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Runoff Estimation Based on Stochastic Optimization Method Original Article, C48 Nikbakht Shahbazi A. Journal. Civil Eng. Urban. 3(5):323-330. 2013 ABSTRACT:Despite widespread use of Curve Number (CN) as main parameter in SCS equations for estimating runoff of basins with limited data, its capability in appropriate estimation of runoff has always been controversial. In this study, ability of CN method has been studied in estimating runoff through the application of a stochastic optimization method and within the framework of SCS equations. In stochastic method, sensitivity of statistical distributions, number of statistical samples and range of CN changes on accuracy of calculations have been studied through the application of Monte Carlo Simulation. The used stochastic method is based on the rainfall and runoff information. For this purpose, rainfall and runoff modeling are first selected and probable distribution functions, governing these events, are specified. Then, rainfall and runoff modeling are produced in different sample sizes within random modeling. Produced samples of rainfall and runoff are evaluated based on the specific criteria and specific runoff modeling is selected. The selective runoffs and number of experimental curves are used in SCS equations, rainfall modeling is calculated and their probable functions are determined. Based on Kolmogrov-Smirnov Test, optimal curve number (CNOPT) is set between produced distribution functions and initial probability distribution function governing rainfall events. Also, average and mean curve number (CNAVG,,CNMEG) has been studied. Statistics of basins representing Kasilian and Emameh (Northen Iran) has been used with the aim of studying efficiency of stochastic method. In Kasilian Basin, the runoffs, calculated through the use of CN OPT, enjoyed the minimum error than observational runoffs. This emphasizes desirable capability of CN method in estimating runoff of the basin but stochastic method was unenforceable in Emameh Basin due to the shortage of rainfall and runoff events. Keywords:Curve Number, Monte Carlo Simulation, SCS Method, Statistical Distribution Function, Stochastic Method. منبع: [Hidden Content] دانلود: [Hidden Content].,48-323-330.pdf J. Civil Eng. Urban.,48-323-330.pdf

Investigating the Distribution and Orientation of Steel Fiber Reinforced Concrete Original Article, B34 Zandi Y. Journal. Civil Eng. Urban. 2(5): 191-195. 2012. ABSTRACT:The distribution functions of fiber location variables were investigated that can be used to produce an analytic model to predict the mechanical properties of SFRC. Considering the effect of surrounding boundaries and distribution function, theoretical expressions were derived for the number of fibers per unit cross sectional area in SFRC. A comparison between empirical results and the theory were done to confirm the analytical results. Fibers are usually used in concrete to control cracking due to both plastic shrinkage and drying shrinkage. The amount of fibers added to a concrete mix is expressed as a percentage of the total volume of the composite (concrete and fibers), termed volume fraction (Vf). Vf typically ranges from 0.1 to 3%. Fibers with a non-circular cross section use an equivalent diameter for the calculation of aspect ratio. Keywords:steel fiber reinforced concrete; steel fibers; vibration, distribution function. منبع: [Hidden Content] دانلود: [Hidden Content],%20B%2034%20191-195.pdf JCEU, B 34 191-195.pdf