Estimating the impact of automated truck platoons on asphalt pavement’s fatigue life using artificial neural networks

ABSTRACT

Automated truck platooning may cause up to 85% reduction in truck following distance compared to conventional vehicles and therefore reduces rest periods between loading cycles on pavement materials. Fatigue cracking is one of the major distresses in asphalt pavement structures, and pavement fatigue life is highly dependent on rest periods between loading cycles. In this paper, Artificial Neural Network (ANN) modeling was applied on the existing NCHRP 09-44A project’s database of extensive laboratory beam fatigue testing results on asphalt mixtures with various rest periods. The developed ANN model was used to predict number of cycles to failure as a function of rest periods and to estimate the impact of truck platooning on pavement fatigue life. In addition, a stand-alone linear multivariate regression equation of fatigue life was developed independently from the ANN model. Based on the results, an 85% reduction in the following distance of platooned trucks may lead to between 7% and 25% reduction in pavement fatigue life. The Platooning Fatigue Life Ratio (PFLR) was found to be dependent on temperature, applied strain level, and mixture parameters. Finally, the applied strain level was the most significant testing factor and binder grade was the most significant mixture parameter on PFLR.

KEYWORDS:

• Truck platooning
• automated vehicles
• fatigue cracking
• rest period
• healing
• stress relaxation
• artificial neural network

Author contribution statement

The authors confirm contribution to the paper as follows: study conception and design: Elwardany, Souliman, and Hanna; data collection: Souliman; analysis and interpretation of results: Hanna, Elwardany, and Souliman; draft manuscript preparation: Elwardany, Hanna, and Souliman; 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).