A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Estimating the impact of automated truck platoons on asphalt pavement’s fatigue life using artificial neural networks
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.
Estimating the impact of automated truck platoons on asphalt pavement’s fatigue life using artificial neural networks
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.
Estimating the impact of automated truck platoons on asphalt pavement’s fatigue life using artificial neural networks
Elwardany, Michael D. (author) / Hanna, Botros N. (author) / Souliman, Mena (author)
International Journal of Pavement Engineering ; 23 ; 4223-4235
2022-10-15
13 pages
Article (Journal)
Electronic Resource
Unknown
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