GWO algorithm in training the RBFNN and LSSVM models. According to sensitivity analysis results, the cyclic stress ratio was also found as the most effective parameter on the soil liquefaction in the studied case.
Mingxiang Cai; Ouaer Hocine; Ahmed Salih Mohammed; Xiaoling Chen; Menad Nait Amar; Mahdi Hasanipanah. Integrating the LSSVM and RBFNN models with three optimization algorithms to predict the soil liquefaction potential. Engineering with Computers 2021, 1 -13.
AMA StyleMingxiang Cai, Ouaer Hocine, Ahmed Salih Mohammed, Xiaoling Chen, Menad Nait Amar, Mahdi Hasanipanah. Integrating the LSSVM and RBFNN models with three optimization algorithms to predict the soil liquefaction potential. Engineering with Computers. 2021; ():1-13.
Chicago/Turabian StyleMingxiang Cai; Ouaer Hocine; Ahmed Salih Mohammed; Xiaoling Chen; Menad Nait Amar; Mahdi Hasanipanah. 2021. "Integrating the LSSVM and RBFNN models with three optimization algorithms to predict the soil liquefaction potential." Engineering with Computers , no. : 1-13.
The use of explosives is a common and economical method to fragment and/or displace hard rocks in tunnels and surface and underground mines. Ground vibration, as a side environmental effect induced by blast events, has detrimental impacts on nearby structures like dams and buildings. Therefore, an accurate and reliable estimation of ground vibration is imperative. The goal of this paper is to present a new hybrid model by combining chaos recurrent adaptive neuro-fuzzy inference system (CRANFIS) and particle swarm optimization (PSO) to predict ground vibration. To the best of our knowledge, this is the first research that predicts the ground vibration through a model integrating CRANFIS and PSO. To evaluate the efficiency of the proposed model, the results of CRANFIS-PSO were compared with those of the CRANFIS, RANFIS, ANFIS, artificial neural network (ANN), and several empirical methods. In other words, first, the empirical methods were developed; then, due to their unacceptable performance, the artificial intelligence methods were developed. The results clearly indicated the superiority of CRANFIS-PSO over the above-mentioned methods in terms of predicting ground vibration. The values of coefficient of determination (R2) obtained from CRANFIS-PSO, CRANFIS, RANFIS, ANFIS, and ANN models were 0.997, 0.967, 0.958, 0.822, and 0.775, respectively. Accordingly, the CRANFIS-PSO model could be employed as a reliable and accurate data intelligent model to solve highly-nonlinear problems such as the prediction of blast-induced flyrock and air-overpressure.
Wei Zhu; Hima Nikafshan Rad; Mahdi Hasanipanah. A chaos recurrent ANFIS optimized by PSO to predict ground vibration generated in rock blasting. Applied Soft Computing 2021, 108, 107434 .
AMA StyleWei Zhu, Hima Nikafshan Rad, Mahdi Hasanipanah. A chaos recurrent ANFIS optimized by PSO to predict ground vibration generated in rock blasting. Applied Soft Computing. 2021; 108 ():107434.
Chicago/Turabian StyleWei Zhu; Hima Nikafshan Rad; Mahdi Hasanipanah. 2021. "A chaos recurrent ANFIS optimized by PSO to predict ground vibration generated in rock blasting." Applied Soft Computing 108, no. : 107434.
Air overpressure (AOp) is a hazardous effect induced by the blasting method in surface mines. Therefore, it needs to be predicted to reduce the potential risk of damage. The aim of this study is to offer an efficient method to predict AOp using a cascaded forward neural network (CFNN) trained by Levenberg–Marquardt (LM) algorithm, called the CFNN-LM model. Additionally, a generalized regression neural network (GRNN) and extreme learning machine (ELM) were employed to demonstrate the accuracy level of the proposed CFNN-LM model. To conduct the CFNN-LM, GRNN, and ELM models, an extensive database, related to four quarry sites in Malaysia, was used including 62 sets of dependent and independent parameters. Next, the performances of the aforementioned models were checked and discussed through statistical criteria and efficient graphical tools. Finally, the results showed the superiority of CFNN-LM (R2 = 0.9263 and RMSE = 3.0444) over GRNN (R2 = 0.7787 and RMSE = 5.1211) and ELM (R2 = 0.6984 and RMSE = 6.2537) models in terms of prediction accuracy. Furthermore, three different regression analysis metrics were used to perform the sensitivity analysis, and according to the obtained results, the maximum charge per delay (\(\beta\) = 0.475, SE = 0.115, t-test = 4.125) was considered as the most influential feature in modeling the AOp.
