Previous studies indicate that traditional asphalt mixtures lack the ability to withstand the stresses caused by heavy traffic volumes under high temperatures. To enhance the rutting resistance of flexible pavement under high levels of temperature and loading, extensive laboratory experiments were carried out. A 60/70 grade bitumen was used as a neat sample for comparison. The study introduced three distinct polymers, polypropylene (PP), low-density polyethylene (LDPE), and acrylonitrile butadiene styrene (ABS), at varying concentrations by weight into the neat bitumen. Initially, conventional tests were performed to evaluate the conventional properties of both the neat and modified bitumen, while aggregate tests assessed the mechanical properties of the aggregates. Subsequently, a Marshall mix design was performed to determine the optimum bitumen content (OBC) in the asphalt mixture. Finally, wheel-tracking tests were performed under a specific load and temperature to investigate the rutting behavior of the modified asphalt mixtures. The results of this comprehensive study revealed that the modified asphalt mixtures displayed improved resistance to rutting compared to the neat asphalt mixture. Furthermore, it was also observed that the LDPE exhibited a superior performance against rutting, followed by the PP and ABS. At polymer contents of 3%, 5%, and 7%, the LDPE achieved reductions in rut depth of 13%, 24%, and 33%, respectively, outperforming both PP- and ABS-modified asphalt. These findings not only enhance our understanding of asphalt behavior under diverse conditions but also highlight the potential of plastic-modified asphalt as an effective solution for mitigating rutting problems in road pavements. By incorporating plastic modifiers into asphalt mixtures, this approach aligns with the principles of sustainable construction by reducing plastic waste while improving pavement durability and performance.

Background: The East African Fracture Zone (EAFZ) stands as a testament to the dynamic forces of Earth, marked by heightened seismic activity that frequently triggers geotechnical disasters such as landslides and rockfalls. Traditionally, the study of earthquake-induced geological risks has been reactive, with a focus on post-incident analysis. While significant advances have been made in spatial analysis and risk mapping, the capabilities for real-time prediction and proactive mitigation are still limited. Objectives: Current study presents an approach to predicting earthquake-induced landslides and rockfalls in the EAFZ. The aim is to change the perception of the problem by viewing disasters as manageable risks and informing decision-making in urban planning and disaster mitigation strategies. Methods: A combination of geotechnical engineering, remote sensing, artificial intelligence and machine learning, and socio-economic analysis were used to develop a holistic software framework that solves the complex problem of earthquake prediction without any problems. Results: A software model has been developed that includes a dynamic learning component that refines its predictions with new data, allowing for a deeper understanding of geological subtleties and socio-economic impacts. Considerable attention is paid to the tangible consequences of landslides and rockfalls, including human, property and economic losses. Despite the inevitable challenges of data accuracy and natural unpredictability, the proposed approach opens up new possibilities for proactive disaster management. The results demonstrate a transformational step in data-driven geotechnics and highlight the global applicability of the methods proposed in this work. Conclusion: In this investigation, was taken a pioneering stride in the realm of geotechnical hazard analysis and prediction, focusing on the complex terrains of the East African Fracture Zone (EAFZ). The results provided critical insights into the dynamics of geotechnical hazards in the EAFZ, laying the foundation for future innovations and enhanced safety measures in vulnerable communities.