A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Incorporating Maintenance and Rehabilitation History into Pavement Performance Modeling for Jointed Plain Concrete Pavement
In order to ensure good quality and well-maintained road, regular maintenance and rehabilitation (M&R) of pavement is mandatory. The Long Term Pavement Performance (LTPP) database has the most comprehensive pavement performance data along with its M&R history for more than 2,500 pavement sections throughout the United States and Canada. The artificial neural networks (ANNs) modeling approach has been used in recent years for the prediction of pavement performance. However, most pavement performance modeling does not consider the M&R history in the model development. As such, this paper aims to exhibit a methodology to determine pavement performance incorporating maintenance and rehabilitation history using the LTPP database and ANN modeling approach. The models will be developed using data collected from the LTPP database for jointed plain concrete pavement (JPCP) from the wet, non-freeze climatic region. The M&R history is denoted as construction number (CN) in the LTPP database. The hypothesis testing demonstrated M&R treatment has a significant effect on pavement performance. Several models will be attempted to evaluate the best way to include M&R history by changing the CN variable from the LTPP database. The use of M&R history in pavement performance modeling reflects more realistic pavement conditions in the model development process. The developed models will establish better accuracy in the prediction of future pavement conditions. This can be beneficial to the policymaker for short-term and long-term budget allocation in the M&R treatment of highway pavements.
Incorporating Maintenance and Rehabilitation History into Pavement Performance Modeling for Jointed Plain Concrete Pavement
In order to ensure good quality and well-maintained road, regular maintenance and rehabilitation (M&R) of pavement is mandatory. The Long Term Pavement Performance (LTPP) database has the most comprehensive pavement performance data along with its M&R history for more than 2,500 pavement sections throughout the United States and Canada. The artificial neural networks (ANNs) modeling approach has been used in recent years for the prediction of pavement performance. However, most pavement performance modeling does not consider the M&R history in the model development. As such, this paper aims to exhibit a methodology to determine pavement performance incorporating maintenance and rehabilitation history using the LTPP database and ANN modeling approach. The models will be developed using data collected from the LTPP database for jointed plain concrete pavement (JPCP) from the wet, non-freeze climatic region. The M&R history is denoted as construction number (CN) in the LTPP database. The hypothesis testing demonstrated M&R treatment has a significant effect on pavement performance. Several models will be attempted to evaluate the best way to include M&R history by changing the CN variable from the LTPP database. The use of M&R history in pavement performance modeling reflects more realistic pavement conditions in the model development process. The developed models will establish better accuracy in the prediction of future pavement conditions. This can be beneficial to the policymaker for short-term and long-term budget allocation in the M&R treatment of highway pavements.
Incorporating Maintenance and Rehabilitation History into Pavement Performance Modeling for Jointed Plain Concrete Pavement
Salma, S. (author) / Hakan, Y. (author) / Rulian, B. (author) / Jacob, N. (author)
International Conference on Transportation and Development 2022 ; 2022 ; Seattle, Washington
2022-08-31
Conference paper
Electronic Resource
English
Jointed Plain Concrete Pavement Model Evaluation
| British Library Online Contents | 2006
Jointed Plain Concrete Pavement Model Evaluation
| British Library Conference Proceedings | 2006