Jie Zeng; Mehdi Jamei; Menad Nait Amar; Mahdi Hasanipanah; Parichehr Bayat. A novel solution for simulating air overpressure resulting from blasting using an efficient cascaded forward neural network. Engineering with Computers 2021, 1 -13.
AMA StyleJie Zeng, Mehdi Jamei, Menad Nait Amar, Mahdi Hasanipanah, Parichehr Bayat. A novel solution for simulating air overpressure resulting from blasting using an efficient cascaded forward neural network. Engineering with Computers. 2021; ():1-13.
Chicago/Turabian StyleJie Zeng; Mehdi Jamei; Menad Nait Amar; Mahdi Hasanipanah; Parichehr Bayat. 2021. "A novel solution for simulating air overpressure resulting from blasting using an efficient cascaded forward neural network." Engineering with Computers , no. : 1-13.
Circular failure can be seen in weak rocks, the slope of soil, mine dump, and highly jointed rock mass. The challenging issue is to accurately predict the safety factor (SF) and the behavior of slopes. The aim of this study is to offer advanced and accurate models to predict the SF of slopes through machine learning methods improved by optimization algorithms. To this view, three different methods, i.e., trial and error (TE) method, gravitational search algorithm (GSA), and whale optimization algorithm (WOA) were used to investigate the proper control parameters of least squares support vector machine (LSSVM) method. In the constructed LSSVM-TE, LSSVM-GSA and LSSVM-WOA methods, six effective parameters on the SF, such as pore pressure ratio and angle of internal friction, were used as the input parameters. The results of the error criteria indicated that both GSA and WOA can improve the performance prediction of the LSSVM method in predicting the SF. However, the LSSVM-WOA method, with root mean square error of 0.141, performed better than the LSSVM-GSA with root mean square error of 0.170.
Fan Zeng; Menad Nait Amar; Ahmed Salih Mohammed; Mohammad Reza Motahari; Mahdi Hasanipanah. Improving the performance of LSSVM model in predicting the safety factor for circular failure slope through optimization algorithms. Engineering with Computers 2021, 1 -12.
AMA StyleFan Zeng, Menad Nait Amar, Ahmed Salih Mohammed, Mohammad Reza Motahari, Mahdi Hasanipanah. Improving the performance of LSSVM model in predicting the safety factor for circular failure slope through optimization algorithms. Engineering with Computers. 2021; ():1-12.
Chicago/Turabian StyleFan Zeng; Menad Nait Amar; Ahmed Salih Mohammed; Mohammad Reza Motahari; Mahdi Hasanipanah. 2021. "Improving the performance of LSSVM model in predicting the safety factor for circular failure slope through optimization algorithms." Engineering with Computers , no. : 1-12.
This study constructs and verifies a new statistical meta based-model to predict tunnel-boring machine (TBM) performance, namely, polynomial chaos expansion (PCE). To test the validity of the proposed PCE, two well-known mathematical models, namely, response surface method (RSM) and multivariate adaptive regression spline (MARS) were developed. According to the results, it can be found that the PCE model, with a coefficient of determination (R2 ) of 0.843, was superior in comparison with the RSM and MARS models as well as those formerly presented in the literature for the same database and rock conditions. Abbreviations: ANFIS: Adaptive Neuro-Fuzzy Inference System; ANN: Artificial Neural Networks; AR: Advance Rate; BI: Rock Brittleness; BTS: Brazilian Tensile Strength; CP: Cutterhead Power; CT: Cutterhead Torque; d: Modified Agreement Index; DNN: Deep Neural Networks; DPW: Distance between Planes of Weakness; ICA: Imperialist Competitive Algorithm; MAE: Mean Absolute Error; MARS: Multivariate Adaptive Regression Spline; NSE: Modified Nash and Sutcliffe Efficiency; NTNU: Norwegian Institute of Technology; PCE: Polynomial Chaos Expansion; PR: Penetration Rate; PSI: Point Strength Index; PSO: Particle Swarm Optimisation; R2: Coefficient of Determination; RF: Random Forests; RMR: Rock Mass Rating; RMSE: Root Mean Square Error; RQD: Rock Quality Designation; RSM: Response Surface Method; RSR: Rock Structure Rating; SE: Specific Energy; SVR: Support Vector Regression; TBM: Tunnel-Boring Machine; TF: Thrust Force; UCS: Uniaxial Compressive Strength; WZ: Weathering Zone; α: Planes Of weakness.
Behrooz Keshtegar; Mahdi Hasanipanah; Troung Nguyen-Thoi; Saffet Yagiz; Hassan Bakhshandeh Amnieh. Potential efficacy and application of a new statistical meta based-model to predict TBM performance. International Journal of Mining, Reclamation and Environment 2021, 35, 471 -487.
