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.
Rate of penetration plays a vital role in field development process because the drilling operation is expensive and include the cost of equipment and materials used during the penetration of rock and efforts of the crew in order to complete the well without major problems. It’s important to finish the well as soon as possible to reduce the expenditures. So, knowing the rate of penetration in the area that is going to be drilled will help in speculation of the cost and that will lead to optimize drilling outgoings. In this research, an intelligent model was built using artificial intelligence to achieve this goal. The model was built using adaptive neuro fuzzy inference system to predict the rate of penetration in
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