The use of recycled Reclaimed Asphalt Pavement (RAP) material into flexible pavement contributes to sustainable development with environmental and economic benefits. In the present work, Semi-Circular Bending (SCB) specimen has been adopted to study the flexural strength and mode-I fracture toughness of flexible pavement with and without RAP. Two different resources of RAP, namely KS and KF, with two different percentages (20% and 40%) have been examined. Smooth and cracked specimens have been cast to measure the flexural strength and mode-I fracture of flexible pavement containing RAP, respectively. Smooth and cracked specimens for flexible pavement without RAP, as control beams, have been cast for the sake of comparisons. The experimental results showed that, the flexural strength and mode-I fracture toughness of flexible pavement containing 40% RAP are higher than those of flexible pavement without RAP, for the two types of RAP. Comparison between the of work of fracture calculated based on AASHTO TP 105-13 and AASHTO TP 124-16 showed that, the calculation based on AASHTO TP 105-13 is more accurate than that based on AASHTO TP 124-16. Furthermore, the suggested quad-linear relation to work of fracture predictions is easier and more reasonable for presenting the experimental results especially for the descending part of P-LLD curve than that suggested as exponential curve by AASHTO TP 124-16. For all groups, the type of failure is intergranular-mode fracture.

The traditional manual approach of pavement condition evaluation is being replaced by more sophisticated automated vehicle systems. Although these automated systems have eased and hastened pavement management processes, research is ongoing to further improve their performances. An average state road agency handles thousands of kilometers of the road network, most of which have multiple lanes. Yet, for practical reasons, these automated systems are designed to evaluate road networks one lane at a time. This requires time, energy, and possibly more equipment and manpower. Multiple Linear Regression (MLR) analysis and Artificial Neural Network (ANN) were employed to examine the feasibility of modeling and predicting pavement distresses of multiple lanes as functions of pavement distresses of a single adjacent lane. The successful implementation of this technique has the potential to cut the energy and time requirement at the condition evaluation stage by at least half, for a uniform multi-lane highway. Results showed promising model performances that indicate the possibility of evaluating a multi-lane highway pavement condition (PC) by single lane inspection. Traffic direction parameters, location, and lane matching parameters contributed significantly to the performance of the ANN PC prediction models.

Several studies have reported the effect of various additives on the moisture resistance of AC, but limited studies explored the impact of aggregate’s properties on the moisture sensitivity of AC. In this study, the influence of aggregate properties and mix’s volumetric properties on the moisture sensitivity of AC was studied. The moisture sensitivity of the AC was based on Retained Stability Index (RSI). The study utilised results from 319 plant-produced asphalt mixtures. The $RSI$ was modelled as a function of aggregates and mix’s variables using Artificial Neural Network (ANN). The variables studied include air voids $(AV)$, void in mineral aggregates $(VMA)$, clay lump $(CL)$, Los Angeles’s abrasion $(LA)$, soundness value (SV), sand equivalence value $(SEV)$, gradation and mix type. Profile method along with weight-connection relative importance ranking were employed to analyse the influence of the input variables on the $RSI$. The relationship between these variables and the $RSI$ fits higher order polynomial functions.