Risk-Based Inspection Model for Hot Mix Asphalt Pavement Construction Projects
Publication: Journal of Construction Engineering and Management
Volume 147, Issue 6
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.
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Data Availability Statement
Data generated or analyzed during the study are available from the corresponding author by request.
Acknowledgments
The authors would like to thank the engineers and inspectors of KDOT. The contents of this paper reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented in this paper. The contents do not necessarily reflect the official views or policies of the KDOT, nor do the contents constitute a standard, specification, or regulation.
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Published In
Journal of Construction Engineering and Management
Volume 147 • Issue 6 • June 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Aug 11, 2020
Accepted: Dec 22, 2020
Published online: Apr 8, 2021
Published in print: Jun 1, 2021
Discussion open until: Sep 8, 2021
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