Genetic mutation-based swarm intelligence for structural design optimization of space framed structures

The study aimed at developing a Binary coded Genetic Mutation of Unified Particle Swarm Optimization (BCGM-UPSO) algorithm for structural design optimization of reinforced concrete (RC) space framed structures while adhering to safety, serviceability, and construction feasibility criteria. The study focuses on optimizing the design of two RC space framed structures (G+8 and G+10 stories) considering seismic loads (IS 1893 Part-I) and wind loads (IS 875 Part-III). Structural analysis of the space framed structures is carried out to derive design loads for beams and columns. GA-UPSO is implemented in MATLAB for cost optimization, incorporating material costs (formwork, concrete, reinforcement) and constraints like IS 456 safety and serviceability requirements. The algorithm also addressed reinforcement feasibility during construction, including curtailment requirements. Numerical analyses confirmed the accuracy of BCGM-UPSO in producing cost-optimized designs. Monte Carlo simulations demonstrated the robustness and consistency of the algorithm. The developed optimized design of multistorey space framed structure is directly applicable to construction projects, offering significant cost savings while ensuring compliance with structural and construction standards. This study demonstrates the practical utility of evolutionary mutation on swarm intelligence techniques, specifically UPSO, in addressing complex engineering challenges, such as cost-effective and feasible design of RC structures under realistic loading and design constraints. The BCGM - UPSO algorithm presents a robust and consistent approach to achieving cost-efficient designs for multistorey RC space framed structures, ready for immediate implementation in construction projects without further modifications.