Modeling deformation modulus of a stratified sedimentary rock mass using neural network, fuzzy inference and genetic programming

Alemdağ, Selçuk; Gürocak, Zülfü; Çevik, Abdülkadir; Çabalar, Ali Fırat; Gökçeoğlu, Candan

doi:10.1016/j.enggeo.2015.12.002

Cited by 96 publications

(20 citation statements)

References 40 publications

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“…The main advantage of the GP-based approaches is their ability to generate prediction equations without assuming prior form of the relationship. Many researchers have employed GP and its variants to discover any complex relationships among experimental data [42–44]. Gene expression programming (GEP) [45] is a recent extension to GP.…”

Section: Introductionmentioning

confidence: 99%

Combining gene expression programming and genetic algorithm as a powerful hybrid modeling approach for pear rootstocks tissue culture media formulation

et al. 2019

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BackgroundPredicting impact of plant tissue culture media components on explant proliferation is important especially in commercial scale for optimizing efficient culture media. Previous studies have focused on predicting the impact of media components on explant growth via conventional multi-layer perceptron neural networks (MLPNN) and Multiple Linear Regression (MLR) methods. So, there is an opportunity to find more efficient algorithms such as Radial Basis Function Neural Network (RBFNN) and Gene Expression Programming (GEP). Here, a novel algorithm, i.e. GEP which has not been previously applied in plant tissue culture researches was compared to RBFNN and MLR for the first time. Pear rootstocks (Pyrodwarf and OHF) were used as case studies on predicting the effect of minerals and some hormones in the culture medium on proliferation indices.ResultsGenerally, RBFNN and GEP showed extremely higher performance accuracy than the MLR. Moreover, GEP models as the most accurate models were optimized using genetic algorithm (GA). The improvement was mainly due to the RBFNN and GEP strong estimation capability and their superior tolerance to experimental noises or improbability.ConclusionsGEP as the most robust and accurate prospecting procedure to achieve the highest proliferation quality and quantity has also the benefit of being easy to use.

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Section: Introductionmentioning

confidence: 99%

Combining gene expression programming and genetic algorithm as a powerful hybrid modeling approach for pear rootstocks tissue culture media formulation

et al. 2019

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“…ANN as a learning method is theoretically developed based on the simulation of the biological nervous system. The application of machine learning methods in general and ANN in particular in solving complex nonlinear models in geotechnical-related problems have been reported extensively in the literature [43][44][45]. Multi-layer perceptron (MLP) is one of the common ANN techniques that is used in this study.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Estimation of rocking capacity of soil-structure systems using a hybrid inverse solver

et al. 2019

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Among the geotechnical-earthquake community, the rocking concept is being acknowledged as an energy dissipation mechanism that benefits soil-structure systems during strong vibrations. Nevertheless, several involving factors such as geomaterials behavior and superstructure size can make the rocking analysis of soil-foundation-structure systems complicated. The mobilized moment and the dissipated energy can be represented as the two primary performance indicators of the soil-structure systems under strong motions. This study employs an assembled database comprised of a wide range of geomaterials with different stiffness values associated with high-rise structures with different dimensions acquired from the implementation of a dynamic nonlinear elastic-perfect plastic finite element model. This study aims to develop predictive models for the responses mentioned above using the implemented database. To predict the both mobilized moment and dissipated energy, a hybrid gene-expression programming-artificial neural network technique (GEP-ANN) was used. The results show that the GEP model can yield promising predictions with reasonable accuracy. However, the GEP model can be fine-tuned by introducing the hybrid model. The hybrid model decreases the recorded prediction errors by a factor of three for both the mobilized moment and damping ratio as compared to the GEP model. The results show that the predictive models yield a sensible performance power that minimizes the efforts needed for implementation of time-consuming finite element method. This study also deploys a local sensitivity analysis technique to assess how the input parameters are attributed to the target values.

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“…Fuzzy mathematics has been applied to predict petrophysical rock parameters [18] and mechanical rock parameters [19][20][21][22] and to analyse various properties [2,[23][24][25][26][27] 2 Mathematical Problems in Engineering and phenomena [28,29] of the rock mass. In a rock mass cavability study, Rafiee et al [2] designed a fuzzy expert semiquantitative coding methodology to assess the cavability of the rock mass, and Rafiee et al [7] applied the fuzzy rock engineering systems method to account for the intricate interactions that exist among parameters in real projects.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Comprehensive Assessment Approach and Application of Rock Mass Cavability in Block Caving Mining

Liu

Ren

et al. 2019

Mathematical Problems in Engineering

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Cavability assessment is an important subject during the feasibility stages before determining whether to use block caving mining. This paper provides a fuzzy comprehensive assessment (FCA) approach based on the cavability assessment approaches and its influencing factors, which are all fuzzy. This approach combines the cavability influencing factors with engineering empirical approaches by fuzzy mathematics, which improves the applicability of the cavability assessment results. This approach is applied to assess the cavability via cores in the Luoboling copper molybdenum mine. The spatial distribution of the rock mass cavability at different depths of the borehole is obtained. The cavability ranks of various rocks are determined in different locations. These assessment results can provide a basis for demonstrating the feasibility of block caving mining in the Luoboling copper molybdenum mine. The study can also provide a basis for the design of mining engineering.

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Modeling deformation modulus of a stratified sedimentary rock mass using neural network, fuzzy inference and genetic programming

Cited by 96 publications

References 40 publications

Combining gene expression programming and genetic algorithm as a powerful hybrid modeling approach for pear rootstocks tissue culture media formulation

Combining gene expression programming and genetic algorithm as a powerful hybrid modeling approach for pear rootstocks tissue culture media formulation

Estimation of rocking capacity of soil-structure systems using a hybrid inverse solver

A Fuzzy Comprehensive Assessment Approach and Application of Rock Mass Cavability in Block Caving Mining

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