Application of Soft Computing for Estimation of Pavement Condition Indicators and Predictive Modeling - Hanandeh, Shadi; Hanandeh, Ahmad; Alhiary, Mohammad; Al Twaiqat, Mohammad (Frontiers in Built Environment, 2022)
Pavement management system recognized as one of the powerful disciplines that working on pavement indices to predict the pavement performance condition. This study used soft computing methods such as genetic algorithm and artificial intelligence methods to propose a modernized generation of pavement indices PCI, IRI and PSR models for road networks in Jordan. The datasets used in this study was performed at multiple roads in Jordan, 128 samples data were provided and utilized in this study. For both methods used in this study ANN and GA, the input variables include two parameters, namely PCI and IRI, the output variable includes PSR. The results show an efficient performance benefit of using these techniques; moreover, new equations were implemented by employing GA software. Besides, neural network model, a precise evaluation was obtained using R2 = 0.95, 0.87, 0.98 for PCI, IRI and PSR respectively. The other technique used in this s study is the genetic algorithm paradigm that provides high (R2) values of 0.94, 0.89, and 0.99 for PCI, IRI, and PSR respectively.