Risk-Based Inspection Model for Hot Mix Asphalt Pavement Construction Projects

Abstract

Hot mix asphalt (HMA) is a critical component in highway construction projects. There is a lack of studies that have investigated the causal relationship between inspection activities and quality of HMA pavement. This study addresses this knowledge gap by developing a risk-based analysis model. The model includes 14 HMA critical inspection activities. The fuzzy set theory (FS) was incorporated into the model to overcome the linguistic nature of the collected data. Bayesian belief networks (BBN) were used to investigate the causal relationship between the model variables. A case study was conducted to test and verify the model. The model is capable of calculating the probability of HMA risk levels, identifying the most likely potential causes of quality shortfall risk, and providing guidance to mitigate the risk via three risk scenarios. This study contributes to the construction engineering and management body of knowledge by proposing a risk-based inspection model to investigate the impact of risk on HMA pavement quality. The proposed model may help transportation agencies optimize inspection resources by updating probabilities based on actual inspection results.