Concrete shear resistance is one of the most important parameters in concrete structures design. Suitable understanding of concrete behavior against shear forces has always been a problem for researchers. Many different methods have been developed for measuring and predicting shear strength. One of the most common method, is to find a relationship between compressive and shear strength. In this study, an attempt was made to provide a practical relationship between compressive and shear strength using an experimental design with compressive strength in the range of 20-30 MPa which resulted in 50 specimens. Gene expression programming (GEP) method has been used to estimate shear resistance and five relationships have been provided. In order to select the best relationship, in addition to considering error measurement parameters such as correlation coefficient, Willmotts index, etc., the criterion of simplicity and easy use of the relation is also considered.

As one of the hazardous pollutants, ozone (O3), has significant adverse effects on urban dwellers' health. Predicting the concentration of ozone in the air can be used to control and prevent unpleasant effects. In this paper, an attempt was made to find out two empirical relationships incorporating multiple linear regression (MLR) and Gene expression programming (GEP) to predict the ozone concentration in the vicinity of Zrenjanin, Serbia. For this purpose, 1564 data sets were collected, each containing 18 input parameters such as concentrations of air pollutants (SO2, CO, H2S, NO, NO2, NOx, PM10, benzene, toluene, m-and p-xylene, o-xylene, ethylbenzene), and meteorological conditions (wind direction, wind speed, air pressure, air temperature, solar radiation, and relative humidity (RH)). In contrast, the output parameter was ozone concentrate. The correlation coefficient and root mean squared error for the MLR were 0. 61 and 21. 28, respectively, while the values for the GEP were 0. 85 and 13. 52, respectively. Also, to evaluate these two methods' validity, a feed-forward artificial neural network (ANN) with an 18-10-5-1 structure has been used to predict the ozone concentration. The correlation coefficient and root mean squared error for the ANN were 0. 78 and 16. 07, respectively. Comparisons of these parameters revealed that the proposed model based on the GEP is more reliable and more reasonable for predicting the ozone concentrate. Also, the sensitivity analysis of the input parameters indicated that the air temperature has the most significant influence on ozone concentration variations.

Today, construction of large structures is growing quickly. For this reason, it is necessary to find the material with a rather low weight and high strength. For this purpose, Steel-Concrete-Steel (SCS) sandwich structures were proposed. SCS structures are composed of two steel layers and one concrete layer. Due to their low weight and high strength and flexibility, they have become popular among engineers. In the present research, first, three specimens of push-out test of strip shear connector were modeled and validated using ABAQUS finite elements software. Then, since the present equations to predict the shear strength of the shear connectors are complicated and are not so precise, the authors proposed an equation taking the effects of different geometrical parameters and the concrete's compressive strength in to account. For this purpose, using the experimental design, 17 specimens were designed and modeled. Then, an equation was proposed using the Genetic expression programming algorithm (GEP) to predict the system's shear strength. Finally, the performance of the proposed equation was evaluated using the error parameters.

Lateral spreading is one of the most significant destructive and catastrophic phenomena associated with liquefaction caused by earthquake and it can cause very serious damage to structures and engineering facilities. The aim of this study is to evaluate liquefaction-induced lateral spreading and  nd new relations using Gene expression programming (GEP), which is a new and developed Generation of Genetic algorithms approaches. Since there are complicated, nonlinear, and higher-order relationships among many factors affecting the lateral spreading, GEP was assumed to be capable of finding complex and accurate relationships among the involved factors. This study includes three main stages: (i) compiling available database (484 data); (ii) dividing data into training and testing categories; and (iii) building new models and proposing new relationships to predict ground displacement in free face, gentle slope, and General ground conditions. The results of modeling each of these different ground conditions were presented in the form of mathematical equations. At the end, the final GEP models for 3 different cases of ground conditions were compared with Multiple Linear Regression (MLR) and other published models. The statistical parameters indicated the higher accuracy of GEP models over other relations.

The linear elastic fracture phenomenon has been characterized with stress intensity factors (SIFs). In this study a General function is obtained in order to predict the fracture parameters. Numerical calculation of the SIFs in a mixed-mode condition is a cumbersome task. In this research, more than 6800 numerical analyses using extended finite element method are conducted to simulate the fracture problem. States are considered for a plate with an arbitrary edge or center crack. Mixed mode SIFs were calculated using of interaction integral. Then, Gene expression programming (GEP) method is utilized to extraction of a function. Results show acceptable correlations between numerical calculations and Genetic programming functions. R-square (R2) values are in a range of 0. 91 to 0. 96 that guarantees the accuracy of the inferred functions.

Groundwater plays a vital role in supplying water demands for different consumptions in dry and semi-dry regions of earth. Iran is considered as an arid and semi-arid region and its groundwater resources have recently shown some significant changes. Owing to the reduction of groundwater resources and recent droughts, simulation of groundwater level variations has significant importance. In some areas of the country of Iran, groundwater levels have been dropped significantly. Therefore, the prediction and simulation of the groundwater level variation are crucially important. In this study, the Gene expression programming (GEP) model was combined with Wavelet Transform (WT) to estimate long-term variations of groundwater level (GWL) in the Sarab-Ghanbar observation well over a 13-years period. Firstly, observation data were divided into two sub-samples, 9 years for training and 4 years for testing. Then, the most effective input lags were identified using the autocorrelation function. Next, four different models for each GEP and WGEP method were developed using the lags. The superior model was identified by analyzing all GEP and WGEP models. The superior GEP model simulated the GWL with acceptable accuracy. For instance, the correlation coefficient and Nash-Sutcliffe efficiency coefficient for the model were calculated at 0. 938 and 0. 851, respectively. A comparison between the GEP and WGEP models showed that the wavelet transforms enhanced the performance of simulation significantly. For example, Variance Accounted For (VAF) index for the best WGEP model was 14 times more than the best GEP model. In addition, the sensitivity analysis indicated that (t-1), (t-2), (t-3) and (t-4) lags were the most influenced input lags.

Canopy temperature (Tc) is one of the essential for irrigation scheduling. Measuring canopy temperature is expensive and time-consuming. Simple approaches such as soft computing can be a good tool for this purpose because there has been no documented research in this field. In this study, the ANN (MLP with two hidden layers) and GEP models were used to estimate Tc using limited data such as the dry (Ta) and wet bulb (TW) temperatures, saturation vapor pressure (es), actual vapor pressure (ea), and the vapor-pressure deficit (VPD). Six combinations of input variables were investigated. The perfect model was selected based on statistical indices during the training and testing. Results showed that the performance of the models were influenced by the number of the input variables. The MLP models outperformed GEP models during the training and testing processes. The MLP7 (input variables: es and ea) with MSE of 1.08 °C, RMSE of 1.04 °C, and R2 of 0.92 in the training phase and MSE of 1.02, RMSE of 1.00, and R2 of 0.95 in the validation phase was selected as the perfect model among MLP models. The GEP11(input variables: Ta, TW, es, ea, and VPD) with MSE of 1.32, RMSE of 1.15, and R2 of 0.89 in the training phase and MSE of 0.91, RMSE of 0.95, and R2 of 0.95 in the validation phase was also the perfect model among GEP models. Accordingly, the proposed GEP and MLP models can be drawn on as a perfect model for estimating TC.