Two-stage optimization method for design of reinforced concrete frames using optimal pre-determined section database and non-revisiting genetic algorithm

Abstract

This paper proposes a novel two-stage optimization method for the design of reinforced concrete (RC) frames that aims to overcome the limitations of existing optimization methods. The proposed method combines a section-based database schema (SBDBS) and a non-revisiting genetic algorithm (NrGA) to enhance the efficiency and effectiveness of the optimization process. In the first stage, the SBDBS utilizes a multi-objective brute force search technique based on non-dominated sorting to generate an optimal pre-determined section list for RC frame members, considering design constraints and cost-effectiveness. In the second stage, the NrGA optimizes the overall structural design by considering the total cost of the structure. To demonstrate the effectiveness of the proposed method, four design examples with 4 to 16 stories are presented, and the method is developed based on ASCE 7-16 and ACI 318-19. The results show that the proposed method outperforms the traditional method that uses non-optimal pre-determined lists. The proposed method is shown to converge faster, up to 75% for a 16-story frame, and attain optimal solutions with fewer evaluations of the objective function, resulting in more efficient and effective optimization. It is also shown that the presented method is more stable in obtaining optimal solutions by improving the standard deviation of results for independent optimizations by 67 to 100%. By using an optimal pre-determined section list tailored to the specific design problem, the proposed method can increase the probability of finding high-performing solutions, reduce the likelihood of getting stuck in local optima, and result in significant improvements in optimization performance. This method has broad potential for impact in the field of structural optimization, improving the efficiency and accuracy of design optimization while also enhancing safety and cost-effectiveness.