Optimization of Flyover Structural Design Using Genetic Algorithm and Multi-Objective Constraints

Abstract

The increasing demand for efficient urban transportation infrastructure has emphasized the need for optimized flyover structural designs that ensure safety, cost-effectiveness, durability, and efficient material utilization. Conventional structural design approaches often rely on iterative manual procedures that may not effectively address multiple conflicting design objectives and constraints simultaneously. This research presents an optimization framework for flyover structural design using genetic algorithms and multi-objective constraints to improve structural efficiency and overall design performance. The proposed study employs genetic algorithm–based optimization techniques integrated with structural analysis methods to evaluate and optimize key design parameters such as beam dimensions, reinforcement configuration, material selection, span arrangement, and load distribution characteristics. Multi-objective optimization criteria, including structural weight minimization, cost reduction, stress limitation, displacement control, and safety enhancement, are incorporated into the optimization model to achieve balanced design solutions. Finite element analysis and numerical simulation techniques are utilized to assess the structural behavior of flyover systems under varying traffic loads, environmental conditions, and dynamic operational scenarios. Performance evaluation is conducted using parameters such as structural stability, material efficiency, load-carrying capacity, deflection response, construction cost, and computational optimization efficiency. Simulation results demonstrate that the proposed genetic algorithm–based optimization framework significantly improves design efficiency, reduces material consumption, and enhances structural performance compared with conventional design methodologies.