Determining the Optimum Sample Size for Quality Assurance (QA) of Asphalt Mixtures: A Case Study

Abstract

Acceptance plans for asphalt mixtures use a certain sample size that is often established based on the purpose of sampling, population size, risk, and allowable error for evaluation. The rate of quality control (QC) sample size is often higher than the quality assurance (QA) sample size. The test results obtained from the QA samples are commonly used to validate the QC test results and to assist the state department of transportation (DOT) with payment decisions. However, if the QA sample size is insufficient to make accurate judgments, the probability of making incorrect decisions regarding acceptance increases. On the other hand, oversampling needlessly consumes both time and cost. To identify the appropriate sample size for QA testing, a balance must be struck between a number of variables. In this case study, two models were developed using the Oregon Department of Transportation (ODOT) data to determine the appropriate QA sample size. The need for this work was realized when a review of ODOT paving projects revealed a large variability in lot size. These ranged from 3000 to more than 100 000 tons with commensurate QA sample size rates. The typical standard deviation (STDEV) values of asphalt content (AC) and in-place density were determined. The developed models show that using the STDEV values that represented more than 90 percent of the projects, ODOT needed to increase QA sample size for both AC and density in lots of less than 22 000 tons. The results also show that sample can be decreased for AC and remain as is for density in projects of more than 22 000 tons of asphalt mixtures. The proposed models can be used to determine the optimum sample size for different lots sizes.

Keywords

• optimum sample size
• lot size
• quality control
• quality assurance
• standard deviation