Multi-Objective Seismic Optimization of Trilinear Disaster Identification Model Under Improved Ant Colony Algorithm

This article focuses on addressing the limitations of traditional trilinear disaster identification models in seismic optimization, and proposes a multi-objective optimization method based on an improved ant colony algorithm. By analyzing the deficiencies in existing research in detail, this study aims to develop a more efficient and precise optimization framework for improving the seismic performance of buildings. The results show that the improved algorithm has significant advantages in improving convergence speed and avoiding local optimal solutions. At the same time, the newly proposed trilinear disaster classification model can more accurately simulate the impact of earthquakes, providing an effective solution for multi-objective seismic optimization. This article evaluates the effectiveness of the improved ant colony algorithm through three key experiments. Firstly, in the evaluation of the safety factor of building structures, the improved method increased the safety factor by an average of 1.0 to 2.0 units, for example, the safety factor of Building 1 increased from 3.0 to 4.5. Secondly, cost-benefit analysis showed that the improved method increased the cost-benefit ratio by approximately 0.45 to 0.5 units, such as the proportion of Building 5 increasing from 0.6 to 1.05, demonstrating better economic benefits. Finally, in the durability evaluation experiment of seismic performance, it was found that the improved method significantly reduced performance degradation, with an average reduction of 10% to 14%. For example, the degradation of Building 1 decreased from 20% to 10%. The above data conclusions comprehensively reflect the efficiency and feasibility of the improved ant colony algorithm and the trilinear disaster identification model in multi-objective seismic optimization.