Knowledge-based evolving connectionist systems for condition evaluation of sustainable roadways: a feasibility study

In place, sustainable rehabilitation of existing deteriorated concrete highways through rubblization is considered to be a green and economical alternative to other options involving total reconstruction, etc. There is currently no standardised method of backcalculating the rubblized pavement layer moduli from pavement non-destructive test data through inverse analysis. This paper explores the feasibility of applying two evolving connectionist systems (ECOS), namely dynamic evolving neuro-fuzzy inference system (DENFIS) and evolving fuzzy neural network (EFuNN), to rubblized pavement moduli backcalculation. It is advantageous to employ ECOS networks for analysing complex engineered systems such as rubblized pavements since ECOS networks are resistant to catastrophic forgetting, have the ability to adapt to and learn new data as soon as they become available, do not have a limit to the amount of knowledge they can store and learn the examples very quickly compared to traditional multi-layered perceptron backpropagation neural networks (MLP-BP NN).