AMA StyleBehrooz Keshtegar, Mahdi Hasanipanah, Troung Nguyen-Thoi, Saffet Yagiz, Hassan Bakhshandeh Amnieh. Potential efficacy and application of a new statistical meta based-model to predict TBM performance. International Journal of Mining, Reclamation and Environment. 2021; 35 (7):471-487.
Chicago/Turabian StyleBehrooz Keshtegar; Mahdi Hasanipanah; Troung Nguyen-Thoi; Saffet Yagiz; Hassan Bakhshandeh Amnieh. 2021. "Potential efficacy and application of a new statistical meta based-model to predict TBM performance." International Journal of Mining, Reclamation and Environment 35, no. 7: 471-487.
Buried pipes are extensively used for oil transportation from offshore platforms. Under unfavorable loading combinations, the pipe’s uplift resistance may be exceeded, which may result in excessive deformations and significant disruptions. This paper presents findings from a series of small-scale tests performed on pipes buried in geogrid-reinforced sands, with the measured peak uplift resistance being used to calibrate advanced numerical models employing neural networks. Multilayer perceptron (MLP) and Radial Basis Function (RBF) primary structure types have been used to train two neural network models, which were then further developed using bagging and boosting ensemble techniques. Correlation coefficients in excess of 0.954 between the measured and predicted peak uplift resistance have been achieved. The results show that the design of pipelines can be significantly improved using the proposed novel, reliable and robust soft computing models.
Jie Zeng; Panagiotis G. Asteris; Anna P. Mamou; Ahmed Salih Mohammed; Emmanuil A. Golias; Danial Jahed Armaghani; Koohyar Faizi; Mahdi Hasanipanah. The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand. Applied Sciences 2021, 11, 908 .
AMA StyleJie Zeng, Panagiotis G. Asteris, Anna P. Mamou, Ahmed Salih Mohammed, Emmanuil A. Golias, Danial Jahed Armaghani, Koohyar Faizi, Mahdi Hasanipanah. The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand. Applied Sciences. 2021; 11 (3):908.
Chicago/Turabian StyleJie Zeng; Panagiotis G. Asteris; Anna P. Mamou; Ahmed Salih Mohammed; Emmanuil A. Golias; Danial Jahed Armaghani; Koohyar Faizi; Mahdi Hasanipanah. 2021. "The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand." Applied Sciences 11, no. 3: 908.
In open-pit mines, the blast-induced flyrock is one of the most fundamental problems, therefore, a precision prediction of flyrock can be useful to design a proper blast pattern and reduce the undesirable effects of flyrock. The aim of this study is to develop a new integrated intelligent model to approximate flyrock based on an adaptive neuro-fuzzy inference system (ANFIS) in combination with a grasshopper optimization algorithm (GOA). In addition, a cultural algorithm (CA) is combined with ANFIS to predict flyrock. In the proposed models, the hyperparameters of ANFIS were tuned using CA and GOA. To achieve the objective of this study, a comprehensive database collected from three quarry sites, located in Malaysia, was used. The performance of both ANFIS-CA and ANFIS-GOA models was evaluated by calculation of the statistical functions such as the correlation of determination (R2). The comparison between the proposed models indicated the higher accuracy of using ANFIS-GOA (R2 = 0.974) as an efficient model to predict flyrock compared to the ANFIS-CA (R2 = 0.953).
Hadi Fattahi; Mahdi Hasanipanah. An integrated approach of ANFIS-grasshopper optimization algorithm to approximate flyrock distance in mine blasting. Engineering with Computers 2021, 1 -13.
AMA StyleHadi Fattahi, Mahdi Hasanipanah. An integrated approach of ANFIS-grasshopper optimization algorithm to approximate flyrock distance in mine blasting. Engineering with Computers. 2021; ():1-13.
Chicago/Turabian StyleHadi Fattahi; Mahdi Hasanipanah. 2021. "An integrated approach of ANFIS-grasshopper optimization algorithm to approximate flyrock distance in mine blasting." Engineering with Computers , no. : 1-13.
Vibrations induced by traffic are of concern for the slope stability of the open-pit mine. Different solutions to mitigate this phenomenon are under investigation. In the field of pavement engineering, the so-called antivibration paving technologies are under investigation in order to avoid the generation of excessive vibration and contains propagation. To more fully examine the effectiveness and potential of the antivibration pavement in the application of vibration absorbing for the open-pit mines, numerical simulations based on a two-dimensional (2D) finite element (FE) model were conducted. Sensitivity analysis of varying monitored points and varying loads are performed. Several important parameters such as the damping layer position and thickness and damping ratio are evaluated as well. By using this FE simulation to model the vibration response induced by traffic, the costly construction mistakes and field experimentation can be avoided.
