Moisture damage is a primary mode of distress occurring in hot mix asphalt (HMA) pavements in Iraq. Because of the loss of bond, or stripping, caused by the presence of moisture between the asphalt and aggregate, which is a problem in some areas and can be severe in some cases, it is requires to evaluate the design asphalt mixture to moisture susceptibility. Many factors such as aggregate characteristics, asphalt characteristics, environment, traffic, construction practices and drainage can contribute to stripping. Asphalt concrete mixes were prepared at their optimum asphalt content by superpave system and then tested to evaluate their engineering properties, which include tensile strength, resilient modulus, and perman

The preparation of the title compound, C₂₆H₂₅N, was achieved by the condensation of an ethanolic mixture of benzaldehyde, cyclohexanone and ammonium acetate in a 2:1:1 molar ratio. There are two crystallographically independent molecules in the asymmetric unit. The two cyclohexyl rings adopt ananti-envelope conformation with the benzyl moiety adopting acisconformation with respect to the nitrogen atom of the phenanthridine segment. In the crystal, molecules are linked through C—H...N interactions into hydrogen-bonded chains that are further arranged into distinct layers by weak offset π–π interactions.

The root lesion nematode, Pratylenchus neglectus, is one of the most damaging nematodes to affect wheat worldwide. The nematode is widely distributed in Montana, primarily affecting winter wheat within the state. Managing the nematode primarily involves rotation to resistant and moderately resistant crops (peas, lentils, and barley). A nematode survey was conducted across the state nearly 10 years after an initial survey, to reassess the nematode threat and assess the impact of changing trends in crop rotations. To assess the broad applicability of rotation crops to control P. neglectus across Montana, greenhouse trials were conducted to challenge rotational crops using eight populations of P. neglectus collected from geographicall

Some structures such as tall buildings, offshore platforms, and bridge bents are subjected to lateral loads of considerable magnitude due to wind and wave actions, ship impacts, or high-speed vehicles. Significant torsional forces can be transferred to the foundation piles by virtue of eccentric lateral loading. The testing program of this study includes one group consists of 3 piles, four percentages of allowable vertical load were used (0%, 25%, 50%, and 100%) with two L/D ratios 20 and 30, vertical allowable load 110 N for L/D = 20 and 156 N for L/D = 30. The results obtained indicate that the torsional capacity for pile group increases with increasing the percentage of allowable vertical load, when the percentage of allowable vertica

A simple straightforward mathematical method has been developed to cluster grid nodes on a boundary segment of an arbitrary geometry that can be fitted by a relevant polynomial. The method of solution is accomplished in two steps. At the first step, the length of the boundary segment is evaluated by using the mean value theorem, then grids are clustered as desired, using relevant linear clustering functions. At the second step, as the coordinates cell nodes have been computed and the incremental distance between each two nodes has been evaluated, the original coordinate of each node is then computed utilizing the same fitted polynomial with the mean value theorem but reversibly.

The method is utilized to predict

Rutting is a crucial concern impacting asphalt concrete pavements’ stability and long-term performance, negatively affecting vehicle drivers’ comfort and safety. This research aims to evaluate the permanent deformation of pavement under different traffic and environmental conditions using an Artificial Neural Network (ANN) prediction model. The model was built based on the outcomes of an experimental uniaxial repeated loading test of 306 cylindrical specimens. Twelve independent variables representing the materials’ properties, mix design parameters, loading settings, and environmental conditions were implemented in the model, resulting in a total of 3214 data points. The network accomplished high prediction accuracy with an R