Predictive multi-criteria decision-support framework for urban flyover design: integrating structural, economic, and environmental constraints

Abstract

Urban flyover design involves complex multi‐objective optimization that must balance structural requirements, economic viability, and environmental sustainability. We developed a predictive decision‐support framework using multivariate radial basis functions (MRBF) with Gaussian kernels to model interdependencies among design parameters. Data from 47 flyover configurations across 12 Indian cities were analyzed, with independent variables including traffic volume, terrain characteristics, urban density, and soil bearing capacity. The framework achieved prediction accuracy of 91.4% (R 2 = 0.914) for construction cost estimation and 88.7% (R 2 = 0.887) for environmental impact assessment. Sensitivity analysis revealed soil bearing capacity and traffic volume accounted for 58% of cost variance, while urban density contributed 42% to environmental impact predictions. Case studies from Delhi, Mumbai, and Bengaluru demonstrated applicability across diverse contexts. The methodology enables rapid preliminary design evaluation, reducing iteration time by 63% while ensuring compliance with IRC:6‐2017 safety standards and sustainability criteria. The framework provides decision‐makers with quantitative tools for trade‐off analysis in constrained urban environments.