Comprehensive multi-objective optimization framework for sustainable urban flyover design: integrating radial basis function metamodeling and advanced optimization algorithms

Introduction Urban flyover infrastructure requires balancing structural efficiency, cost minimization, and environmental sustainability. Traditional deterministic methods fail to capture variability and reproducibility in large-scale civil projects. Methods A comprehensive framework was developed integrating Radial Basis Function (RBF) surrogate modeling, sensitivity and uncertainty analysis, and comparative evaluation of nine optimization algorithms (NSGA-II, NSGA-III, DE, CMA-ES, GA, ES, SRES, Nelder-Mead, Pattern Search). The dataset included 47 flyover configurations across 12 Indian cities. Results The RBF surrogate reduced computational effort by 95–98% while maintaining predictive accuracy (R 2 > 0.95). Application to the Aurangabad interlinking flyover project achieved cost savings of 8–12%, environmental impact reductions of 15–25%, and material efficiency improvements of 20–30%. Soil bearing capacity and traffic volume accounted for 58% of cost variance. Discussion The integration of surrogate modeling and multi-algorithm optimization advances sustainable infrastructure design by providing robust, reproducible, and evidence-based solutions. This framework demonstrates practical relevance for balancing economic viability, structural adequacy, and environmental responsibility in urban flyover development.