Skilful forecasting of monthly streamflow in intermittent rivers is a challenging task in stochastic hydrology. In this study, genetic algorithm (GA) was combined with gene expression programming (GEP) as a new hybrid...
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Skilful forecasting of monthly streamflow in intermittent rivers is a challenging task in stochastic hydrology. In this study, genetic algorithm (GA) was combined with gene expression programming (GEP) as a new hybrid model for month ahead streamflow forecasting in an intermittent stream. The hybrid model was named GEP-GA in which sub-expression trees of the best evolved GEP model were rescaled by appropriate weighting coefficients through the use of GA optimizer. Auto-correlation and partial auto-correlation functions of the streamflow records as well as evolutionary search of GEP were used to identify the optimum predictors (i.e., number of lags) for the model. The proposed methodology was demonstrated using monthly streamflow data from the Shavir Creek in Iran. Performance of the GEP-GA was compared to that of classic genetic programming (GP), GEP, multiple linear regression and GEP-linear regression models developed in the present study as the benchmarks. The results showed that the GEP-GA outperforms all the benchmarks and motivated to be used in practice.
Water ingress poses a common and intricate geological hazard with profound implications for tunnel construction's speed and safety. The project's success hinges significantly on the precision of estimating wat...
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Water ingress poses a common and intricate geological hazard with profound implications for tunnel construction's speed and safety. The project's success hinges significantly on the precision of estimating water inflow during excavation, a critical factor in early -stage decision -making during conception and design. This article introduces an optimized model employing the gene expression programming (GEP) approach to forecast tunnel water inflow. The GEP model was refined by developing an equation that best aligns with predictive outcomes. The equation's outputs were compared with measured data and assessed against practical scenarios to validate its potential applicability in calculating tunnel water input. The optimized GEP model excelled in forecasting tunnel water inflow, outperforming alternative machine learning algorithms like SVR, GPR, DT, and KNN. This positions the GEP model as a leading choice for accurate and superior predictions. A state-of-the-art machine learning -based graphical user interface (GUI) was innovatively crafted for predicting and visualizing tunnel water inflow. This cutting -edge tool leverages ML algorithms, marking a substantial advancement in tunneling prediction technologies, providing accuracy and accessibility in water inflow projections.
Turbulence in two-phase flows drives many important natural and engineering processes, from geophysical flows to nuclear power generation. Strong interphase coupling between the carrier fluid and disperse phase preclu...
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Turbulence in two-phase flows drives many important natural and engineering processes, from geophysical flows to nuclear power generation. Strong interphase coupling between the carrier fluid and disperse phase precludes the use of classical turbulence models developed for single-phase flows. In recent years, there has been an explosion of machine learning techniques for turbulence closure modeling, though many rely on augmenting existing models. In this work, we propose an approach that blends sparse regression and gene expression programming (GEP) to generate closed-form algebraic models from simulation data. Sparse regression is used to determine a minimum set of functional groups required to capture the physics, and GEP is used to automate the formulation of the coefficients and dependencies on operating conditions. The framework is demonstrated on homogeneous turbulent gas-particle flows in which two-way coupling generates and sustains carrier-phase turbulence.
A novel machine learning method, gene expression programming（GEP）, was employed to build quatitative structure-activity relationship（QSAR） models for predicting the enhancement effect of nitroimidazole compounds o...
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A novel machine learning method, gene expression programming（GEP）, was employed to build quatitative structure-activity relationship（QSAR） models for predicting the enhancement effect of nitroimidazole compounds on irradiation. The models were based on descriptors which were calculated from the molecular structures. Four descriptors were selected from the pool of descriptors by best multiple linear regression（BMLR） method. After that, three regression methods, multiple linear regression（MLR）, support vector machine（SVM） and GEP, were used to build QSAR models. Compared to MLR and SVM, GEP produced a better model with the square of correlation coefficient（R2）, 0.9203 and 0.9014, and the root mean square error（RMSE）, 0.6187 and 0.6875, for training set and test set, respectively. The results show that the GEP model has better predictive ability and more reliable than the MLR and SVM models. This indicates that GEP is a promising method on relevant researches in radiation area.
In this study, a new design equation is derived to predict the shear strength of slender reinforced concrete (RC) beams without stirrups using gene expression programming (GEP). The predictor variables included in the...
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In this study, a new design equation is derived to predict the shear strength of slender reinforced concrete (RC) beams without stirrups using gene expression programming (GEP). The predictor variables included in the analysis are web width, effective depth, concrete compressive strength, amount of longitudinal reinforcement, and shear span to depth ratio. A set of published database containing 1942 experimental test results is used to develop the model. An extra set of test results which is not involved in the modeling process is employed to verify the applicability of the proposed model. Sensitivity and parametric analyses are carried out to determine the contributions of the affecting parameters. The proposed model is effectively capable of estimating the ultimate shear capacity of members without shear steel. The results obtained by GEP are found to be more accurate than those obtained using several building codes. The GEP-based formula is fairly simple and useful for pre-design applications. (C) 2014 Elsevier B.V. All rights reserved.
Prediction of rainfall and runoff is one of the most important issues in managing catchment water resources and sustainable use of water resources. In this study, the accuracy and efficiency of the geneexpression Pro...
