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Effect of Hydrated Lime on Moisture Susceptibility of Asphalt Mixtures
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Moisture induced damage can cause a progressive deterioration in the performance of asphalt pavement by the loss of adhesion between asphalt binder and aggregate surface and/or loss of cohesion within the binder in the presence of water. The objective of this paper is to improve the asphalt mixtures resistance to moisture by using hydrated lime as an anti-stripping additive. For this purpose, two types of asphalt binder were utilized; asphalt grades (40-50) and (60-70) with one type of aggregate of 19.0 mm aggregate nominal maximum size, and limestone dust as a mineral filler. Marshall method was adopted to find the optimum asphalt content. Essentially, two parameters were determined to evaluate the moisture susceptibility, namely: The Index of Retained Strength and the Tensile Strength Ratio. The hydrated lime was added by 1.0, 1.5, and 2.0 percentages (by weight of aggregate) using the saturated surface dry method. It was concluded that using hydrated lime will improve the moisture damage resistance. This was adopted as the value of tensile strength ratio increased by 24.50 % and 29.16% for AC (40-50) and AC (60-70) respectively, furthermore, the index of retained strength also increased by 14.28 % and 17.50 % for both asphalt grades. The optimum hydrated lime content founded to be 1.5 %.

 

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Publication Date
Wed Apr 25 2018
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Different Estimation Methods for System Reliability Multi-Components model: Exponentiated Weibull Distribution
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        In this paper, estimation of system reliability of the multi-components in stress-strength model R(s,k) is considered, when the stress and strength are independent random variables and follows the Exponentiated Weibull Distribution (EWD) with known first shape parameter θ and, the second shape parameter α is unknown using different estimation methods. Comparisons among the proposed estimators through  Monte Carlo simulation technique were made depend on mean squared error (MSE)  criteria

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