Optimization of Reinforced Concrete Frames using Artificial Intelligence

The design of reinforced concrete (RC) frames is a complex and iterative process that involves numerous variables such as load-bearing capacity, material strength, and safety standards. Traditional methods often fail to efficiently navigate this complexity due to their reliance on predefined assumptions and linear models. The optimization of RC frames, therefore, remains a critical challenge for structural engineers. This paper proposes the use of Artificial Intelligence (AI) techniques, specifically genetic algorithms (GA), particle swarm optimization (PSO), and artificial neural networks (ANN), to optimize the design of reinforced concrete frames. The AI-based system seeks to minimize material usage while ensuring structural integrity, safety, and performance. A comparison of the AI-based optimization system with traditional methods is performed to demonstrate its superiority in terms of computational efficiency, cost reduction, and overall design optimization. The proposed system can be employed in real-time design applications to create more sustainable and cost-effective reinforced concrete structures