Construction inspection plays a critical role in Portland cement concrete pavement (PCCP) projects. Limited research has focused on identifying PCCP critical inspection activities and examining the relationship between these inspection activities and the quality of PCCP. To address this gap, a Fuzzy-Bayesian network model was developed based on 12 critical inspection activities of PCCP projects. Fuzzy set theory and Bayesian networks were employed to quantify the linguistic nature of the collected data and evaluate the causal relationships between model variables. A case study was conducted to test and validate the model. The case study results showed that air content, concrete mass per cubic foot, and cored thickness have the greatest impact on the PCCP quality. This study bridged the gap between theory and practice by developing a risk-based model that may help researchers and practitioners better understand how inspection risk is propagated from inspection activities to the quality of PCCP.

施工检查在波特兰水泥混凝土路面（PCCP）项目中起着至关重要的作用。有限的研究重点在于确定PCCP关键检查活动，并检查这些检查活动与PCCP质量之间的关系。为了解决这一差距，基于PCCP项目的12个关键检查活动，开发了模糊贝叶斯网络模型。模糊集理论和贝叶斯网络被用来量化所收集数据的语言性质，并评估模型变量之间的因果关系。进行了案例研究以测试和验证模型。案例研究结果表明，空气含量，每立方英尺的混凝土质量和芯厚对PCCP质量的影响最大。