Novel Model to Predict Critical Strain Energy Release Rate in Semi-Circular Bend Test as Fracture Parameter for Asphalt Mixtures Using an Artificial Neural Network Approach

Abstract

Growing use of recycled asphalt materials in asphalt pavement means the current volumetric-based Superpave mixture design may not address durability concerns arising from replacement of a proportion of virgin binder with recycled ones. To address this limitation, performance-based testing is introduced to supplement conventional volumetric mixture design in assessing cracking performance of asphalt mixtures. Louisiana Department of Transportation and Development’s Specifications for Roads and Bridges specify a criterion for the critical strain energy release rate, Jc, obtained from semi-circular bend (SCB) test as a complement of current practice to evaluate cracking resistance of asphalt mixtures. Quality control/assurance practices, however, require SCB samples to be long-term aged for five days at 85°C, which is a time-consuming process. Therefore, it is beneficial to be able to estimate SCB Jc for long-term aged asphalt mixtures based on SCB Jc measured from plant-produced asphalt mixtures. Asphalt mixture aging is complex, and various variables are involved in the aging process, including volumetric properties of asphalt mixture and chemical/rheological characteristics of asphalt binder. With the capability of artificial neural network (ANN) to address complex relationships between input and output variables, this study aims to predict the fracture parameter, SCB Jc, of asphalt mixtures using ANN. A total of 34 asphalt mixtures were selected for this study. SCB fracture test and asphalt binder tests for chemical and rheological characterization were conducted. Stepwise regression analysis was used to determine the significant parameters in the correlation with SCB Jc. With determined significant parameters, ANN using the gradient descent backpropagation approach was then applied to develop and validate the predictive model. It was shown that the developed ANN model was able to predict the fracture parameter, SCB Jc, of asphalt mixtures more accurately than linear and non-linear regression models.

Use of Random Forest to Predict Intermediate Temperature SCB Jc Parameter of Long-Term Aged Asphalt Mixtures

“…The rheological and chemical properties of asphalt binder and the volumetric properties of asphalt mixture have been reported to have significant effects on the SCB J c ( 9 , 15 ). Therefore, various parameters for volumetric properties of asphalt mixture and rheological and chemical properties of asphalt binder were considered as potential model inputs in this study.…”

“…In total, 66 sets of data points from 21 mixtures were collected in this study. The following gives a brief description of the 31 potential asphalt binder and mixture properties related inputs considered in this study ( 9 , 15 , 27 , 28 ). Except for these inputs, the long-term aging days (LTAD) was also considered for model development.…”

“…These new approaches are called artificial intelligence (AI) and have been applied to model different civil engineering systems. Among AI tools, the Artificial Neural Network (ANN) approach has been applied to develop predictive SCB J c models which have provided significant improvement in accuracy compared with the multivariate regression-based model ( 9 ). However, ANN displays certain shortcomings, such as slow convergence speed, poor generalizing performance, and arriving at local minimum and overfitting problems.…”