saleh amin-yavari

Recent advances in polymer science, chemical synthesis, and engineering have increased the importance of polyolefins in structural applications. Polyolefin fibers have good tensile properties, wear resistance, and excellent chemical resistance, slowing the development of crack length and width. Studying the role of polyolefin fibers in two combined forms on the resistance properties of concrete is one of the main goals of this research. The samples were examined at 7 and 28 days for compression, tension, and elastic modulus tests. Two designs without fibers and four with needle and mesh fibers were combined in two water-cement ratios to investigate the effect of fiber integration on concrete properties. In these designs, the amount of super-plasticizer was permanently fixed, and the fibers were used in two volume percentages of 0.2 and 0.3 with equal lengths. Fibers reduced concrete slump loss and increased compressive strength, tensile strength, and elastic modulus, respectively 52.60, 44.29 and 26.74% compared to the original design. In total, fibers with a volume of 0.3% have resulted in higher resistance in the investigated properties of the research.

Seasonal precipitation forecasting plays a pivotal role in water resource management and development of warning systems. This study evaluated the ensemble forecasts of three C3S models over the period 1993–2017 in Iran’s eight classified precipitation clusters for 1- to 3-month lead times. The quantile mapping (QM), the linear scaling (LS), and the gamma distribution mapping (GDM) for post-processing of precipitation forecasts, and the LS and variance scaling (VS) was used to post-process temperature forecasts. The results were then compared with the raw forecasts. It is indicated that the models performed best in western precipitation clusters, while in the northern humid cluster the models had negative skill scores. Almost all the post-processing methods were able to reduce the errors and improve the forecast accuracy in most groups. In general, after post-processing the ECMWF models had the best performance and the MF model had the worst performance. Among the precipitation post-processing methods, GDM and LS performed better, and the superiority of these methods is quite noticeable, especially in the rain-heavy groups of northern Iran (G6 and G8), which had poor raw forecasts. Regarding the post-processing of ensemble temperature forecasts, the performance of each LS and VS method is similar, they have a slight difference in increasing the accuracy of forecasts. Of course, overall, the VS method has worked a little better. The performance of post-processing methods is very effective in the cold months of the year (late autumn and winter) and slightly weaker in the hot months (summer).

Precipitation is one of the most important meteorological phenomena and the main parameter for streamflow forecasting. Therefore, determining the amount of precipitation in the future will help to manage water resources and predict the flood. In this regard, some of the most important meteorological centers in the world provided users with Quantitative Precipitation Forecasts (QPFs) on a global scale. The availability of global ensemble forecasting models in the TIGGE database creates new opportunities for flood forecasting. In this research, the effect of post-processing on the most important global numerical ensemble forecasting models such as UKMO, ECMWF, NCEP and CMA in the TIGGE database during the years 2007 to 2014 for the Bashar river Basin investigated. Evaluations were conducted in probabilistic and nonprobabilistic approach. Initially, the four NWP models with quantile mapping methods were bias corrected. Then, by using Bayesian model averaging (BMA), the post-processing was carried out. The results of probabilistic evaluation after post-processing showed that the skill of forecasting models for the Bashar basin increased and uniform distributions were achieved in verification rank histograms. Also, the results of the probabilistic evaluation with the BSS for the combined mode of four QPF Models with BMA method at most stations were close to 0.5 and in the simple combination was close to zero, indicating that Grand ensemble has a higher skill than single models.

In recent years, we have witnessed a considerable increase in the study of thin flat sheet and various issues related to it. However, comprehensive studies on the formulation of curved plates are rare. Due to the growing use of curved panels in various fields, in this study, the effects of torsional stiffness, bending stiffness and axial stiffness of the peripheral members on shear buckling andpost buckling behavior of curved steel panels were investigated. To do this, ABAQUS software and Riks method were used. The findings revealed thatflexural stiffness and axial stiffness do not increase buckling resistance of panels while the torsional stiffness on elastic buckling resistance of panels was found to be effective. The nonlinear behavior of panels is the function of stiffness ratio of peripheral members to stiffness of panels. Stiffness of peripheral members on capacity after buckling anddevelopment of principal stresses of panels were not affected.