A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Pavement Performance Modeling Considering Maintenance and Rehabilitation for Composite Pavements in the LTPP Wet Non-Freeze Region Using Neural Networks
Efficient and well-maintained pavement systems are crucial to ensure appropriate conditions for the road networks. If timely maintenance and rehabilitation (M&R) is not performed, the pavement deterioration may lead to poor conditions that affect the comfort and safety of road users. The effectiveness of any M&R actions essentially depends on the time of treatment. This paper presents the development of pavement roughness models using the artificial neural networks (ANNs) approach for composite pavements using the Long-Term Performance Pavement (LTPP) program database for the wet, non-freeze climate region. A total of 49 composite pavement sections with 353 data points were analyzed. The use of an M&R variable in the model development resulted in more realistic and accurate models to predict future pavement conditions, identify M&R actions, and simulate interventions for future years. The developed models could be used by transportation agencies as a valuable tool for more effective M&R scheduling prioritizing worst condition pavement sections.
Pavement Performance Modeling Considering Maintenance and Rehabilitation for Composite Pavements in the LTPP Wet Non-Freeze Region Using Neural Networks
Efficient and well-maintained pavement systems are crucial to ensure appropriate conditions for the road networks. If timely maintenance and rehabilitation (M&R) is not performed, the pavement deterioration may lead to poor conditions that affect the comfort and safety of road users. The effectiveness of any M&R actions essentially depends on the time of treatment. This paper presents the development of pavement roughness models using the artificial neural networks (ANNs) approach for composite pavements using the Long-Term Performance Pavement (LTPP) program database for the wet, non-freeze climate region. A total of 49 composite pavement sections with 353 data points were analyzed. The use of an M&R variable in the model development resulted in more realistic and accurate models to predict future pavement conditions, identify M&R actions, and simulate interventions for future years. The developed models could be used by transportation agencies as a valuable tool for more effective M&R scheduling prioritizing worst condition pavement sections.
Pavement Performance Modeling Considering Maintenance and Rehabilitation for Composite Pavements in the LTPP Wet Non-Freeze Region Using Neural Networks
Rulian, B. (author) / Hakan, Y. (author) / Salma, S. (author) / Yacoub, N. (author)
International Conference on Transportation and Development 2022 ; 2022 ; Seattle, Washington
2022-08-31
Conference paper
Electronic Resource
English