Jiandong Huang; Tianhong Duan; Yawei Lei; Mahdi Hasanipanah. Finite Element Modeling for the Antivibration Pavement Used to Improve the Slope Stability of the Open-Pit Mine. Shock and Vibration 2020, 2020, 1 -11.
AMA StyleJiandong Huang, Tianhong Duan, Yawei Lei, Mahdi Hasanipanah. Finite Element Modeling for the Antivibration Pavement Used to Improve the Slope Stability of the Open-Pit Mine. Shock and Vibration. 2020; 2020 ():1-11.
Chicago/Turabian StyleJiandong Huang; Tianhong Duan; Yawei Lei; Mahdi Hasanipanah. 2020. "Finite Element Modeling for the Antivibration Pavement Used to Improve the Slope Stability of the Open-Pit Mine." Shock and Vibration 2020, no. : 1-11.
The main focus of the present work is to offer an auto-tuning model, called cat swarm optimization (CSO), to predict rock fragmentation. This population-based method has a stochastic formation involving exploration and exploitation phases. CSO is a robust and powerful meta-heuristic algorithm inspired by the behaviors of cats; it is composed of two search modes: seeking and tracing, which can be joined by mixture ratio parameter. CSO is applied to large-scale optimization problems like rock fragmentation to have good forecasting parameters in D80 formulas (D80 is a common descriptor that evaluates rock fragmentation). To evaluate the efficiency of the proposed CSO model, its obtained results were compared to those of the particle swarm optimization (PSO) algorithm. In the modeling, two forms of CSO and PSO models, including power and linear forms, were developed. The comparative results showed that CSO models outperformed the rival in terms of the task defined. The precision of the proposed models was computed using statistical evaluation criteria. Comparison results concluded that CSO-power model with the root mean square error (RMSE) of 0.847 was more computationally efficient with better predictive ability compared to CSO-linear, PSO-linear and PSO-power models with the RMSE of 1.314, 1.545 and 2.307, respectively. Furthermore, the sensitivity analysis revealed the effect of the stemming parameter upon D80 in comparison with other input parameters.
Jiandong Huang; Panagiotis G. Asteris; Siavash Manafi Khajeh Pasha; Ahmed Salih Mohammed; Mahdi Hasanipanah. A new auto-tuning model for predicting the rock fragmentation: a cat swarm optimization algorithm. Engineering with Computers 2020, 1 -12.
AMA StyleJiandong Huang, Panagiotis G. Asteris, Siavash Manafi Khajeh Pasha, Ahmed Salih Mohammed, Mahdi Hasanipanah. A new auto-tuning model for predicting the rock fragmentation: a cat swarm optimization algorithm. Engineering with Computers. 2020; ():1-12.
Chicago/Turabian StyleJiandong Huang; Panagiotis G. Asteris; Siavash Manafi Khajeh Pasha; Ahmed Salih Mohammed; Mahdi Hasanipanah. 2020. "A new auto-tuning model for predicting the rock fragmentation: a cat swarm optimization algorithm." Engineering with Computers , no. : 1-12.
The uniaxial compressive strength (UCS) is considered as a significant parameter related to rock material in design of geotechnical structures connected to the rock mass. Determining UCS values in laboratory is costly and time consuming, hence, its indirect determination through use of rock index tests is of a great interest and advantage. This study presents a prediction process of the UCS values through the use of three non-destructive tests i.e., p-wave velocity, Schmidt hammer and density. This process was done by developing an intelligent predictive technique namely the group method of data handling (GMDH). Before constructing intelligence system, a series of experimental equations were proposed using three non-destructive tests. The results showed that there is a need to propose new model with taking advantages of all three non-destructive tests results. Then, several GMDH models were built through the use of various parametric studies on the most effective GMDH factors. For comparison purposes, an artificial neural network (ANN) was also modelled to predict rock strength. The obtained results of the ANN and GMDH were assessed based on system error and coefficient of determination values. The results confirmed that the proposed GMDH model is an applicable, powerful, and practical intelligence system that is able to provide an acceptable accuracy level for predicting rock strength.
Diyuan Li; Danial Jahed Armaghani; Jian Zhou; Sai Hin Lai; Mahdi Hasanipanah. A GMDH Predictive Model to Predict Rock Material Strength Using Three Non-destructive Tests. Journal of Nondestructive Evaluation 2020, 39, 1 -14.
AMA StyleDiyuan Li, Danial Jahed Armaghani, Jian Zhou, Sai Hin Lai, Mahdi Hasanipanah. A GMDH Predictive Model to Predict Rock Material Strength Using Three Non-destructive Tests. Journal of Nondestructive Evaluation. 2020; 39 (4):1-14.
