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.
Acknowledgements
The authors confirm contribution to the paper as follows: study conception and design: Dimitrios Goulias, Yunpeng Zhao; data collection: Yunpeng Zhao, Dimitrios Goulias; analysis and interpretation of results: Yunpeng Zhao, Dimitrios Goulias; draft manuscript preparation: Yunpeng Zhao, Dimitrios Goulias. All authors reviewed the results and approved the final version of the manuscript.
Disclosure statement
No potential conflict of interest was reported by the author(s).