Rutting is among the most important distresses in flexible pavements, which is mainly influenced by asphalt mixes properties at high temperatures. There are different Methods for measuring the rutting resistance of asphalt mixes. Flow number of asphalt mix, which is measured experimentally by dynamic creep test is one of the most commonly used rutting index, which requires advanced devices, notable cost and time. This paper aims to develop a simple model for predicting the flow number of asphalt mixes using Evolutionary polynomial Regression ((EPR)). The developed model can be used for predicting flow number based on Marshall mix design parameters including percentage of fine and coarse aggregates, bitumen content, filler content, air void content, void in mineral aggregate, Marshall stability, and flow. The coefficient of determination (R2) of model in case of training and testing set is 0. 9714, and 0. 969, respectively, which confirms the high accuracy of model. Comparison of the developed model with the existing models shows the superior performance of the developed model. In addition, the parametric analysis indicates the proper conformity of the developed model with the physical behavior of the asphalt mixtures.

In recent years the rupture of landfill centers has resulted in the importance of studying the behavior of municipal solid waste (MSW). MSW as the main constituent element in landfills has a complicated performance. In this study, by using the results of large – scale direct shear experiments with dimensions of 300 mm x 300 mm x 150 mm, 2 models to predict the behavior of MSW with ages of fresh and 3 months are presented. The purpose of this investigation is to predict MSW stress-strain behavior for kahrizak landfill as a sample of developing countries landfills under aging and by structural models. These models are Hyperbolic model and Evolutionary polynomial Regression ((EPR)). In these collection of experiments, aging process up to 3 months, was artificially applied to samples. Three normal stresses 20, 50, 100 kpa and three shear strain rates 0. 8, 8, 19 mm per minute were used for samples with different ages. The results of these two models, in addition to predict MSW behavior under aging and degradation, shows high accordance with experimental results by direct shear apparatus. Finally this study states the advantage of (EPR) model relative to Hyperbolic model in higher accuracy for all experiments.

A water distribution network is one of the important parts of infrastructure systems. The efficient management and proactive planning of capital investment of these assets are fundamental for efficient and effective service delivered by water companies. The direct economic costs (i.e. rehabilitation investment, repair costs, water loss, etc.) as well as indirect costs (i.e. service and traffic interruptions, etc.) related to water pipe bursts are rapidly increasing. The ability to predict burst rate in pipes is an important strategic key in order to optimization of rehabilitation decision in water distribution systems. Most networks suffer from lack of enough and reliable data for bursts and failures. In this study basic variables which influence on pipes burst and burst statistical analysis have been identified and evaluated. Then common Methods for burst predicting are discussed. In order to identify logical, useful and understandable patterns of breaks data, a data mining Methodology named Evolutionary polynomial Regression ((EPR)) is described. Starting from a hybrid Evolutionary strategy, (EPR) searches for patterns in data and returns symbolic expressions/models. This approach is demonstrated through a detailed case study. Required data were collected from the Mashhad Water Company which includes both asset and bursts data recorded for year 1384. The whole database was divided into 8 material/diameter classes (from 64 mm to 300 mm). The resulting models for burst prediction in different zones contain explicitly recognizable independent variables. The expression models confirm that pipe age, diameter and length are the most important variables leading to pipe bursts. Also the effects of pressure on pipe burst prediction were implicitly investigated. It was found that pressure is an important parameter which influences number of breaks in a pipe network.

This paper presents a new approach, based on Evolutionary polynomial Regression ((EPR)), for predicting the safety factors of a quay wall against sliding, overturning and bearing capacity failure as functions of the soil’s shear strength parameters, geometry of the wall and loading conditions. To this end, a database of around 80000 data sets was created based on a conceptual model, employing a MATLAB-aided program. Based on input and output values of this database and employing (EPR), three different models for the estimation of safety factors were developed and their results were compared with the values in the database. Investigation into the performance of the developed models indicates that these models are capable of estimating the stability of quay walls with a precision of around 95%. Parametric analyses were performed on the models to identify parameters with a key role in the stability of quay walls. The parametric studies were indicative of the models’ ability in capturing the effects of individual parameters on the wall safety factors, therefore proving the models helpful as tools in the preliminary design of gravity quay walls.

