Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming

Çanakçı, Hanifi; Baykasoğlu, Adil; Güllü, Hamza

doi:10.1007/s00521-008-0208-0

Cited by 94 publications

(20 citation statements)

References 13 publications

(25 reference statements)

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“…Utilization of the GEP algorithm in the field of rock mechanics and mining engineering has only been limited into a few studies. For instance, Baykasoglu et al (2008) andÇ anakcı et al (2009) proposed new models based on GEP for solving problems related to compressive and tensile strength of the rock with high degree of accuracy. Ozbek et al (2013) and Dindarloo and Siami-Irdemoosa (2015) developed GEP models for prediction of the uniaxial compressive strength (UCS) of the rock samples.…”

Section: Introductionmentioning

confidence: 99%

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Faradonbeh

Armaghani

Majid

et al. 2016

Int. J. Environ. Sci. Technol.

146

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Blasting is a widely used technique for rock fragmentation in opencast mines and tunneling projects. Ground vibration is one of the most environmental effects produced by blasting operation. Therefore, the proper prediction of blast-induced ground vibrations is essential to identify safety area of blasting. This paper presents a predictive model based on gene expression programming (GEP) for estimating ground vibration produced by blasting operations conducted in a granite quarry, Malaysia. To achieve this aim, a total number of 102 blasting operations were investigated and relevant blasting parameters were measured. Furthermore, the most influential parameters on ground vibration, i.e., burden-to-spacing ratio, hole depth, stemming, powder factor, maximum charge per delay, and the distance from the blast face were considered and utilized to construct the GEP model. In order to show the capability of GEP model in estimating ground vibration, nonlinear multiple regression (NLMR) technique was also performed using the same datasets. The results demonstrated that the proposed model is able to predict blast-induced ground vibration more accurately than other developed technique. Coefficient of determination values of 0.914 and 0.874 for training and testing datasets of GEP model, respectively show superiority of this model in predicting ground vibration, while these values were obtained as 0.829 and 0.790 for NLMR model.

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Section: Introductionmentioning

confidence: 99%

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Faradonbeh

Armaghani

Majid

et al. 2016

Int. J. Environ. Sci. Technol.

146

View full text Add to dashboard Cite

show abstract

“…1 The tree representation of a GP model (X 1 ? 3/X 2 ) 2 are decoded and expressed like nonlinear entities (trees) [24,30].…”

Section: Genetic Programmingmentioning

confidence: 99%

“…The parameter selection will affect the model generalization capability of GEP. They were selected based on some previously suggested values [24] and also after a trial and error approach.…”

Section: Model Construction Using Gepmentioning

confidence: 99%

“…Another alternative AI-based approach, which is based on the data alone to determine the structure and parameters of the model, is known as genetic programming (GP) [23,24]. GP may generally be defined as a supervised machine learning technique that searches a program space instead of a data space [24].…”

Section: Introductionmentioning

confidence: 99%

“…GP may generally be defined as a supervised machine learning technique that searches a program space instead of a data space [24]. Many researchers have employed GP and its variants to find out any complex relationships between experimental data [24][25][26][27]. Gene expression programming (GEP) [28] is a recent extension to GP that evolves computer programs of different sizes and shapes encoded in linear chromosomes of fixed length.…”

Section: Introductionmentioning

confidence: 99%

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Short-term load forecasting of power systems by gene expression programming

Hosseini

Gandomi

2010

Neural Comput & Applic

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Short-term load forecasting is a popular topic in the electric power industry due to its essentiality in energy system planning and operation. Load forecasting is important in deregulated power systems since an improvement of a few percentages in the prediction accuracy will bring benefits worth of millions of dollars. In this study, a promising variant of genetic programming, namely gene expression programming (GEP), is utilized to improve the accuracy and enhance the robustness of load forecasting results. With the use of the GEP technique, accurate relationships were obtained to correlate the peak and total loads to average, maximum and lowest temperatures of day. The presented model is applied to forecast short-term load using the actual data from a North American electric utility. A multiple least squares regression analysis was performed using the same variables and same data sets to benchmark the GEP models. For more verification, a subsequent parametric study was also carried out. The observed agreement between the predicted and measured peak and total load values indicates that the proposed correlations are capable of effectively forecasting the shortterm load. The GEP-based formulas are relatively short, simple and particularly valuable for providing an analysis tool accessible to practicing engineers.

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A New GEP Algorithm and Its Applications in Vegetable Price Forecasting Modeling Problems

Yang

Zhang

et al. 2016

Communications in Computer and Information Science

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Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming

Abstract: In this paper, two soft computing approaches,

Cited by 94 publications

References 13 publications

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Short-term load forecasting of power systems by gene expression programming

A New GEP Algorithm and Its Applications in Vegetable Price Forecasting Modeling Problems

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