Intelligent Prediction of Blasting-Induced Ground Vibration Using ANFIS Optimized by GA and PSO

Yang, Haiqing; Hasanipanah, Mahdi; Tahir, Mahmood Md.; Bui, Dieu Tien

doi:10.1007/s11053-019-09515-3

Cited by 79 publications

(22 citation statements)

References 52 publications

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“…GA is a type of random optimization procedures based on genetics theories. GA is divided into three principal steps, known as elementary population generation, GA operators (selection, crossover, and mutation), and assessment through the adjustment function, which are described more precisely in [ 40 , 41 ]. In GA, the solution is considered as a chromosome.…”

Section: Methodsmentioning

confidence: 99%

Hybrid intelligence methods for modeling the diffusivity of light hydrocarbons in bitumen

Kuyakhi

Zarenia

Boldaji

2020

Heliyon

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The solvent diffusivity is considered as a key factor in the design of solvent assisted processes in the bitumen field. In this study, a novel Adaptive neuro-fuzzy interference system ( ANFIS ) is employed to evaluate the diffusivity of the light hydrocarbons in the bitumen system. The particle swarm optimization ( PSO ) and genetic algorithm ( GA ) are adopted to promote ANFIS efficiency. The proposed models are established by a prepared dataset from multiple papers in the literature. Temperature (T), pressure (P) and molecular weight of alkanes (Mw) were considered as the input variables and on the other hand, Statistical parameters and graphical methods were used to appraise ANFIS , ANFIS-PSO , and ANFIS-GA performance. The results demonstrated that the highest correlation coefficient is related to ANFIS-PSO with R 2 = 0.991 and 0.987 for train and test data, respectively. In the end, the results indicated that the ANFIS-PSO model has a higher level of desirability based on statistical parameters.

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Section: Methodsmentioning

confidence: 99%

Hybrid intelligence methods for modeling the diffusivity of light hydrocarbons in bitumen

Kuyakhi

Zarenia

Boldaji

2020

Heliyon

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“…Ergo there is an extreme necessity to construct such a model that can establish perfect connection between the output and the input factors. The GA integrated hybrid approach such as GA-ANFIS is propounded in this analysis, to scrutinize the impact of process variables on the CO2 fixation rate (Yang et al 2020;Sarkheyli et al 2015). The GA integrated hybrid approach is suitable to develop a better inter-connection between the process parameters and the response values by improvising the performance level of ANFIS model.…”

Section: Introduction 28mentioning

confidence: 99%

Modeling of Carbon Dioxide Fixation Rate by Micro Algae Using Hybrid Artificial Intelligence and Fuzzy Logic Methods and Optimization by Genetic Algorithm

Kushwaha¹,

Uthayakumar²,

Kumaresan³

2021

Preprint

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In this study we are reporting a prediction model for the estimation of carbon dioxide (CO2) fixation based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA) hybrid approach. The experimental parameters such as temperature and pH conditions of the micro-algae-based carbon dioxide uptake process were taken as the input variables and the CO2 fixation rate was taken as the output variable. The optimization of ANFIS parameters and formation of the model structure were performed by genetic algorithm (GA) algorithm in order to achieve optimum prediction capability and industrial applicability. The best-fitting model was figured out using statistical analysis parameters such as RMSE, R2 and AARD. According to the analysis, GA-ANFIS model depicted a superior prediction capability over ANFIS optimized model. The Root Mean Square Error (RMSE), coefficient of determination (R2) and AARD for GA-ANFIS were determined as 0.000431, 0.97865 and 0.044354 in the training phase and 0.00056, 0.98457 and 0.032156 in the testing phase, respectively for the GA-ANFIS Model. As a result, it can be concluded that the proposed GA-ANFIS model is an efficient technique having very high potential to accurately calculate CO2 fixation rate and the exploration of the industrial scale-up process for commercial activities.

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“…Therefore, the ability to make accurate predictions of the ground vibration is a crucial need in this field. As shown in Figure 2, body waves including the compressional P-wave, transverse S-wave, and surface waves including the Love wave (Q-wave) and the Rayleigh wave (R-wave) are generated According to the literature [1][2][3][4] the intensity of ground vibration can be measured based on some descriptors, including the frequency and peak particle velocity (PPV). Among them, PPV is accepted globally and is used in many studies to evaluate the blast-induced ground vibration [5,6].…”

Section: Introductionmentioning

confidence: 99%

Developing a New Computational Intelligence Approach for Approximating the Blast-Induced Ground Vibration

Kumar

Samui

et al. 2020

Applied Sciences

Self Cite

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Ground vibration induced by blasting operations is an important undesirable effect in surface mines and has significant environmental impacts on surrounding areas. Therefore, the precise prediction of blast-induced ground vibration is a challenging task for engineers and for managers. This study explores and evaluates the use of two stochastic metaheuristic algorithms, namely biogeography-based optimization (BBO) and particle swarm optimization (PSO), as well as one deterministic optimization algorithm, namely the DIRECT method, to improve the performance of an artificial neural network (ANN) for predicting the ground vibration. It is worth mentioning this is the first time that BBO-ANN and DIRECT-ANN models have been applied to predict ground vibration. To demonstrate model reliability and effectiveness, a minimax probability machine regression (MPMR), extreme learning machine (ELM), and three well-known empirical methods were also tested. To collect the required datasets, two quarry mines in the Shur river dam region, located in the southwest of Iran, were monitored, and the values of input and output parameters were measured. Five statistical indicators, namely the percentage root mean square error (%RMSE), coefficient of determination (R2), Ratio of RMSE to the standard deviation of the observations (RSR), mean absolute error (MAE), and degree of agreement (d) were taken into account for the model assessment. According to the results, BBO-ANN provided a better generalization capability than the other predictive models. As a conclusion, BBO, as a robust evolutionary algorithm, can be successfully linked to the ANN for better performance.

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Intelligent Prediction of Blasting-Induced Ground Vibration Using ANFIS Optimized by GA and PSO

Cited by 79 publications

References 52 publications

Hybrid intelligence methods for modeling the diffusivity of light hydrocarbons in bitumen

Hybrid intelligence methods for modeling the diffusivity of light hydrocarbons in bitumen

Modeling of Carbon Dioxide Fixation Rate by Micro Algae Using Hybrid Artificial Intelligence and Fuzzy Logic Methods and Optimization by Genetic Algorithm

Developing a New Computational Intelligence Approach for Approximating the Blast-Induced Ground Vibration

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