Corrosion- induced damage in reinforced concrete structure such as bridges, parking garages, and buildings, and the related cost for maintaining them in a serviceable condition, is a source of major concern for the owners of these structures.
Fly ash produced from south Baghdad power plant with different concentrations (20, 25 and 30) % by weight from the cement ratio were used as a corrosion inhibitor as a weight ratio from the cement content.
The concrete batch ratio under study was (1:1.5:3) cement, sand and gravel respectively which is used in Iraq. All the raw materials used were locally manufactured.
Concrete slabs (250x250x70) mm dimensions were casted, using Poly-wood molds. Two steel bars were embedded in the central position of each slab at the mid- height (about 35 mm), with a space of 100 mm between each other.
A 16 concrete slabs were prepared (0, 20, 25 and 30) wt. % of fly ash. The specimens were partially immersed in 3.5 wt. % NaCl solution in order to predict the corrosion.
Half-cell potential test technique was used to estimate the corrosion rate which is occurred in the steel bar due to the migration of chloride ions through the concrete, depending on the ASTM C876-08.
The result shows that the potential values of steel in concrete were shifts to the positive direction with increasing the percentage of fly ash, because the reduction of porosity by the addition of fly ash which fill the pores and inhibit the chloride ions to reach to steel.
The results also show that the further increase of fly ash (30%) the possibility of carbonation is increased which result in reduction its alkalinity, thereby permitting corrosion of embedded steel.
The objective of the study is to demonstrate the predictive ability is better between the logistic regression model and Linear Discriminant function using the original data first and then the Home vehicles to reduce the dimensions of the variables for data and socio-economic survey of the family to the province of Baghdad in 2012 and included a sample of 615 observation with 13 variable, 12 of them is an explanatory variable and the depended variable is number of workers and the unemployed.
Was conducted to compare the two methods above and it became clear by comparing the logistic regression model best of a Linear Discriminant function written
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This study aims to depend on virtual Benchmarking style in the eval
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