![]() ![]() If you do not agree to the terms of this License Agreement, IMPORTANT- READ THESE TERMS CAREFULLY BEFORE ENTERING THIS ASTM PRODUCT.īy purchasing a subscription and clicking through this agreement, you are entering intoĪ contract, and acknowledge that you have read this License Agreement, that you understand The proposed analysis approach can be implemented by agencies in decision making of quality assurance based on actual practice of maintenance strategy. Compared with the pay adjustment obtained from the deterministic approach, the probabilistic analysis resulted in a similar pay adjustment for the case of single overlay but a greater pay adjustment for the case of successive overlays. The probabilistic distribution of pay adjustments was presented when the uncertainty was considered with respect to the effect of initial IRI on pavement life and the assumption of overlay sequence. The Bayesian approach with Markov Chain Monte Carlo (MCMC) methods was used to develop the probabilistic model between the expected pavement life and the initial IRI. The pay adjustments were significantly affected by the assumption of analysis period in LCCA considering single or successive overlay applications. It was found that the terminal IRI values when pavement overlay life was reached based on the SDI threshold of poor condition had an average value of 128 in./mile (2.02 m/km) with standard deviation of 15 in./mile (0.24 m/km). The relationship between the initial International Roughness Index (IRI) and pavement life based on the threshold of surface distress index (SDI) was developed to calculate the pay adjustment using LCCA. This study aims at developing pay adjustments for initial smoothness of asphalt pavement overlay using life-cycle cost analysis (LCCA). ![]()
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