DEVELOPMENT AND PERFORMANCE EVALUATION OF AI-Optimised SMART AND ADAPTIVE HYBRID MATERIALS FOR CLIMATE-RESILIENT INDUSTRIAL APPLICATIONS

Climate change continues to challenge the performance and durability of industrial materials, necessitating the development of advanced systems with adaptive and self-regulating capabilities. This study presents the development of smart and adaptive materials for climate-resilient industrial applications through the integration of nanoparticle reinforcement, self-healing polymers, phase change materials (PCMs), embedded sensing systems, artificial intelligence (AI), and digital twin technology. The developed materials were evaluated for mechanical strength, thermal performance, self-healing efficiency, and overall resilience. Experimental results showed notable improvements in tensile strength (50%), compressive strength (41.5%), elastic modulus (61.9%), and fracture toughness (60.7%) compared with conventional materials. Thermal analysis revealed enhanced heat absorption and reduced thermal conductivity, while self-healing efficiencies ranged from 85% to 92%, indicating strong autonomous repair capability. Performance evaluation using the Material Resilience Index (MRI) demonstrated a progressive increase from 0.45 for the control material to 0.94 for the AI-optimised hybrid smart material. Statistical analyses using ANOVA, Tukey HSD, and regression confirmed that the observed improvements were highly significant (p < .001), with a strong model fit (R² = 0.94). Additionally, an Artificial Neural Network model achieved a predictive accuracy of R² = 0.93, validating the effectiveness of AI-driven optimization. The findings demonstrate that integrating smart material systems with AI and digital twin technologies provides a robust pathway for developing durable, adaptive, and climate-resilient industrial materials.