AI-FREE OPTIMIZATION OF SUSTAINABLE PAVEMENT MATERIALS FOR COST-EFFICIENT HIGHWAY CONSTRUCTION: A LIFECYCLE-BASED ENGINEERING APPROACH

Abstract

The serious degradation of road infrastructure and the demand for green pavement materials and lifecycle cost-effective solutions have prompted the embracing of green pavement materials and lifecycle cost-effective practices. The research explores optimization of blends of sustainable road materials through an approach having a materials engineering method with no application of artificial intelligence strategies. Five asphalt mixes with various percentages (0%–40%) of industrial by-products and recycled materials like Reclaimed Asphalt Pavement (RAP), Fly Ash, and Ground Granulated Blast Furnace Slag (GGBS) were tested as part of a systematic experimental program. A range of laboratory tests including Marshall Stability, Indirect Tensile Strength, Rutting Resistance, and Fatigue Life for determining mechanical properties and durability were conducted. In addition to the mechanical testing, Lifecycle Cost Analysis (LCA) and an Environmental Lifecycle Assessment (LCA) were performed to identify long-term economic and environmental effects. Outcomes indicate 30% sustainable content blends with improved balance when performance, cost, and environmental performance metrics are taken into account. The use of Multi-Criteria Decision Analysis (MCDA) identified the 30% mix as the most suitable composition that facilitated up to 22% cost saving and 35% CO₂ reduction against the control blend. This study provides data-based, reproducible framework for highway authorities to make infrastructure more sustainable without using AI software, advancing global sustainable development objectives.