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A platform for research: civil engineering, architecture and urbanism
Artificial Neural Network Models for the Wander Effect for Connected and Autonomous Vehicles to Minimize Pavement Damage
https://orcid.org/http://orcid.org/0000-0002-7555-1195
After the success of the electric cars, the future will be based on Connected and Autonomous Vehicles (CAVs) that will change the actual driving mode. CAVs will also change the design of road pavements because their transverse position in the lane produces different distresses compared to the actual cars due to CAVs ability to keep their transverse position constantly in the lane, eliminating lateral wandering and inducing more damage to pavements than conventional vehicles. It is crucial to evaluate how the wander of CAVs affects pavement performance for the definition of maintenance and rehabilitation policies. This paper analyses how the lateral wander of CAVs affects pavement performance in terms of fatigue cracking and permanent deformation. These results can be used to establish the values for the wander to minimize those distresses in road pavements. This work was carried out for a pavement with three layers, changing the thickness of the asphalt layer and the stiffness of the subgrade. The wander varied from 0.2 to 0.6 m, applying a normal and a uniform distribution. The variation of the pavement life due to the wander was modeled using Artificial Neural Networks (ANN) and models were developed for these two distributions and for cracking and permanent deformation distresses. It was concluded that a significant damage reduction occurred as the wander increased, and the probability distribution also shaped the damage profiles. The developed ANN models showed that they are suitable tools for predicting the performance of pavement subject to any wheel wander.
Artificial Neural Network Models for the Wander Effect for Connected and Autonomous Vehicles to Minimize Pavement Damage
https://orcid.org/http://orcid.org/0000-0002-7555-1195
After the success of the electric cars, the future will be based on Connected and Autonomous Vehicles (CAVs) that will change the actual driving mode. CAVs will also change the design of road pavements because their transverse position in the lane produces different distresses compared to the actual cars due to CAVs ability to keep their transverse position constantly in the lane, eliminating lateral wandering and inducing more damage to pavements than conventional vehicles. It is crucial to evaluate how the wander of CAVs affects pavement performance for the definition of maintenance and rehabilitation policies. This paper analyses how the lateral wander of CAVs affects pavement performance in terms of fatigue cracking and permanent deformation. These results can be used to establish the values for the wander to minimize those distresses in road pavements. This work was carried out for a pavement with three layers, changing the thickness of the asphalt layer and the stiffness of the subgrade. The wander varied from 0.2 to 0.6 m, applying a normal and a uniform distribution. The variation of the pavement life due to the wander was modeled using Artificial Neural Networks (ANN) and models were developed for these two distributions and for cracking and permanent deformation distresses. It was concluded that a significant damage reduction occurred as the wander increased, and the probability distribution also shaped the damage profiles. The developed ANN models showed that they are suitable tools for predicting the performance of pavement subject to any wheel wander.
Artificial Neural Network Models for the Wander Effect for Connected and Autonomous Vehicles to Minimize Pavement Damage
https://orcid.org/http://orcid.org/0000-0002-7555-1195
After the success of the electric cars, the future will be based on Connected and Autonomous Vehicles (CAVs) that will change the actual driving mode. CAVs will also change the design of road pavements because their transverse position in the lane produces different distresses compared to the actual cars due to CAVs ability to keep their transverse position constantly in the lane, eliminating lateral wandering and inducing more damage to pavements than conventional vehicles. It is crucial to evaluate how the wander of CAVs affects pavement performance for the definition of maintenance and rehabilitation policies. This paper analyses how the lateral wander of CAVs affects pavement performance in terms of fatigue cracking and permanent deformation. These results can be used to establish the values for the wander to minimize those distresses in road pavements. This work was carried out for a pavement with three layers, changing the thickness of the asphalt layer and the stiffness of the subgrade. The wander varied from 0.2 to 0.6 m, applying a normal and a uniform distribution. The variation of the pavement life due to the wander was modeled using Artificial Neural Networks (ANN) and models were developed for these two distributions and for cracking and permanent deformation distresses. It was concluded that a significant damage reduction occurred as the wander increased, and the probability distribution also shaped the damage profiles. The developed ANN models showed that they are suitable tools for predicting the performance of pavement subject to any wheel wander.
Artificial Neural Network Models for the Wander Effect for Connected and Autonomous Vehicles to Minimize Pavement Damage
Lecture Notes in Civil Engineering
Pereira, Paulo (editor) / Pais, Jorge (editor) / Pais, Jorge (author) / Thives, Liseane (author) / Pereira, Paulo (author)
International Conference on Maintenance and Rehabilitation of Pavements ; 2024 ; Guimarães, Portugal
Proceedings of the 10th International Conference on Maintenance and Rehabilitation of Pavements ; Chapter: 45 ; 471-481
2024-07-21
11 pages
Article/Chapter (Book)
Electronic Resource
English
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