Modeling rutting susceptibility of asphalt pavement using principal component pseudo inputs in regression and neural networks

Abstract

Permanent deformation is a major load-associated distress occurring in flexible pavement systems and increases with load repetitions affecting road roughness, serviceability, and the international roughness index (IRI). Early detection of rutting is necessary for maintenance and rehabilitation activities, but due to the complex behavior of asphalt mixtures, accurately predicting the permanent deformation of asphalt pavement is difficult. Historically, multivariate regression modeling and recently, artificial neural networks (ANNs) are used widely for material properties prediction. The ability to model accurately the response variable is adversely affected when inputs have pairwise correlations. To overcome this barrier, principal component analysis (PCA), as a dimensionality reduction technique, can be used to produce uncorrelated linear combinations of the original inputs as illustrated in this work using 83 (i.e., samples) laboratory compacted specimens from the State of Wisconsin. Asphalt binder, aggregate, and mix properties are obtained and used as the model inputs. The response parameter is the accumulated strain at the corresponding flow number. Using the developed pseudo inputs from PCA, a multivariate regression and an ANN model are generated and were able to fit the test cases with rfit of 0.8 and 0.97 respectively. The developed machine learning-based framework is shown to be a capable tool in estimating the rutting behavior of asphalt mixture.