A new model based on gene expression programming to estimate air flow in a single rock joint

Khandelwal, Manoj; Armaghani, Danial Jahed; Faradonbeh, Roohollah Shirani; Ranjith, P.G.; Ghoraba, Saber

doi:10.1007/s12665-016-5524-6

Cited by 55 publications

(12 citation statements)

References 44 publications

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“…Table 5 indicates the training and test evaluation of GEP model after 66 oxidation cycles. In a similar approach for validation of PSO-ANN networks, the statistical indicator including root mean square error (RMSE) and an absolute fraction of variance (R 2 ) are utilized for validation of the accuracy GEP models [41]. As shown in Table 4, R 2 values changed in the range of 0.938-0.991 for the training step and 0.931-981 for the testing step.…”

Section: Gep Model Resultsmentioning

confidence: 99%

Modified Particle Swarm Optimization-Artificial Neural Network and Gene Expression Programing for Predicting High Temperature Oxidation Behavior of Ni–Cr–W-Mo Alloys

Hasibi

Mahmoudian

Khayati

2020

IJE

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This paper is an attempt to model the oxidation behavior of Ni-base alloys by considering the alloying elements, i.e., Cr, W, Mo, as variables. Modified particle swarm optimization-artificial neural network (MPSO-ANN) and gene expression programming (GEP) techniques were employed for modeling. Data set for construction of (MPSO-ANN) and GEP models selected from 66 cyclic oxidation performed in the temperature range of 400-1150 ᵒC for 27 different Ni-based alloy samples at various amounts of Cr, W, and Mo. The weight percent of alloying elements selected as input variables and the changes of weight during the oxidation cycle considered as output. To analyze the performance of proposed models, various statistical indices, viz. root mean squared error (RMSE) and the correlation coefficient between two data sets (R 2) were utilized. The collected data of GEP randomly divided into 21 training sets and 6 testing sets. The results confirmed that the possibility of oxidation behavior modeling using GEP by R 2 = 0.981, RMSE =0.0822. By consideration of oxidation resistance as criteria, Cr, Mo, and W enhanced the oxidation resistance of Ni-based alloys. The results showed that in the presence of Cr as alloying element, especially at Cr contents higher than 22 wt.%, the effect of W and Mo were negligible. However, the same trend was reversed at the sample with Cr content lower than 20 wt.%. In these cases, the effect of W and Mo on oxidation resistance were significantly enhanced.

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Section: Gep Model Resultsmentioning

confidence: 99%

Modified Particle Swarm Optimization-Artificial Neural Network and Gene Expression Programing for Predicting High Temperature Oxidation Behavior of Ni–Cr–W-Mo Alloys

Hasibi

Mahmoudian

Khayati

2020

IJE

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“…This method was firstly introduced by Ferreira [78]. Recently, the application of this method has increased in civil and mining engineering [7,67,[79][80][81][82][83][84]. With the use of different mathematical operators and linear functions (e.g., +, −, sin, tan, cos, sqrt, x 2 , and x 3 ), this method forms a mathematical relation between the input and output variables.…”

Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

Practical Risk Assessment of Ground Vibrations Resulting from Blasting, Using Gene Expression Programming and Monte Carlo Simulation Techniques

et al. 2020

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Peak particle velocity (PPV) is a critical parameter for the evaluation of the impact of blasting operations on nearby structures and buildings. Accurate estimation of the amount of PPV resulting from a blasting operation and its comparison with the allowable ranges is an integral part of blasting design. In this study, four quarry sites in Malaysia were considered, and the PPV was simulated using gene expression programming (GEP) and Monte Carlo simulation techniques. Data from 149 blasting operations were gathered, and as a result of this study, a PPV predictive model was developed using GEP to be used in the simulation. In order to ensure that all of the combinations of input variables were considered, 10,000 iterations were performed, considering the correlations among the input variables. The simulation results demonstrate that the minimum and maximum PPV amounts were 1.13 mm/s and 34.58 mm/s, respectively. Two types of sensitivity analyses were performed to determine the sensitivity of the PPV results based on the effective variables. In addition, this study proposes a method specific to the four case studies, and presents an approach which could be readily applied to similar applications with different conditions.

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“…In the planning of gene expression, various operators, such as mutation and combination, are used. The model uses the famous Roulette wheel for selecting individuals [87,88]. The mutation operator is a random regeneration within certain chromosomes.…”

Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate

Afradi

Ebrahimabadi

2020

SN Appl. Sci.

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A new model based on gene expression programming to estimate air flow in a single rock joint

Cited by 55 publications

References 44 publications

Modified Particle Swarm Optimization-Artificial Neural Network and Gene Expression Programing for Predicting High Temperature Oxidation Behavior of Ni–Cr–W-Mo Alloys

Modified Particle Swarm Optimization-Artificial Neural Network and Gene Expression Programing for Predicting High Temperature Oxidation Behavior of Ni–Cr–W-Mo Alloys

Practical Risk Assessment of Ground Vibrations Resulting from Blasting, Using Gene Expression Programming and Monte Carlo Simulation Techniques

Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate

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