Accurate determination of resilience modulus (Mr) of pavement subgrade soil is one of the important factors for successful design of pavement structure. This parameter is usually measured using a dynamic triaxial test, which is a complex and expensive experiment. In this study, the Evolutionary polynomial Regression ((EPR)) Method has been used to develop a model for predicting the resilient modulus of clay subgrade soils based on the results of cone penetration test (CPT). By means of the developed model, the resilient modulus of subgrade soils can be estimated by having the parameters of cone tip resistance (qc), slave friction resistance (fs), moisture content (w) and dry density (γ, d). The results of this study show that the model developed by the exponential function is the best model constructed. Based on the developed model, the coefficient of determination (R2) for training set, testing set and total set was 0. 9808, 0. 9714 and 0. 9785, respectively. The sensitivity analysis performed also showed the very good agreement of the developed model in predicting the resilient modulus of subgrade soil. The results of sensitivity analysis showed that the moisture content is the least important parameter for predicting the resilient modulus of fine-grained soils and the importance of other parameters is almost the same. In this study, the effect of different parameters on the resilient modulus of subgrade soil has also been evaluated using parametric analysis.

1. Introduction In the present study, Evolutionary polynomial Regression ((EPR)) technique is employed to develop a mathematical model to estimate the USC of Full Depth reclaimed (FDR) materials stabilized with Portland cement. To this end, a dataset containing 62 records from experimental studies related to unconfined compressive strength of full-depth reclaimed (FDR) base stabilized with Portland cement were used. Percentage of cement, percentage of RAP, percent passing of #200 sieve, optimum moisture content, and curing time were considered as independent variables. The results show that (EPR) has a great capability for prediction of the UCS in case of FDR base stabilized with Portland cement...

Nowadays, most of the planting machines that are used in Iran, do not have enough planting accuracy or because of having complex systems, require a lot of initial investment for farmers. These tools are offered at a high price and are not suitable for small farms that have low performance. In Iran, the Method of planting chickpeas is mostly done manually, which requires spending a lot of time and money. Thus using such row crop planters will save costs and reduce the duration of planting. In this research, an automatic distribution system of a row crop chickpea planting machine was constructed and evaluated, and a new system was considered for the planter which automatically plant chickpea by relying on the uniform flow of seeds. For this purpose, the indicators including no planting, multiple planting, quality of nutrition and precision in planting distance were considered to investigate the uniformity and accuracy of chickpea planting. Tractor speed at three levels of 3, 5 and 7 km/hr and the distance between seeds at three levels of 5, 10 and 15 cm were used to evaluate the performance of the system. The results showed that the effect of speed on all indicators was significant at the level of 1%. Also, the effect of the distance between seeds on the indicators of multi-planting, nutrition quality and accuracy in planting distance was also significant at the level of 1%, however it had no significant effect on the no-planting index. In none of the indices, the mentioned variables had no mutual effect. Evolutionary polynomial Regression was used to investigate the relationship between constant variables of speed and distance between seeds with distributed functional variables. This Regression model was able to fit the relationship between variables with a high Regression coefficient. According to the measured indices, the uniformity and precision in planting chickpeas with this automatic distributor was desirable.

Tabriz fault is one of the active and dangerous faults in the North West of Iran. The fault crosses the north through the city and put crossing towns at risk. The majority of marginal inhabitants within the city are settled there. Their buildings do not resist the slightest shake. Analysis of seismic data using polynomial Regression for predicting future earthquake's magnitude and risks can be helpful to reduce disaster results. First of all, input data are extracted and normalized. At the second step, model is obtained. In the third step, the magnitude of earthquake for the test data is predicated. In the fourth step, the performance of predicting earthquake magnitude is evaluated.Finally, to estimate the magnitude of earthquakes using other attributes a formula is obtained that can be used to estimate the coefficients of each of the independent variables.