Chicago/Turabian StyleDiyuan Li; Danial Jahed Armaghani; Jian Zhou; Sai Hin Lai; Mahdi Hasanipanah. 2020. "A GMDH Predictive Model to Predict Rock Material Strength Using Three Non-destructive Tests." Journal of Nondestructive Evaluation 39, no. 4: 1-14.
Prediction of ground vibration induced by blasting operations is a crucial challenge to engineers working in surface mines. This study aims to assess the efficiency of two advanced machine learning models in predicting ground vibrations in a granite quarry located in Malaysia. To this end, two intelligent models were proposed by hybridizing the relevance vector regression (RVR) with the grey wolf optimization (GWO) (which formed the RVR-GWO model) and with the bat-inspired algorithm (BA) (which formed the RVR-BA model). To the best of our knowledge, this is the first attempt to predict ground vibration using the RVR-GWO and RVR-BA models. The afore-mentioned models were developed and tested using 95 datasets. Then, the performance of the developed models was statistically checked through four comparative experiments using, among others, mean square error (MSE) and correlation coefficient (R). The results indicated the superiority of the RVR-GWO model over the RVR-BA model in terms of prediction precision. The RVR-GWO model with R of 0.915 and MSE = 7.920 predicted the ground vibration better than the RVR-BA model with R of 0.867 and MSE = 8.551. Accordingly, it was concluded that applying the GWO algorithm to RVR can result in high accuracy in the prediction of blast-induced ground vibration.
Hadi Fattahi; Mahdi Hasanipanah. Prediction of Blast-Induced Ground Vibration in a Mine Using Relevance Vector Regression Optimized by Metaheuristic Algorithms. Natural Resources Research 2020, 30, 1849 -1863.
AMA StyleHadi Fattahi, Mahdi Hasanipanah. Prediction of Blast-Induced Ground Vibration in a Mine Using Relevance Vector Regression Optimized by Metaheuristic Algorithms. Natural Resources Research. 2020; 30 (2):1849-1863.
Chicago/Turabian StyleHadi Fattahi; Mahdi Hasanipanah. 2020. "Prediction of Blast-Induced Ground Vibration in a Mine Using Relevance Vector Regression Optimized by Metaheuristic Algorithms." Natural Resources Research 30, no. 2: 1849-1863.
Making a relation between strains and stresses is an important subject in the rock engineering field. Shear behaviors of rock fractures have been extensively investigated by different researchers. Literature mostly consists of constitutive models in the form of empirical functions that represent experimental data using mathematical regression techniques. As an alternative, this study aims to present a new integrated intelligent computing paradigm to form a constitutive model applicable to rock fractures. To this end, an RBFNN-GWO model is presented, which integrates the radial basis function neural network (RBFNN) with grey wolf optimization (GWO). In the proposed model, the hyperparameters and weights of RBFNN were tuned using the GWO algorithm. The efficiency of the designed RBFNN-GWO was examined comparing it with the RBFNN-GA model (a combination of RBFNN and the Genetic Algorithm). The proposed models were trained based on the results of a systematic set of 84 direct shear tests gathered from the literature. The finding of the current study demonstrated the efficiency of both the RBFNN-GA and RBFNN-GWO models in predicting the dilation angle, peak shear displacement, and stress as the rock fracture properties. Among the two models proposed in this study, the statistical results revealed the superiority of RBFNN-GWO over RBFNN-GA in terms of prediction accuracy.
Kang Peng; Menad Nait Amar; Hocine Ouaer; Mohammad Reza Motahari; Mahdi Hasanipanah. Automated design of a new integrated intelligent computing paradigm for constructing a constitutive model applicable to predicting rock fractures. Engineering with Computers 2020, 1 -12.
AMA StyleKang Peng, Menad Nait Amar, Hocine Ouaer, Mohammad Reza Motahari, Mahdi Hasanipanah. Automated design of a new integrated intelligent computing paradigm for constructing a constitutive model applicable to predicting rock fractures. Engineering with Computers. 2020; ():1-12.
Chicago/Turabian StyleKang Peng; Menad Nait Amar; Hocine Ouaer; Mohammad Reza Motahari; Mahdi Hasanipanah. 2020. "Automated design of a new integrated intelligent computing paradigm for constructing a constitutive model applicable to predicting rock fractures." Engineering with Computers , no. : 1-12.
