A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Application of Soft Computing for Estimation of Pavement Condition Indicators and Predictive Modeling
The pavement management system is recognized as an assertive discipline that works on pavement indices to predict the pavement performance condition. This study used soft computing methods such as genetic algorithms and artificial intelligence to propose a modern generation of pavement indices for road networks in Jordan. The datasets used in this study were collected from multiple roads in Jordan, and 128 data points were used in this study. The input variables are the pavement condition index (PCI) and the international roughness index (IRI) in the artificial neural network (ANN) and gene expression programming (GEP) models. The output variable is the pavement serviceability rate (PSR). The results show an efficient performance benefit of using these techniques. In addition, the ANN and GEP models were able to predict the output variable with a reasonable accuracy, where the ANN model has an R2 value of 0.95, 0.87, and 0.98 for the PCI, IRI, and PSR, respectively. The (R2) values of the GEP model are 0.94, 0.89, and 0.99 for PCI, IRI, and PSR, respectively.
Application of Soft Computing for Estimation of Pavement Condition Indicators and Predictive Modeling
The pavement management system is recognized as an assertive discipline that works on pavement indices to predict the pavement performance condition. This study used soft computing methods such as genetic algorithms and artificial intelligence to propose a modern generation of pavement indices for road networks in Jordan. The datasets used in this study were collected from multiple roads in Jordan, and 128 data points were used in this study. The input variables are the pavement condition index (PCI) and the international roughness index (IRI) in the artificial neural network (ANN) and gene expression programming (GEP) models. The output variable is the pavement serviceability rate (PSR). The results show an efficient performance benefit of using these techniques. In addition, the ANN and GEP models were able to predict the output variable with a reasonable accuracy, where the ANN model has an R2 value of 0.95, 0.87, and 0.98 for the PCI, IRI, and PSR, respectively. The (R2) values of the GEP model are 0.94, 0.89, and 0.99 for PCI, IRI, and PSR, respectively.
Application of Soft Computing for Estimation of Pavement Condition Indicators and Predictive Modeling
Shadi Hanandeh (author) / Ahmad Hanandeh (author) / Mohammad Alhiary (author) / Mohammad Al Twaiqat (author)
2022
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Application of Soft Computing for Prediction of Pavement Condition Index
| Online Contents | 2012
PAVEMENT SURFACE CONDITION INDICATORS
| British Library Conference Proceedings | 2004
Pavement Functional Condition Indicators
| NTIS | 1975
Comprehensive performance indicators for road pavement condition assessment
| Taylor & Francis Verlag | 2018