Evaluation of acceptance risks and rewards for highway materials using statistical power analysis

Abstract

ABSTRACT State highway agencies (SHAs) use contractor test results in the acceptance and pay rewards of materials and construction. F- and t- tests are commonly used for validating contractor test results. However, these tests are based on sample statistics, which may lead to significant acceptance risks and inconsistent rewards. In this study a Monte Carlo simulation process was developed to systematically quantify the acceptance risks and assess the implications on pay factors (PF). The simulation was performed using typical acceptance quality characteristics (AQCs), such as thickness, for Portland cement concrete (PCC) pavements. The statistical power of the F- and t-test was determined. The analysis indicated that specific combinations of contractor and agency sample sizes and population characteristics have a greater impact on the acceptance risks and may provide inconsistent PF. Thus, the findings of this study can assist both agencies and producers to better assess the acceptance risks and rewards associated with the validation procedures. Therefore, the proposed methodology can be adopted by SHAs to develop statistically valid verification procedures and thus more rational quality assurance (QA). Producers may use such analysis to identify the level of risks and rewards associated with the current production and identify potential improvements in quality.