The geomechanical properties of rock, including shear strength (SS) and uniaxial compressive strength (UCS), are very important parameters in designing rock structures. To improve the accuracy of SS and UCS prediction, this study presented an evolving support vector regression (SVR) using Grey Wolf optimization (GWO). To examine the feasibility and applicability of the SVR-GWO model, the differential evolution (DE) and artificial bee colony (ABC) algorithms were also used. In other words, the SVR hyperparameters were tuned using the GWO, DE, and ABC algorithms. To implement the proposed models, a comprehensive database accessible in an open-source was used in this study. Finally, the comparative experiments such as root mean square error (RMSE) were conducted to show the superiority of the proposed models. The SVR-GWO model predicted the SS and UCS with RMSE of 0.460 and 3.208, respectively, while, the SVR-DE model predicted the SS and UCS with RMSE of 0.542 and 5.4, respectively. Furthermore, the SVR-ABC model predicted the SS and UCS with RMSE of 0.855 and 5.033, respectively. The aforementioned results clearly exhibited the applicability as well as the usability of the proposed SVR-GWO model in the prediction of both SS and UCS parameters. Accordingly, the SVR-GWO model can be also applied to solving various complex systems, especially in geotechnical and mining fields.
Chuanhua Xu; Menad Nait Amar; Mohammed Abdelfetah Ghriga; Hocine Ouaer; Xiliang Zhang; Mahdi Hasanipanah. Evolving support vector regression using Grey Wolf optimization; forecasting the geomechanical properties of rock. Engineering with Computers 2020, 1 -15.
AMA StyleChuanhua Xu, Menad Nait Amar, Mohammed Abdelfetah Ghriga, Hocine Ouaer, Xiliang Zhang, Mahdi Hasanipanah. Evolving support vector regression using Grey Wolf optimization; forecasting the geomechanical properties of rock. Engineering with Computers. 2020; ():1-15.
Chicago/Turabian StyleChuanhua Xu; Menad Nait Amar; Mohammed Abdelfetah Ghriga; Hocine Ouaer; Xiliang Zhang; Mahdi Hasanipanah. 2020. "Evolving support vector regression using Grey Wolf optimization; forecasting the geomechanical properties of rock." Engineering with Computers , no. : 1-15.
One of the most basic topics in rock mechanic is the shear strength criteria for rock joints. Thus, it is of high importance to accurately predict the shear strength of rock joints. In this study, the abilities for accuracy and agreement of Kriging model-based nonlinear interpolation strategy are investigated in terms of predicting the shear strength of rock joints. Totally 84 datasets were used to construct the Kriging models; the datasets were divided into two main parts: training and testing. The prepared database was applied to the training phase in the Kriging model; this way, several nonlinear basic functions were introduced to enhance the predictions of the Kriging model. The examined functions in this paper were linear, 2-order, 3-order, exponential, logarithmic, logistic, hyperbolic tangent, and hyperbolic sine. The sigmoid forms of the basic functions, including logistic and hyperbolic tangent, provide the superior predictions compared to other mathematical functions, while the 2-order and 3-order forms provide the worst performances than the linear, exponential, and logarithmic functions. According to the obtained results, the logistic-based model with coefficient of determination (R2) of 0.916 was found the optimal model that can be successfully applied to estimating the shear strength of rock joints.
Mahdi Hasanipanah; Debiao Meng; Behrooz Keshtegar; Nguyen-Thoi Trung; Duc-Kien Thai. Nonlinear models based on enhanced Kriging interpolation for prediction of rock joint shear strength. Neural Computing and Applications 2020, 33, 4205 -4215.
AMA StyleMahdi Hasanipanah, Debiao Meng, Behrooz Keshtegar, Nguyen-Thoi Trung, Duc-Kien Thai. Nonlinear models based on enhanced Kriging interpolation for prediction of rock joint shear strength. Neural Computing and Applications. 2020; 33 (9):4205-4215.
Chicago/Turabian StyleMahdi Hasanipanah; Debiao Meng; Behrooz Keshtegar; Nguyen-Thoi Trung; Duc-Kien Thai. 2020. "Nonlinear models based on enhanced Kriging interpolation for prediction of rock joint shear strength." Neural Computing and Applications 33, no. 9: 4205-4215.
Blasting is the cheapest and most common method of rock excavation. The basic purpose of blasting is to breakage and displacement of rock mass and, on the other hand, it has some undesirable and inevitable effects such as flyrock. In this study, a novel hybrid artificial neural network (ANN) based on the adaptive musical inspired optimization method is proposed for accurate prediction of blast-induced flyrock. The dynamical adjusting process was adaptively introduced to enhance the ability of harmony search algorithm to obtain the optimum relationship between input variables, i.e., spacing, burden, stemming, powder factor and density of rock and output variable, i.e., flyrock. Two adjusting processes were used to update the new position of particles. The statistical information of the harmony memory was implemented in the proposed hybrid ANN coupled with adaptive dynamical harmony search (ANN-ADHS). The capacity for agreement, tendency, and accuracy of the proposed ANN-ADHS was compared with that of the ANN and two hybrid ANN models coupled by harmony search (ANN-HS) and particle swarm optimization (ANN-PSO) models using comparative statistics such as root mean square error (RMSE). The results confirmed viability and effectiveness of the ANN-ADHS model (with RMSE = 17.871 m and correlation coefficient (R2) = 0.929) and showed its capacity for better predictive performance compared to ANN-HS (with RMSE = 22.362 m and R2= 0.871), ANN-PSO (with RMSE = 24.286 m and R2= 0.832), and ANN (with RMSE = 24.319 m and R2= 0.831).
