Multiobjective optimization of suspension bridges via coupled modeling and dual population multiobjective particle...

Area of Science:

• Structural Engineering
• Computational Mechanics
• Optimization Algorithms

Background:

• Traditional suspension bridge main cable shaping methods lack automated shape-finding during optimization.
• Finite element (FE) modeling efficiency is a challenge in complex structural optimization.

Purpose of the Study:

• To develop an automated shape-finding method for suspension bridge main cables.
• To enhance the efficiency and effectiveness of multi-objective optimization for suspension bridges.
• To reduce material costs and improve safety factors in bridge design.

Main Methods:

• Integration of a bisection-parabolic (BP) method with finite element (FE) modeling.
• Development of a multi-objective optimization model using layout and dimension parameters.
• Application of Lasso function for elastic net regression analysis.
• Proposal of a dual-population coevolutionary multi-objective particle swarm optimization algorithm (DPMOPSO), incorporating NSGA-II and MOPSO.

Main Results:

• The proposed BP method enhances FE modeling efficiency during optimization.
• The DPMOPSO algorithm effectively escapes local optima.
• The study demonstrates improved multi-objective optimization performance for suspension bridge design.

Conclusions:

• The bisection-parabolic method offers an efficient approach to suspension bridge main cable shaping.
• The DPMOPSO algorithm provides a robust solution for complex multi-objective structural optimization problems.
• This research contributes to more cost-effective and safer suspension bridge designs.