This report presents the procedures and results of a computer simulation analysis conducted to investigate the performance of 7 methods for determining the payment factor for a lot of materials when 3 correlated acceptance properties, i.e., the Marshall stability, flow and air voids, are used for acceptance purposes. The methods investigated were based upon triple numerical integration of the 3 properties, multiplying the individual property values, averaging the individual property values and using the smallest individual property value. Marshall test results from 15 runway paving projects were analyzed to determine mean, variance and correlation values obtained in field construction. Computer simulation was used to investigate the performance of the various methods for determining the payment factor for multiple acceptance properties. Simulation was used to generate Marshall test results for using population values from the 15 field projects. The mean square payment error (MSE) for each method was used as the norm and the minimum MSE as the criterion for choice among the methods. It is recommended that the average of the 3 payment factors for the individual Marshall properties be determined using the quality index approach currently employed by the FFA Eastern Region for density acceptance purposes. The payment factor for the Marshall properties can then be calculated as the average of the 3 individual property payment factors. Keywords: Acceptance Plans, Multiple Price Adjustments, Marshall Properties, Quality Assurance, Computer Simulation, and Statistical Quality Control.

This report summarizes a research project that investigated the use of Marshall properties for acceptance purposes. Since the Marshall properties are physically related, they can be expected to be statistically correlated. It is therefore necessary to determine whether correlations should be considered when developing acceptance plans. The research consisted of 3 major phases; a laboratory analysis, field data collection and computer simulation analyses. A laboratory analysis was conducted to establish whether correlations are present among asphalt content, gradation, and the Marshall values for stability, flow and air voids. Another aspect of the laboratory analysis investigated 3 methods for determining maximum specific gravity (MSG) for air voids determination. Field data were also collected from 5 paving projects. Finally, computer simulation was used to evaluate the performance of 7 methods for determining the payment factor for the Marshall properties. It is recommended that ASTM D-2041 be used to establish MSG values for both the job mix formula and for field quality control testing. It is also recommended that the quality control testing. It is also recommended that the quality index approach be used to estimate individual PWL values for each of the Marshall properties. These PWL values can then be used with a payment schedule to determine individual payment factors for the 3 properties. The overall Marshall payment factor is then the average of the 3 individual factors. Keywords: Acceptance Plans, Marshall Properties, Computer Simulation, Quality Assurance, Multiple Price Adjustments, Correlation, Statistical Quality Control.