A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Performance Evaluation of Jointed Concrete Pavements on Mississippi Highways via Artificial Neural Network
Local, state, and federal highway agencies run some form of maintenance and rehabilitation program during the design life of highways. Due to budgetary restrictions, maintenance and rehabilitation actions must be prioritized based on the future condition of the highway section. There are important factors that affect the performance of highways. To properly assess the condition of the pavement and operate maintenance, prediction models with significant condition variables are essential. Mississippi Department of Transportation (MDOT) utilizes probability-based prediction models to determine which sections of the highway and when they need rehabilitation. The current probability models predict the performance without the section-specific parameters. The goal of this study is to develop a new set of performance prediction models for the jointed concrete pavements (JCP) in Mississippi by using the artificial neural networks (ANNs) approach. The best performing ANN model integrated into a Microsoft Excel spreadsheet to generate an application that is simple, user-friendly, and allows the user to visualize the future projections of the pavement section. MDOT personnel can employ this application to predict the condition of the JCP section and prioritize the maintenance and rehabilitation schedule.
Performance Evaluation of Jointed Concrete Pavements on Mississippi Highways via Artificial Neural Network
Local, state, and federal highway agencies run some form of maintenance and rehabilitation program during the design life of highways. Due to budgetary restrictions, maintenance and rehabilitation actions must be prioritized based on the future condition of the highway section. There are important factors that affect the performance of highways. To properly assess the condition of the pavement and operate maintenance, prediction models with significant condition variables are essential. Mississippi Department of Transportation (MDOT) utilizes probability-based prediction models to determine which sections of the highway and when they need rehabilitation. The current probability models predict the performance without the section-specific parameters. The goal of this study is to develop a new set of performance prediction models for the jointed concrete pavements (JCP) in Mississippi by using the artificial neural networks (ANNs) approach. The best performing ANN model integrated into a Microsoft Excel spreadsheet to generate an application that is simple, user-friendly, and allows the user to visualize the future projections of the pavement section. MDOT personnel can employ this application to predict the condition of the JCP section and prioritize the maintenance and rehabilitation schedule.
Performance Evaluation of Jointed Concrete Pavements on Mississippi Highways via Artificial Neural Network
Yasarer, Hakan (author) / Andrews, William (author)
Tran-SET 2021 ; 2021 ; Virtual Conference
Tran-SET 2021 ; 86-92
2021-11-17
Conference paper
Electronic Resource
English
Asphalt subsealing of concrete pavements on Mississippi Highways
| Engineering Index Backfile | 1946
Performance of Jointed Concrete Pavements
NTIS | 1990
Analysis of Jointed Concrete Pavements
| NTIS | 1984