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Prediction of rainfall and runoff is one of the most important issues in managing catchment water resources and sustainable use of water resources. In this study, the accuracy and efficiency of the gene expression programming (GEP) model and the Regional Climate Model (RegCM) to predict runoff values from monthly precipitation were investigated. For this purpose, monthly precipitation data of 48 synoptic stations, monthly temperature data of 21 synoptic stations, and also monthly runoff data of 40 hydrometric stations located in the Karkheh basin during 45 years (1972-2017) were used. Out of this statistical period, 40 years was used for calibration, and 5 years (1995-1999) for the validation of the model results. The results showed that the GEP model with an average R-2 value of 0.948, average RMSE value of 19.4 m(3)/s, average NSE value of 0.91, and average SE value of 0.3, had a much more accurate performance than the RegCM model, which had an average R-2 value of 0.04, average RMSE value of 298.2 m(3)/s, average NSE value of -0.64, and average SE value of 4.6 in predicting monthly runoff.
One way to enhance the mechanical properties of nanocomposites has been to use different fillers. In this study, ternary hybrid composites of graphene oxide/hydroxyapatite/epoxy resin were investigated. An experimenta...
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One way to enhance the mechanical properties of nanocomposites has been to use different fillers. In this study, ternary hybrid composites of graphene oxide/hydroxyapatite/epoxy resin were investigated. An experimental design was performed based on the central composite design (CCD). Epoxy resin was modified by incorporating different graphene oxide and hydroxyapatite weight from 0 to 0.5 wt.% and 0 to 7 wt.%, respectively. Experimental results showed that Young's modulus, yield strength and impact strength improved up to 25.64%, 5.95% and 100.05% compared to the neat epoxy resin, respectively. In addition, gene expression programming (GEP), multivariate non-linear regression (MNLR) and fuzzy neural network (FNN) methods were employed to determine the effects of nanoparticles on the mechanical properties. Based on the modelling results, optimization process was investigated by using particle swarm optimization (PSO). Finally, the fracture surface morphologies of the nanocomposites were analyzed by scanning electron microscopy.
In reinforced concrete (RC), concrete's relatively low tensile strength and ductility are counteracted by reinforcement of materials having higher tensile strength or ductility, such as steel reinforcing bars. A r...
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In reinforced concrete (RC), concrete's relatively low tensile strength and ductility are counteracted by reinforcement of materials having higher tensile strength or ductility, such as steel reinforcing bars. A remarkable concern in design of RC columns confined with transverse reinforcement is to achieve an acceptable estimation of confined compressive strength and strain factors when they are subjected to compressive and lateral loading. To cope with this issue, various models have been proposed by researchers due to the costly procedure of experimental studies and lack of high-capacity testing equipment. Despite this fact, there still exists the necessity to develop more robust estimation models. This paper explores the capability of gene expression programming for the prediction of confined compressive strength and strain of RC columns with circular cross section. A reliable database is used to develop two new models which can be used via hand calculations for design purposes. In order to verify and validate the proposed models, several analyses are conducted and the results are compared with those provided by other researchers. Consequently, the results explicitly represent that the proposed models accurately estimate the confined compressive strength and corresponding strain of circular concrete columns and reach a notably better prediction performance than the traditional models.
Uniaxial compressive strength (UCS) is a critical geomechanicalparameter that plays a significant role in the evaluation of rocks. The practice of indirectly estimating said characteristics is widespread due to the ch...
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Uniaxial compressive strength (UCS) is a critical geomechanicalparameter that plays a significant role in the evaluation of rocks. The practice of indirectly estimating said characteristics is widespread due to the challenges associated with obtaining high-quality core samples. The primary aim of this study is to investigate the feasibility of utilizing the gene expression programming (GEP) technique for the purpose of forecasting the UCS for various rock categories, including Schist, Granite, Claystone, Travertine, Sandstone, Slate, Limestone, Marl, and Dolomite, which were sourced from a wide range of quarry sites. The present study utilized a total of 170 datasets, comprising Schmidt hammer (SH), porosity (n), point load index (Is(50)), and P-wave velocity (Vp), as the effective parameters in the model to determine their impact on the UCS. The UCS parameter was computed through the utilization of the GEP model, resulting in the generation of an equation. Subsequently, the efficacy of the GEP model and the resultant equation were assessed using various statistical evaluation metrics to determine their predictive capabilities. The outcomes indicate the prospective capacity of the GEP model and the resultant equation in forecasting the unconfined compressive strength (UCS). The significance of this study lies in its ability to enable geotechnical engineers to make estimations of the UCS of rocks, without the requirement of conducting expensive and time-consuming experimental *** particular, a user-friendly program was developed based on the GEP model to enable rapidand very accurate calculation of rock's UCS, doing away with the necessity for costly and time-consuming laboratory experiments
In the reinforced concrete (RC) columns which are exposed extreme loads such as earthquake effects, the plastic hinge length can be defined as the length of the region where flexural moments exceed the yielding capaci...
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In the reinforced concrete (RC) columns which are exposed extreme loads such as earthquake effects, the plastic hinge length can be defined as the length of the region where flexural moments exceed the yielding capacity, and the plastic deformations are concentrated. More accurate estimation of plastic hinge length increases the reliability of the seismic design. However, a sensitivity prediction of plastic hinge length is difficult due to a large number of model parameters. Therefore, this study aims to predict the plastic hinge length using the gene expression programming (GEP). An experimental database of 133 RC columns gathered from the literature was utilized for prediction with GEP. The results of GEP model are statistically compared with those of 13 models existing in the literature proposed by various researchers. The comparison results reveal that the proposed GEP-based formulation has the best efficiency among all models. Furthermore, a sensitivity analysis and parametric study are conducted to identify the most influential parameters affecting the GEP formulation.
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