The transport characteristics of waste glass powder incorporated self-compacting concrete (SCC) for a number of different durability indicators are reported in this paper. SCC mixes were cast at a water to binder ratio of 0.4 using glass powders with a mean particle size of 10, 20 and 40 µm and at cement replacement levels of 20, 30 and 40%. The oxygen permeability, electrical resistivity, porosity and chloride diffusivity were measured at different ages from 3 to 545 days of curing. The amount and particle size of the incorporated waste glass powder was found to influence the durability properties of SCC. The glass incorporated SCC mixes showed similar or better durability characteristics compared to general purpose (GP) and fly ash mixes at similar cement replacement level. A significant improvement in the transport properties of the glass SCC mixes was observed beyond 90 days.

The effect of replacement of Portland cement using glass powder on fresh and mechanical properties of self-compacting concrete (SCC) was investigated in this study. A standard SCC mix design was used with substitution with either fly ash or glass powder of different fineness and replacement levels. Compressive strength, splitting tensile strength, and elastic modulus were measured at 7, 28, 90, 180, 365, and 545 days. Rheological testing found that SCC containing glass powder had consistently higher yield shear stress than the control despite testing being conducted at optimum slump flow levels. Replacement of Portland cement with glass powder was found to reduce strength performance in the shorter-term but longer-term strengths were comparable. Tensile strength and elastic modulus of SCC containing glass powder were consistent with design guidelines and can be reliably predicted from compressive strength as is typically used in structural design.

The increasing complexity of global logistics networks has necessitated integrating advanced technologies to enhance transportation efficiency, reliability, and sustainability. This study investigated the application of geospatial Artificial Intelligence (AI) and machine learning to optimize transportation routes across urban, regional, and global networks. Using a quantitative research design, operational datasets from logistics firms were combined with satellite imagery and geospatial data to evaluate the effectiveness of AI-enabled predictive models, including convolutional neural networks (CNN) and reinforcement learning algorithms. Results demonstrated significant reductions in travel time and route distances, improved on-time delivery, and enhanced fuel efficiency, indicating operational and environmental benefits. Predictive models accurately forecast congestion and dynamically adjust routes, thereby enhancing reliability across diverse operational contexts. Furthermore, the study highlighted the potential of geospatial AI to reduce carbon emissions, aligning logistics operations with sustainability objectives. Challenges related to data quality, computational complexity, and model interpretability were identified, emphasizing the need for explainable AI solutions. Overall, the findings provide empirical evidence that AI-integrated geospatial analytics can transform traditional logistics networks into intelligent, adaptive, and environmentally conscious systems. The study offers practical insights for logistics managers and policymakers seeking to implement AI-driven optimization strategies while advancing sustainable supply chain management.