Subsurface soil water retention (SWRT) is a recent technology for increasing the crop yield, water use efficiency and then the water productivity with less amount of applied water. The goal of this research was to evaluate the existing of SWRT with the influence of surface and subsurface trickle irrigation on economic water productivity of cucumber crop. Field study was carried out at the Hawr Rajab district of Baghdad governorate from October 1st, to December 31st, 2017. Three experimental treatments were used, treatment plot T1 using SWRT with subsurface trickle irrigation, plot T2 using SWRT with surface trickle irrigation, while plot T3 without using SWRT and using surface tickle irrigation system. The obtained results showed th

Many researchers tried to prevent or reduce moisture damage and its sensitivity to temperature to improving the performance of hot mix asphalt because it is decreasing the functional and structural life of fixable pavement due to the moisture damage had exposed to it.

The main objective of this study is to inspect the effect of (fly ash “3%, 6%, 12%”, hydrated lime”5%, 10%, 20%” and silica fumes”1%, 2%, 4%) referring to previous research by the net weight asphalt cement as a modified material on the moisture and temperature sensitivity of hot mix asphalt. This was done using asphalt from AL-Nasiria refinery with penetration grade 40-50, nominal maximum size (12.5) mm (surface course) of aggregate and on

The current Iraqi standard specifications for roads and bridges allowed the prepared Job-Mix Formula for asphalt mixtures to witness some tolerances with regard to the following: coarse aggregate gradation by ± 6.0 %, fine aggregate gradation by ± 4.0 %, filler gradation by ± 2.0 %, asphalt cement content by ± 0.3 % and mixing temperature by ± 15 ^oC. The objective of this work is to evaluate the behavior of asphalt mixtures prepared by different aggregates gradations (12.5 mm nominal maximum size) that fabricated by several asphalt contents (40-50 grade) and various mixing temperature. All the tolerances specified in the specifications are taken into account, furthermore, the zones beyond these tolerances

Estimation the unknown parameters of a two-dimensional sinusoidal signal model is an important and a difficult problem , The importance of this model  in modeling Symmetric gray- scale texture image . In this paper, we propose employment Deferential Evaluation algorithm and the use of Sequential approach to estimate the unknown frequencies and amplitudes of the 2-D sinusoidal components when the signal is affected by noise. Numerical simulation are performed for different sample size, and various level of standard deviation to observe the performance of this method in estimate the parameters of 2-D sinusoidal signal model , This model was used for modeling  the Symmetric gray scale texture image and estimating by using

Moisture induced damage can cause a progressive deterioration in the performance of asphalt pavement by the loss of adhesion between asphalt binder and aggregate surface and/or loss of cohesion within the binder in the presence of water. The objective of this paper is to improve the asphalt mixtures resistance to moisture by using hydrated lime as an anti-stripping additive. For this purpose, two types of asphalt binder were utilized; asphalt grades (40-50) and (60-70) with one type of aggregate of 19.0 mm aggregate nominal maximum size, and limestone dust as a mineral filler. Marshall method was adopted to find the optimum asphalt content. Essentially, two parameters were determined to evaluate the moisture susceptibili

In this research, the focus was placed on estimating the parameters of the Hypoexponential distribution function using the maximum likelihood method and genetic algorithm. More than one standard, including MSE, has been adopted for comparison by Using the simulation method

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F