In the study of toxicology, animal experiments have given the toxicity of nitrobenzene compounds. Similarly, in the environment of nitrobenzene has potential harm to human body. In order to further study the character...
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In the study of toxicology, animal experiments have given the toxicity of nitrobenzene compounds. Similarly, in the environment of nitrobenzene has potential harm to human body. In order to further study the characteristics of nitrobenzene,according to its physical and chemical properties, through the different method to establish QSAR model to predict the toxicity. In our study, a nonlinear quantitative structure–activity relationship model for the prediction of the LC50 of nitrobenzene was developed by the gene expression programming（GEP）. This GEP model is established basis on four descriptors which were selected from the descriptor list by heuristic method（HM）. A nonlinear, four-descriptor model based on GEP with mean-square error 0.065 and a correlation coefficient（R） 0.906 for the training set, mean-square error 0.043 and a correlation coefficient（R） 0.889 for the test set. The results indicate that this QSAR model has stable predictive capability of nitrobenzene toxicity and it provides a new method for toxicology research. The QSAR used in toxicology, will greatly reduce the harm of experimental animals.
Five different tool steels (DIN 1.2080, 1.2210, 1.2344, 1.2510 and 1.3343) have been targeted for a duplex surface treatment consisted of nitriding followed by vanadium thermo-reactive diffusion (IRD). TRD"proces...
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Five different tool steels (DIN 1.2080, 1.2210, 1.2344, 1.2510 and 1.3343) have been targeted for a duplex surface treatment consisted of nitriding followed by vanadium thermo-reactive diffusion (IRD). TRD"process was performed in molten salt bath at 575, 650 and 725 degrees C for 1 to 15 h. A duplex ceramic coating of vanadium carbonitride (VCN) with a thickness up to 10.2 mu m was formed on tool steel substrates. Characterization of the ceramic coating by means of scanning electron microscopy (SEM) and X-ray diffraction analysis (XRD) indicated that the diffused compact and dense layers mainly consisted of V(C,N) and V-2(C,N) phases. Layer thickness of duplex coating has been modeled by gene expression programming (GEP). Recently, application of GEP as a computer-aided technique has got appreciable attraction especially for modeling and to formulate engineering demands. For GEP approaches, chemical composition of steel substrates along with different bath and processing parameters totally composed of 17 different parameters were considered as inputs to establish mathematical correlations. Finally, the training and testing results in models have shown strong potential for predicting the layer thickness of duplex treated ceramic coating on tool steels. (C) 2013 Elsevier Ltd and Techna Group S.r.l. All rights reserved.
Due to the heterogeneous nature of granular soils and the involvement of many effective parameters in the geotechnical behavior of soil-foundation systems, the accurate prediction of shallow foundation settlements on ...
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Due to the heterogeneous nature of granular soils and the involvement of many effective parameters in the geotechnical behavior of soil-foundation systems, the accurate prediction of shallow foundation settlements on cohesionless soils is a complex engineering problem. In this study, three new evolutionary-based techniques, including evolutionary polynomial regression (EPR), classical genetic programming (GP), and gene expression programming (GEP), are utilized to obtain more accurate predictive settlement models. The models are developed using a large databank of standard penetration test (SPT)-based case histories. The values obtained from the new models are compared with those of the most precise models that have been previously proposed by researchers. The results show that the new EPR and GP-based models are able to predict the foundation settlement on cohesionless soils under the described conditions with R-2 values higher than 87%. The artificial neural networks (ANNs) and genetic programming (GP)-based models obtained from the literature, have R-2 values of about 85% and 83%, respectively which are higher than 80% for the GEP-based model. A subsequent comprehensive parametric study is further carried out to evaluate the sensitivity of the foundation settlement to the effective input parameters. The comparison results prove that the new EPR and GP-based models are the most accurate models. In this study, the feasibility of the EPR, GP and GEP approaches in finding solutions for highly nonlinear problems such as settlement of shallow foundations on granular soils is also clearly illustrated. The developed models are quite simple and straightforward and can be used reliably for routine design practice.
Side-weirs have been widely used in hydraulic and environmental engineering applications. Side-weir is known as a lateral intake structure, which are significant parts of the distribution channel in irrigation, land d...
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Side-weirs have been widely used in hydraulic and environmental engineering applications. Side-weir is known as a lateral intake structure, which are significant parts of the distribution channel in irrigation, land drainage, and urban sewerage system, by flow diversion device. Local scour involves the removal of material around piers, abutments, side-weir, spurs, and embankments. Clearwater scour depth based on five dimensional parameters: approach flow velocity (V-1/V-c), water head ratio (h(1)-p)/h(1), side-weir length (L/r), side-weir crest height (b/p) and angle of bend.. The aim of this study is to develop a new formulation for prediction of clear-water scour of side-weir intersection along curved channel using gene expression programming (GEP) which is an algorithm based on genetic algorithms (GA) and genetic programming (GP). In addition, the explicit formulations of the developed GEP models are presented. Also equations are obtained using multiple linear regressions (MLR) and multiple nonlinear regressions (MNRL). The performance of GEP is found more influential than multiple linear regression equation for predicting the clear-water scour depth at side-weir intersection along curved channel. Multiple nonlinear regression equation was quite close to GEP, which serve much simpler model with explicit formulation.
This study presents gene expression programming (GEP), which is an extension to genetic programming (GP), as an alternative approach to modeling the functional relationships for the River Kurau, River Langat, and Rive...
