Modeling of asphalt pavement performance indices in different climate regions using soft computing techniques

Pavement Management Systems (PMS) enhance pavement performance over the pavements' predicted lifespan by maximizing pavement life. PMS have become an essential aspect of construction and maintenance in the road domain, providing significant cost and energy emission reductions. In addition, using pavement performance prediction models have become an important part of PMS as a technically method for road engineers and various transportation agencies during the past several decades. The Pavement Condition Index (PCI) and International Roughness Index (IRI) are generally accepted methods for gauging ride quality and pavement conditions. Asphalt pavements are highly sensitive to various parameters, including pavement distress, environment, and traffic volume. Hence, studying these variables while developing prediction models is a vital step that can help develop asphalt pavement performance indices. This research aimed to introduce an effective method for developing asphalt pavement performance indices in different climate regions. This research provided a methodology to develop performance models using three soft computing techniques, namely the fuzzy inference system (FIS), multiple linear regression (MLR), and artificial neural networks (ANNs). Two sources were used for the extracted dataset: the long-term pavement performance (LTPP) data set for four climate regions in the U.S. and Canada and filed survey data of section roads of St. John's, Newfoundland, Canada. First, for the classification section, the research presented in this study provided a FIS that uses appropriate membership functions for computing PCI and IRI values. A fuzzy input was calculated by considering the degree of distress from nine density types of pavement distress coefficients (rutting, fatigue cracking, block cracking, longitudinal cracking, transverse cracking, patching, potholes, bleeding, and ravelling), which were considered as fuzzy input variables. Results presented that the rutting and transverse cracking had the most significant influence on the PCI model, while rutting and patching had the most significant impact on the IRI model. Second, the MLR and ANNs techniques were used for predicting and developing models for the PCI and IRI of flexible pavements. The LTPP database was used to obtain three fundamental variables (pavement distress, environmental, and traffic volume) as input variables for four climate regions. Finally, for the case study, the research developed a second set of pavement distress models based on a field survey of St. John's city's input variables for predicting PCI and IRI models. A high determination coefficient (R²), low root mean square error (RMSE) and mean absolute error (MAE)indicated good accuracy for the prediction models. The results showed that the ANNs have more precision than the MLR techniques. However, the results showed that both methods perform well

Application of Artificial neural network technique for prediction of pavement roughness as a performance indicator

One of the most important and widely accepted pavement performance and ride quality indicators is the International Roughness Index (IRI). This study investigates the combined effect of pavement distress on flexible pavement performance in two climate regions (wet freeze and wet freeze) in the U.S. and Canada. The long-term pavement performance (LTPP) database was used to obtain pavement distress data. Data from forty-three of the LTPP pavement sections (333 observations) with no previous maintenance were collected. The proposed models predict the IRI as a function of pavement distress variables, namely the pavement age, rutting, fatigue cracking, block cracking, longitudinal cracking, transverse cracking, potholes, patching, bleeding, and ravelling. After the data were collected, modelling was conducted to predict IRI using two techniques: multiple linear regression (MLR) and artificial neural network (ANN). The coefficient of determination (R2), root mean squared error (RMSE) and mean absolute error (MAE) were used to examine the performance of the two techniques adopted in this study. The models' results revealed that both ANN and MLR models could predict IRI with good accuracy. The MLR models yielded the R2 values of 77.7% and 89.3%, whereas the ANN models resulted in the R2 values of 99.1% and 97.5% for wet freeze and wet no freeze climate regions, respectively. As a result, ANN models are more accurate and efficient than MLR models