Mahdi Hasanipanah; Behrooz Keshtegar; Duc-Kien Thai; Nguyen-Thoi Troung. An ANN-adaptive dynamical harmony search algorithm to approximate the flyrock resulting from blasting. Engineering with Computers 2020, 1 -13.
AMA StyleMahdi Hasanipanah, Behrooz Keshtegar, Duc-Kien Thai, Nguyen-Thoi Troung. An ANN-adaptive dynamical harmony search algorithm to approximate the flyrock resulting from blasting. Engineering with Computers. 2020; ():1-13.
Chicago/Turabian StyleMahdi Hasanipanah; Behrooz Keshtegar; Duc-Kien Thai; Nguyen-Thoi Troung. 2020. "An ANN-adaptive dynamical harmony search algorithm to approximate the flyrock resulting from blasting." Engineering with Computers , no. : 1-13.
Air overpressure (AOp) induced by rock blasting is an undesirable phenomenon in open-pit mines and civil construction works. The prediction of AOp has been always a complicated task since many parameters have potential to affect the propagation of air waves. This study aims to assess the capability of a new hybrid evolutionary model based on an integrated adaptive neuro-fuzzy inference system (ANFIS) with a stochastic fractal search (SFS) algorithm. To assess the reliability and acceptability of ANFIS-SFS model, the particle swarm optimization (PSO) and genetic algorithm (GA) were also combined with ANFIS. The proposed models were developed using a comprehensive database including 62 sets of data collected from four granite quarry sites in Malaysia. Performances of the ANFIS-SFS, ANFIS-GA, and ANFIS-PSO models were checked using statistical functions as the performance criteria. The obtained results showed that the proposed ANFIS-SFS model, with root mean square error of 1.223 dB, provided much higher generalization capacity than the ANFIS-PSO (RMSE of 1.939 dB), ANFIS-GA (RMSE of 2.418 dB), and ANFIS (RMSE of 3.403 dB) models in terms of predicting AOp. This clearly demonstrates the effectiveness of SFS to provide a more accurate model in the AOp prediction field.
Jinbi Ye; Juhriyansyah Dalle; Ramin Nezami; Mahdi Hasanipanah; Danial Jahed Armaghani. Stochastic fractal search-tuned ANFIS model to predict blast-induced air overpressure. Engineering with Computers 2020, 1 -15.
AMA StyleJinbi Ye, Juhriyansyah Dalle, Ramin Nezami, Mahdi Hasanipanah, Danial Jahed Armaghani. Stochastic fractal search-tuned ANFIS model to predict blast-induced air overpressure. Engineering with Computers. 2020; ():1-15.
Chicago/Turabian StyleJinbi Ye; Juhriyansyah Dalle; Ramin Nezami; Mahdi Hasanipanah; Danial Jahed Armaghani. 2020. "Stochastic fractal search-tuned ANFIS model to predict blast-induced air overpressure." Engineering with Computers , no. : 1-15.
Effective prediction of the peak shear strength (PSS) is of crucial importance in evaluating the stability of a rock slope with interlayered rocks and has both theoretical and practical significance. This paper offers two novel prediction tools for the PSS prediction based on radial basis function neural network (RBFNN) and meta-heuristic computing paradigms. For this work, the gray wolf optimization (GWO) and ant colony optimization (ACO) algorithms were used to select the optimal parameters of RBFNN. Then, these two new models were compared with the gene expression programming (GEP) model. A total of 158 experimental data were used to train and test the proposed models using three input parameters, i.e., normal stress, compressive strength ratio of joint walls, and joint roughness coefficient. Finally, the computational result revealed that the RBFNN-GWO model, with the coefficient of determination (R2) of 0.997, produced a better convergence speed and higher accuracy compared with RBFNN-ACO and GEP models, with the R2 of 0.995 and 0.996, respectively. The RBFNN-GWO model was found an efficient predictive tool that can help rock engineers in the slopes design processes.
Juncheng Gao; Menad Nait Amar; Mohammad Reza Motahari; Mahdi Hasanipanah; Danial Jahed Armaghani. Two novel combined systems for predicting the peak shear strength using RBFNN and meta-heuristic computing paradigms. Engineering with Computers 2020, 1 -12.
