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.

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.

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.