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This research aimed to develop artificial intelligence models, including multivariate adaptive regression splines (MARS), group of method data handling (GMDH), and genetic expression programming(GE...

The CBR (California bearing ratio) value is an important parameter of the subgrade soil required for the design of pavements. The present study deals with the application of genetic expression prog...

The present study aims to apply different methods to investigate effect of previous time of discharge and stage on modeling stage–discharge curve at two hydrometric stations (Faraman and Qorbaghestan stations of Qara Sou River) using measured data-set through the artificial networks (ANNs) and Genetic Expression Programming (GEP) techniques. Subsequently, stage and discharge data of several consecutive water years are applied as models input–output variables. The obtained results indicate that applied machine learning techniques (GEP and ANNs) have reliable performance in modeling stage–discharge curve and can be used instead of other methods. As well as previous time discharge and stage have positive influence on modeling stage–discharge curve.

This research is aimed to develop new practical equations to predict flyrock distance based on genetic programming (GP) and genetic expression programming (GEP) techniques. For this purpose, 97 blasting operations in Delkan iron mine, Iran were investigated and the most effective parameters on flyrock were recorded. A database comprising of five inputs (i.e. burden, spacing, stemming length, hole depth, and powder factor) and one output (flyrock) was prepared to develop flyrock distance. Several GP and GEP models were proposed to predict flyrock considering the modeling procedures of them. To compare the performance prediction of the developed models, coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE) and variance account for (VAF) were computed and then, the best GP and GEP models were selected. According to the obtained results, it was found that the best flyrock predictive model is the GEP based-model. As an example, considering results of RMSE, values of 2.119 and 2.511 for training and testing datasets of GEP model, respectively show higher accuracy of this model in predicting flyrock, while, these values were obtained as 5.788 and 10.062 for GP model.

Summary Study of the energy dissipation over the stepped spillways is necessary in flood control-related studies. The aim of this study is to apply different methods to modeling energy dissipation in nappe and skimming flow regimes over stepped spillway by using original experimental dataset through the artificial networks (ANNs) and Genetic Expression Programming (GEP) techniques. Subsequently, three kinds of data including the napped and skimming regimes data as well as combination of them are applied as models input–output variables. A preliminary investigation on various GEP operators is also carried out for selecting the proper operators. The obtained results indicate that applied machine learning techniques have reliable performance in predicting energy dissipation over stepped spillways.

Artificial intelligence is the area of computer science focusing on creating machines that can engage on behaviors that humans consider intelligent. In the past few years, the applications of artificial intelligence methods have attracted the attention of many investigators. Many artificial intelligence methods have been applied in various areas of civil and environmental engineering. The aim of this study is to develop models to estimate oxygen transfer efficiency in nappe, transition and skimming flow regimes over stepped cascades. For this aim, genetic expression programming, a new member of genetic computing techniques, is used. It is similar, but not equivalent to genetic algorithms, nor genetic programming. For nappe, transition and skimming flow regimes, three models are constructed using the experimental data. The test results indicate that for the model equations obtained, the correlation coefficients are very high and the minimum square error values are less than 0.0033. So, genetic expression programming approach can be successfully used in stepped cascades to predict the oxygen transfer efficiency.

Text corpora which are tagged with part-of-speech (pos) information are useful in many areas of linguistic research. This paper proposes a model of Genetic Expression Programming (GEP) for pos tagging. GEP is used to search for appropriate structures in function space. After the evolution of sequence of tags, GEP can find the best individual as solution. Before simulation, a set of appropriate parameters of algorithm is fitted. Experiments on Brown Corpus show that the proposed model can achieve higher accuracy rate than Genetic Algorithm model and HMM model.

WOS: 000268270600046, Precise determination of the effective angle of shearing resistance (phi') value is a major concern and an essential criterion in the design process of the geotechnical structures, such as foundations, embankments, roads, slopes, excavation and liner systems for the solid waste. The experimental determination of phi' is often very difficult, expensive and requires extreme cautions and labor. Therefore many statistical and numerical modeling techniques have been suggested for the phi' value. However they can only consider no more than one parameter, in a simplified manner and do not provide consistent accurate prediction of the phi' value. This study explores the potential of Genetic Expression Programming, Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy (ANFIS) computing paradigm in the prediction of phi' value of soils. The data from consolidated-drained triaxial tests (CID) conducted in this study and the different project in Turkey and literature were used for training and testing of the models. Four basic physical properties of soils that cover the percentage of fine grained (FG), the percentage of coarse grained (CG), liquid limit (LL) and bulk density (BD) were presented to the models as input parameters. The performance of models was comprehensively evaluated some statistical criteria. The results revealed that GEP model is fairly promising approach for the prediction of angle of shearing resistance of soils. The statistical performance evaluations showed that the GEP model significantly outperforms the ANN and ANFIS models in the sense of training performances and prediction accuracies. (C) 2009 Elsevier Ltd. All rights reserved.