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Support vector machines for nonlinear pavement backanalysis

Backanalysis or backcalculation of in-service pavement mechanical properties (such as elastic modulus) from pavement Non-Destructive Test (NDT) deflection data is a routine practice carried out by highway engineers for pavement structural condition evaluation, remaining life calculations, and mechanistic-based analysis. Owing to the complexity of this ill-conditioned inverse modeling problem, numerous backcalculation routines have been developed and implemented over the years ranging from simple deflection-basin matching programs to intelligent and soft computing based methodologies and each has its own pros and cons. This paper presents an efficient off-line pavement backcalculation system based on support vector machines (SVM) and compares its performance with another popular machine learning technique, multi-layer perceptrons (MLP). Both systems are trained and tested using synthetic deflection basins generated using a two-dimensional axisymmetric finite element software covering a wide range of in-service pavement scenarios. The results show that the effectiveness of SVM approach in pavement backanalysis is comparable to MLP approach, in general, and better in some specific cases.