Nanomaterials enhance the performance of both asphalt binders and asphalt mixtures. They also improve asphalt durability, which reduces resource consumption and environmental impact in the long term associated with the production and transportation of asphalt materials. Thus, this paper studies the effectiveness of Nano Calcium Carbonate (Nano CaCO₃) and Nano Hydrated Lime (NHL) as modifiers and examines their impact on ranges from 0% to 10% through comprehensive laboratory tests. Softening point, penetration, storage stability, viscosity, and mass loss due to short-term aging using the Rolling Thin Film Oven Test (RTFO) were performed on asphalt binders. Results indicated a significant improvement in binder stiffness, particularly at 4% Nano CaCO₃ and 6% NHL content by weight. Dynamic Shear Rheometer (DSR) tests further revealed substantial improvements in rutting resistance, with NHL exhibiting superior high-temperature stability and a notable increase in the rutting factor. Marshall stability tests on asphalt concrete (AC) mixtures showed a 22.3% increase in stability with 6% NHL by weight, surpassing the 20.2% improvement observed with Nano CaCO₃ and indicating enhanced load-bearing capacity. The resilient modulus of the mixtures consistently increased with the addition of NHL, suggesting improved durability in rutting. Moisture susceptibility tests revealed that NHL significantly enhances moisture resistance, exceeding the 80% TSR benchmark at just 2% content by weight and reaching an impressive 94.6% at 10% content by weight. In contrast, Nano CaCO₃ demonstrated a more gradual improvement, achieving an 88.2% TSR at 10% content. Furthermore, permanent deformation analysis indicated a 68.64% improvement in rutting resistance with 10% NHL content by weight, exceeding Nano CaCO₃’s improvement rate. Optimal fatigue resistance was achieved at 4% for Nano CaCO₃ and 6% for NHL by weight, with respective CT index improvements of 30% and 35.4%, showing NHL’s consistent benefits across various nanomaterial contents. Overall, the study suggests that both Nano CaCO₃ and NHL positively impact asphalt performance, with NHL offering more pronounced benefits across a range of properties. These findings provide valuable insights for pavement engineers and underscore NHL’s potential as an effective additive in asphalt mixture design. Real-world applications and validations are essential for a comprehensive understanding of these nanomaterials in practical pavement engineering scenarios.

Developing a responsive pavement-management infrastructure system is of paramount importance, accentuated by the quest for sustainability through adoption of the Road Traffic Management System. Technological advances have been witnessed in developed countries concerning the development of smart, sustainable transportation infrastructure. However, the same cannot be said of developing countries. In this study, the development of a pavement management system at network level was examined to contribute towards a framework for evaluating a Pavement Quality Index and service life capacity. Environmental surface response models in the form of temperature and moisture variations within the pavement were applied, using sensor devices connected to a data cloud system to carry out mathematical analysis using a distinctive mesh analysis deformation model. The results indicated variation in the Resilient Modulus of the pavement, with increasing moisture content. Increase in moisture propagation increased saturation of the unbound granular base which reduced the elastic modulus of the sub-base and base layer and reduced the strength of the pavement, resulting in bottom-up cracks and cracking failure. The horizontal deformation reduced, indicating that the material was experiencing work hardening and further stress would not result in significant damage. Increasing temperature gradient resulted in reduced stiffness of the asphalt layer. In tropical regions, this can result in rutting failure which, over time, results in top-down cracks and potholes, coupled with increasing moisture content.

Portland Cement Concrete (PCC) pavement was studied with incorporation of an environmentally friendly eco-additive, sodium acetate (C₂H₃NaO₂). This additive was added to PCC pavement in three different percentages of 2%, 4% and 6% of binder weight. For a comprehensive elucidation of the eco-additive incorporation on the performance of PCC pavement, casted samples were cured in three different environments, namely: water, outdoors and pond water. Water absorption tests, flexural and compressive strength tests after 7 and 28 days of curing were conducted and results compared with the control samples without any addition of sodium acetate. Results demonstrated a significant improvement in the impermeability, compressive strength and flexural strength of PCC pavement when sodium acetate concrete is cured in a water bath and outdoors. However, no/little improvement in the impermeability, compressive strength and flexural strength was observed in sodium acetate samples that were cured in pond water. Microstructural analysis of treated samples by using scanning electron microscopy (SEM) illustrated the strengthening effect that sodium acetate provides to the pore structure of concrete pavement.

Distress measurement is essential in pavement management. Image-based distress identification is increasingly becoming an integral part of traffic speed network-level road condition surveys. This allows an aggregated summary of road conditions over the whole network, so it does not require an exact distress location within the lane. In this context, multi-label classification (MLC), based on convolutional neural networks (CNN), is proposed as a potential solution for distress identification from a network-level right-of-way (ROW) video survey. MLC has the advantage of low computing resource consumption, as it is implemented from lightweight classification networks. In this work, the developed MLC models used three different CNN architectures (VGG16, ResNet-34, and ResNet-50) to detect potholes, cracks, patches, and bleeding. The best model obtained 97% average accuracy with an F1-score of 93% in distress identification despite the variability in imaging hardware. This makes it possible to generalize the classification algorithm, allowing versatile applications and incorporating it into network-level pavement management systems. This model has good potential for fast and accurate distress identification from a video survey, avoiding the need for various types of expensive sensors like laser scanners.