This article aims to investigate the feasibility of incorporating of an artificial neural network (ANN) as an innovative technique for modelling the pavement structural condition, into pavement management systems. For the development of the ANN, strain assessment criteria are set in order to characterise the structural condition of flexible asphalt pavements with regards to fatigue failure. This initial task is directly followed with the development of an ANN model for the prediction of strains primarily based on in situ field gathered data and not through the usage of synthetic databases. For this purpose, falling weight deflectometer (FWD) measurements were systematically conducted on a highway network, with ground-penetrating radar providing the required pavement thickness data. The FWD data (i.e. deflections) were back-analysed in order to assess strains that would be utilised as output data in the process of developing the ANN model. A paper exercise demonstrates how the developed ANN model combined with the suggested conceptual approach for characterising pavement structural condition with regard to strain assessment could make provisions for pavement management activities, categorising network pavement sections according to the need for maintenance or rehabilitation. Preliminary results indicate that the ANN technique could help assist policy decision makers in deriving optimum strategies for the planning of pavement infrastructure maintenance.

Pavement roughness as a critical determinant of public satisfaction can potentially play a major role in road or highway resource allocation to competing pavement resurfacing projects. With this in mind, the aim of the present paper is to develop an accurate model for the prediction of pavement roughness in terms of the International Roughness Index (IRI) using artificial neural networks (ANNs) and support vector machines (SVMs). The modeling is based on pavement roughness data collected periodically for a high‐volume motorway during a seven‐year period, on a yearly basis. The comparative study of the developed models concludes that the performance of the ANN model is slightly better compared to the SVM in terms of prediction accuracy. Further, the analysis results produce evidence in support of the statement that both models are capable to predict accurately pavement roughness; hence, they are deemed useful for supporting decision making of pavement maintenance and rehabilitation strategies.

This study quantifies the potential environmental benefits associated with the incorporation of industrial by-products and recycled materials combined with warm mix asphalt (WMA) additives in wearing course mixtures for asphalt pavement construction. An electric arc furnace (EAF) steel slag-based mix and three recycled mixtures incorporating steel slag and fractionated reclaimed asphalt (RA) of varying contents (i.e. 25, 40, and 50%) were analyzed comparatively to reference hot mix asphalt (HMA) using the ‘cradle-to-gate’ approach. The results of this study indicated that the warm asphalt mixture incorporating steel slag and 50% RA is the most environmentally friendly alternative. The greatest environmental benefits result from the combined use of steel slag and reclaimed asphalt, which is attributed to lesser demands for raw material extraction, bitumen production, and resource supply. On the contrary, the contribution of lowering mixtures’ production temperature by 30°C is minimal.