Evaluating the influence of road construction on landslide susceptibility in Saudi Arabia's mountainous terrain: a Bayesian-optimised deep learning approach with attention mechanism and sensitivity analysis.

In the mountainous region of Asir region of Saudi Arabia, road construction activities are closely associated with frequent landslides, posing significant risks to both human life and infrastructural development. This highlights an urgent need for a highly accurate landslide susceptibility map to guide future development and risk mitigation strategies. Therefore, this study aims to (1) develop robust well-optimised deep learning (DL) models for predicting landslide susceptibility and (2) conduct a comprehensive sensitivity analysis to quantify the impact of each parameter influencing landslides.

Slag and Bagasse Ash: Potential Binders for Sustainable Rigid Pavement.

The current study tries to cut carbon emissions by using various waste materials in place of cement, including sugarcane bagasse ash (SCBA), ground granulated blast furnace slag (GGBFS), and ladle furnace slag (LFS), individually and in a combined form also, which has not been studied yet. In the same context, effort was made to utilize the maximum amount of waste materials as the replacement of cement to create a sustainable environment. Besides this, another aim is checking the performance of these waste materials as binding materials with respect to compressive strength for sustainable rigid pavement construction without activating them or using any activating solution.