From data to decision: integrated approach to pavement preservation in Iowa through treatment effectiveness analysis

ABSTRACT

This investigation seeks to enhance ‘Iowa's Pavement Preservation Guide’ with a data-informed decision-making framework. Iowa DOT's databases were used as the data source for evaluating pavement performances under different preservation treatment regimens and determining the effectiveness of those strategies. Five preservation treatment methods for flexible and composite pavements, namely, crack sealing-and-filling, microsurfacing, slurry sealing, patching, and thin hot mix asphalt (HMA) overlays were evaluated using historic data from about 7000 road segments between 1998 and 2020. The data included various attributes describing pavement, road, and traffic characteristics required for analysing condition-based performance and treatment impacts. Two key indicators, service life extension (LE) and index benefit (IB), were defined and used to quantify treatment effectiveness in terms of pavement condition improvement and impact on deterioration rate. The treatment-effectiveness indicators were then factored into the cost analyses to assess the cost-effectiveness of preservation strategies. The results of the treatment effectiveness analysis were used to develop decision matrices that provide insights for selecting preservation treatments based on benefit–cost analysis considering both the overall condition and the dominant distress. This approach to treatment effectiveness highlighted the distinction between an optimum pavement preservation strategy and one that targets maximum service life extension.

KEYWORDS:

• Pavement management system
• pavement preservation
• data-driven decision-making

Acknowledgements

The authors express their sincere appreciation for the invaluable support provided by the Iowa DOT throughout this project. The authors would also like to thank Dr. Benjamin Claypool of the City of Cedar Falls, Iowa, for graciously sharing his knowledge with the research team, enriching the study with his valuable insights. The authors acknowledge the usage of the big language model ChatGPT, developed by OpenAI, in editing this manuscript for enhancing the clarity and coherence of the final document.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

The authors confirm contribution to the paper as follows: study conception and design: O. S., A.S., and A.B.; analysis and interpretation of results: A.S., S.A., O.S.; draft manuscript preparation: S.A., A.S., O.S., and A.B.; All authors reviewed the results and approved the final version of the manuscript.

Additional information

Funding

This research was funded by the Iowa Department of Transportation (Iowa DOT) under Grant 20-728, TR-784.