Application of numerical modeling and genetic programming to estimate rock mass modulus of deformation

Rahmannejad, Reza; Monfared, Amir Ehsan Feili; Ravandi, Ebrahim Ghotbi

doi:10.1016/j.ijmst.2013.08.018

Cited by 26 publications

(5 citation statements)

References 17 publications

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“…The main difference between genetic programming and genetic algorithms is the representation of the solution. Genetic algorithms create a string of numbers that represent the solution but genetic programming creates computer programs (CPs) in the lisp or scheme computer languages as the solution (Ghotbi Ravandi et al 2013). GP can be used to find a relationship between input and output data in the form of mathematical expression represented by functions generated in the training (learning) process.…”

Section: Genetic Programmingmentioning

confidence: 99%

Water Curtain System Pre-design for Crude Oil Storage URCs: A Numerical Modeling and Genetic Programming Approach

et al. 2017

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In this paper the main criteria of the water curtain system for unlined rock caverns (URCs) is described. By the application of numerical modeling and genetic programming (GP), a method for water curtain system pre-design for Iranian crude oil storage URCs (common dimension worldwide) is presented. A comprehensive set of numerical simulations is performed using the finite element based commercial software (COMSOL 5.1) where the results are used as database for genetic programming. Describing equations of water inflow to the filled and empty caverns and water production rate of water curtain boreholes are generated using GP. By equating the proposed equations to each other, water curtain system can be pre-designed. Relative error of the generated GP equations shows their ability and accuracy. Applying a standard regression coefficient method, sensitivity analysis of parameters related to water curtain performance and water inflow to the caverns is performed as well. The results help the design of the water curtain system for crude oil storage caverns worldwide.

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Section: Genetic Programmingmentioning

confidence: 99%

Water Curtain System Pre-design for Crude Oil Storage URCs: A Numerical Modeling and Genetic Programming Approach

et al. 2017

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“…Genetic programming (GP) is a heuristic evolutionary computing technique and one of the machine learning or artificial intelligence techniques. GP was introduced by Koza, based on a tree representation of gens, and the original concept was derived from the GA. More recently, several researches have used tree-based GP modeling in order to ascertain process parameters. − …”

Section: Establishment Of the Genetic Programming Techniquementioning

confidence: 99%

Prediction of the Minimum Spouting Velocity by Genetic Programming Approach

Hosseini

Karami

Olazar

et al. 2014

Ind. Eng. Chem. Res.

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A genetic programming (GP) algorithm is developed to estimate the minimum spouting velocity (U ms) in the spouted beds with a cone base. In order to have a general model, five dimensionless variables including seven critical geometric and operating parameters of spouted beds, namely, column diameter, spout nozzle diameter, base angle, static bed height, particle diameter, particle density, and gas density, have been taken as model inputs. A general correlation including nearly all fundamental and operating variables has been obtained based on the GP approach. The U ms values predicted by the GP are in fair agreement with those obtained by experiments, with a root-mean-square error of 0.1329 m/s. The model results show that GP can be used as an effective tool to provide relatively accurate information on minimum spouting velocity in conical spouted beds.

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“…Due to the complexity of the rock mass, using various in situ test methods for determining the deformation modulus of a rock mass directly in the field is difficult and time-consuming, and the results may not be trustworthy [1,[6][7][8][9][10][11][12][13][14][15][16][17][18]. In addition, the field determination technique for rock mass deformation may include in situ uncertainty and rock surface damage resulting from test blasts [9,[19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Hence, based on the complexity of in situ testing methods, researchers now prefer to use alternate methods in predicting or estimating rock mass deformation compared to in situ methods [35][36][37][38][39][40][41][42][43][44][45][46].…”

Section: Introductionmentioning

confidence: 99%

An Appropriate Model for the Prediction of Rock Mass Deformation Modulus among Various Artificial Intelligence Models

et al. 2022

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The rock mass deformation modulus (Em) is an essential input parameter in numerical modeling for assessing the rock mass behavior required for the sustainable design of engineering structures. The in situ methods for determining this parameter are costly and time consuming. Their results may not be reliable due to the presence of various natures of joints and following difficult field testing procedures. Therefore, it is imperative to predict the rock mass deformation modulus using alternate methods. In this research, four different predictive models were developed, i.e., one statistical model (Muti Linear Regression (MLR)) and three Artificial Intelligence models (Artificial Neural Network (ANN), Random Forest Regression (RFR), and K-Neighbor Network (KNN)) by employing Rock Mass Rating (RMR89) and Point load index (I50) as appropriate input variables selected through correlation matrix analysis among eight different variables to propose an appropriate model for the prediction of Em. The efficacy of each predictive model was evaluated by using four different performance indicators: performance coefficient R2, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Median Absolute Error (MEAE). The results show that the R2, MAE, MSE, and MEAE for the ANN model are 0.999, 0.2343, 0.2873, and 0.0814, respectively, which are better than MLR, KNN, and RFR. Therefore, the ANN model is proposed as the most appropriate model for the prediction of Em. The findings of this research will provide a better understanding and foundation for the professionals working in fields during the prediction of various engineering parameters, especially Em for sustainable engineering design in the rock engineering field.

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Application of numerical modeling and genetic programming to estimate rock mass modulus of deformation

Cited by 26 publications

References 17 publications

Water Curtain System Pre-design for Crude Oil Storage URCs: A Numerical Modeling and Genetic Programming Approach

Water Curtain System Pre-design for Crude Oil Storage URCs: A Numerical Modeling and Genetic Programming Approach

Prediction of the Minimum Spouting Velocity by Genetic Programming Approach

An Appropriate Model for the Prediction of Rock Mass Deformation Modulus among Various Artificial Intelligence Models

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