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To properly plan for construction, repair, maintenance, and reconstruction of highways the minimum acceptable roadway condition is needed information. This, along with other pavement management tools, will help select the most desirable roadway alternatives. In this research the minimum acceptable conditions are developed based on an opinion survey of non-technical but high-level decision makers. Roadway roughness, expressed as international roughness index (IRI), is used as the measurement criteria. Because IRI is a widely known, acceptable, and a uniformly measurable index, it is used for the purpose of this research. The minimum IRI values developed here will help managers, planners, and engineers in prioritizing their plans and projects. Iran has a central planning system, hence having a minimum acceptable IRI will help in producing homogeneity in decision making. A questionnaire is sent to top level and influential managementlevel officials who have a decisive input in highway matters. The officials are asked to choose the minimum acceptable service level of different types of roadways and classifications. Naturally, roadways with higher levels of importance would require higher service levels. The answers to the survey questionnaires are investigated to determine a preferred minimum acceptable roadway condition. The IRI is computed using a mechanical device enabling a more uniform data collection. The IRI was first proposed by The World Bank as a standard roughness statistic. Extensive research has proven that the IRI can be related to pavement condition. The result of the opinion survey is investigated to determine the minimum levels acceptable for each category. The responses show distinct preference patterns for most of the roadway types. Survey results are investigated by plotting and analyzing them. Based on road user’s perception of roadway condition using guidelines from AASHTO, the Corp of Engineers, and related research work. The appropriate IRI limits and ranges are determined for Iran’s highways. These values are adjusted to obtain final values for Iran. The result, shown in a table, gives upper and lower IRI values accepted and recommended for Iran’s highways. The result of this research work is specifically useful in developing specifications for new pavement design, accepting new pavement from contractors, pavement management, highway planning, and in roadway life cycle cost analysis decision making. The results are subject to refinement over time.

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In this study an empirical model which can be used to predict the rutting parameter (G*/sinδ) for neat and powder rubber modified bitumen describes. The model was developed using 36 unique powder rubber modified bitumen combinations, rubber concentrations were varied at 5% intervals between 5 and 20%. The effects of powder rubber particle size on model accuracy were also studied ultimately a model was produced with the capability of predicting rutting parameter values over a range of temperatures and rubber concentrations. By definition, the upper limit of the performance grade is dependent on the rutting parameter value therefore, the relationship was also considered in terms of high end failure temperature. The Rubber Coefficient for rutting parameter (Rcg) was identified as an important parameter in the estimation of rutting parameter (G*/sinδ) with the addition of powder rubber. This term is a quantitative representation of the increase typically witnessed in rutting parameter values with the addition of powder rubber. Ambient ground powder rubber exhibited higher Rcg values than cryogenically ground particles. Additionally, 95% confidence intervals were generated for the predictive model thus providing a range of accuracy for the model. The resulting confidence intervals were approximately +/-1300 Pa these confidence intervals were seen to capture 92.6% of the 462 data points used. Findings from this research suggest that the differences between cryogenic and ambient powder rubber bitumen are accurately described using the Rcg, furthermore bitumen properties may be predicted using an empirical equation.