AMA StyleJuncheng Gao, Menad Nait Amar, Mohammad Reza Motahari, Mahdi Hasanipanah, Danial Jahed Armaghani. Two novel combined systems for predicting the peak shear strength using RBFNN and meta-heuristic computing paradigms. Engineering with Computers. 2020; ():1-12.
Chicago/Turabian StyleJuncheng Gao; Menad Nait Amar; Mohammad Reza Motahari; Mahdi Hasanipanah; Danial Jahed Armaghani. 2020. "Two novel combined systems for predicting the peak shear strength using RBFNN and meta-heuristic computing paradigms." Engineering with Computers , no. : 1-12.
Flyrock is one of the most important environmental and hazardous issues in mine blasting, which can affect equipment and people, and may lead to fatal accidents. Therefore, prediction and minimization of this phenomenon are crucial objectives of many rock removal projects. This study is aimed to predict the flyrock distance with the use of machine learning techniques. The most effective parameters of flyrock were measured during blasting operations in six mines. In total, 262 data samples of blasting operations were accurately measured and used for approximation purposes. Then, flyrock was evaluated and estimated using three machine learning methods: principle component regression (PCR), support vector regression (SVR), and multivariate adaptive regression splines (MARS). Many models of PCR, SVR, and MARS were constructed for the flyrock distance prediction. The modeling process of each method is elaborated separately in a way to be used by other researchers. The most important parameters affecting these models were assessed to obtain the best performance for the developed models. Eventually, a preferable model of each machine learning technique was used for comparison purposes. According to the used performance indices, coefficient of determination (R2), and root mean square error, the SVR model showed a better performance capacity in predicting flyrock distance compared with the other proposed models. Thus, the SVR prediction model can be used to accurately predict flyrock distance, thereby properly determining the blast safety area. Additionally, the SVR model was optimized by new optimization algorithm namely gray wolf optimization (GWO) for minimizing the flyrock resulting from blasting operation. By developing optimization technique of GWO, the value of flyrock can be decreased 4% compared with the minimum flyrock distance.
Danial Jahed Armaghani; Mohammadreza Koopialipoor; Maziyar Bahri; Mahdi Hasanipanah; M. M. Tahir. A SVR-GWO technique to minimize flyrock distance resulting from blasting. Bulletin of Engineering Geology and the Environment 2020, 79, 1 -17.
AMA StyleDanial Jahed Armaghani, Mohammadreza Koopialipoor, Maziyar Bahri, Mahdi Hasanipanah, M. M. Tahir. A SVR-GWO technique to minimize flyrock distance resulting from blasting. Bulletin of Engineering Geology and the Environment. 2020; 79 (8):1-17.
Chicago/Turabian StyleDanial Jahed Armaghani; Mohammadreza Koopialipoor; Maziyar Bahri; Mahdi Hasanipanah; M. M. Tahir. 2020. "A SVR-GWO technique to minimize flyrock distance resulting from blasting." Bulletin of Engineering Geology and the Environment 79, no. 8: 1-17.
Tensile strength (TS) of rock is one of the important properties in design process of construction civil works such as foundations and tunnels. Brazilian tensile strength (BTS) or splitting test is considered as a well-known method in evaluating TS. The present study attempts to propose a novel metaheuristic approach for the indirect measurement of BTS. This new approach is based on the firefly algorithm (FA) for training and optimizing the consequent parameters of the adaptive neuro-fuzzy inference system (ANFIS). The rock samples collected from a tunnel in Malaysia were tested in the laboratory for the purpose of providing a database consisting of totally 80 samples for analysis. Then, the statistical metrics such as R-square (R2) were used to examine the acceptability of the proposed ANFIS-FA and ANFIS models. Finally, it was concluded that the ANFIS-FA (with R2 of 0.982) can be effectively used as a robust model to predict BTS.
Mahdi Hasanipanah; Wengang Zhang; Danial Jahed Armaghani; Hima Nikafshan Rad. The Potential Application of a New Intelligent Based Approach in Predicting the Tensile Strength of Rock. IEEE Access 2020, 8, 57148 -57157.
AMA StyleMahdi Hasanipanah, Wengang Zhang, Danial Jahed Armaghani, Hima Nikafshan Rad. The Potential Application of a New Intelligent Based Approach in Predicting the Tensile Strength of Rock. IEEE Access. 2020; 8 (99):57148-57157.
Chicago/Turabian StyleMahdi Hasanipanah; Wengang Zhang; Danial Jahed Armaghani; Hima Nikafshan Rad. 2020. "The Potential Application of a New Intelligent Based Approach in Predicting the Tensile Strength of Rock." IEEE Access 8, no. 99: 57148-57157.