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This study presents gene expression programming (GEP), which is an extension to genetic programming (GP), as an alternative approach to modeling the functional relationships for the River Kurau, River Langat, and River Muda of the Malaysia. A functional relation has been developed using GEP with non-dimensional variables. The development of a GEP non-dimensional model is described. This paper compares current prediction equation with the existing GEP model for the same rivers (Zakaria et al. in Sci Total Environ 408:5078-5085, (2010). The presented model in this study is a less input GEP model and that predicts good performance. The proposed GEP approach gives satisfactory results compared to existing predictors.
Adequate Knowledge of reservoir fluid characteristics (e.g., bubble point pressure) plays a crucial role while conducting modeling/simulation of production processes in petroleum reservoirs. Although many efforts have...
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Adequate Knowledge of reservoir fluid characteristics (e.g., bubble point pressure) plays a crucial role while conducting modeling/simulation of production processes in petroleum reservoirs. Although many efforts have been made to obtain proper correlations for prediction of bubble point pressure (BPP) of reservoir fluids, there is still relatively high magnitude of error with the developed predictive tools available in the literature. To fill this lacuna, a robust and effective technique, called gene expression programming (GEP), is employed to determine BPP of crude oil samples as a function of temperature, oil composition, molecular weight of C7+, and specific gravity of C7+. The GEP method is built based on the experimental (or real) data used for training and testing phases in order to develop an appropriate correlation. The previous predictive methods are also reported in this study and employed to calculate BPP as a function of independent parameters when the same data bank is utilized. Comparing the outputs obtained from the previous models with the BPP values predicted by the GEP technique, it was found that the GEP approach exhibits higher accuracy and lower uncertainty on the basis of statistical analysis in terms of coefficient of determination (R-2) and mean squared error (MSE). Great precision attained in this study through using GEP recommends linking reservoir simulator packages with the GEP tool when thermodynamic properties such as BPP are required for modeling and optimization purposes. (C) 2014 Elsevier B.V. All rights reserved.
In the present work, the performance and emission parameters of a single cylinder four-stroke CRDI engine under CNG-diesel dual-fuel mode have been modelled by gene expression programming. Based on the experimental da...
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In the present work, the performance and emission parameters of a single cylinder four-stroke CRDI engine under CNG-diesel dual-fuel mode have been modelled by gene expression programming. Based on the experimental data, GEP model was developed to predict BSFCeq, BTE, NOx, PM and HC. Load, fuel injection pressure and CNG energy share were chosen as input parameters for the model. The developed GEP model was capable of predicting the performance and emission parameters with commendable accuracy as observed from correlation coefficients within the range of 0.999368-0.999999. Mean absolute percentage error in the range of 0.036-1.09% along with noticeably low root mean square errors provided an acceptable index of the robustness of the predicted accuracy. In addition, the obtained results were also compared with an ANN model developed on the same parametric ranges wherein the GEP model was observed to be superior in predicting the desired response variables. (C) 2014 Elsevier B.V. All rights reserved.
A total dissolved solid (TDS) is an important indicator for water quality assessment. Since the composition of mineral salts and discharge affects the TDS of water, it is important to understand the relationship of mi...
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A total dissolved solid (TDS) is an important indicator for water quality assessment. Since the composition of mineral salts and discharge affects the TDS of water, it is important to understand the relationship of mineral salt composition with TDS. In the present study, four artificial intelligence approaches, namely artificial neural networks (ANNs), two different adaptive-neuro-fuzzy inference system (ANFIS) including ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), and gene expression programming (GEP) were applied to forecast TDS in river water over a period of 18 years at seven different sites. Five different GEP, ANFIS and ANN models comprising various combinations of water quality and flow variables from Zarinehroud basin in northwest of Iran were developed to forecast TDS variations. The correlation coefficient (R), root mean square error and mean absolute error statistics were used for evaluating the accuracy of models. Based on the comparisons, it was found that the GEP, ANFIS-GP, ANFIS-SC and ANN models could be employed successfully in forecasting TDS variations. A comparison was made between these artificial intelligence approaches which emphasized the superiority of GEP over the other intelligent models.
This study presents Artificial Intelligence (AI)-based modeling of total bed material load through developing the accuracy level of the predictions of traditional models. gene expression programming (GEP) and adaptive...
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This study presents Artificial Intelligence (AI)-based modeling of total bed material load through developing the accuracy level of the predictions of traditional models. gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS)-based models were developed and validated for estimations. Sediment data from Qotur River (Northwestern Iran) were used for developing and validation of the applied techniques. In order to assess the applied techniques in relation to traditional models, stream power-based and shear stress-based physical models were also applied in the studied case. The obtained results reveal that developed AI-based models using minimum number of dominant factors, give more accurate results than the other applied models. Nonetheless, it was revealed that k-fold test is a practical but high-cost technique for complete scanning of applied data and avoiding the over-fitting. (C) 2014 Elsevier B.V. All rights reserved.
This paper presents an analysis for the prediction of thrust force in drilling of aluminium-based composites, reinforced with boron-carbide B4C produced with the powder-metallurgy (PM) technique. The formulation was d...
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This paper presents an analysis for the prediction of thrust force in drilling of aluminium-based composites, reinforced with boron-carbide B4C produced with the powder-metallurgy (PM) technique. The formulation was derived on experimental bases. The experiments were conducted with various cutting tools and parameters on conditions of dry machining in a computer numerical control (CNC) vertical machining centre. The thrust forces were obtained by measuring the forces between the drill bit and the work pieces during the experiments. In the experiments, particle fraction, feed rate, spindle speed and drill bit type were used as input parameters, and thrust force was the output data for the gene expression programming (GEP) software. Customizing for formulation in order to describe the problem was generated by GEP, and it was analysed from different perspectives and verified the reliability of equation.
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