Multi-objective optimization design of steel cross section of integrated supports and hangers based on NSGA-II and MDOS

The stability and safety of building Mechanical, Electrical, and Plumbing (MEP) systems are significantly influenced by the steel section design of integrated supports and hangers. Traditional design methods, reliant on empirical steel section selection, are often inefficient and can result in unnecessary material usage. This paper introduces a novel multi-objective optimization approach that enhances the section design process by minimizing material costs and maximizing bending performance of supports and hangers. Firstly, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to identify an array of optimal cross-sectional designs that reconcile economic considerations with structural mechanical performance. Subsequently, in order to pinpoint the most suitable design from the resulting Pareto-optimal scheme, the Method of Determining the Optimal Scheme (MDOS) is proposed. MDOS harnesses expert scoring, the Continuous ordered weighted averaging (COWA) operator, game theory, and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to objectively evaluate and prioritize design schemes according to stakeholder preferences and project-specific criteria. Finally, the optimization results are analyzed and the influence of different spacing of supports and hangers and different subjective weight factors on the selection of cross-section is studied. The findings reveal that the method can precisely determine all cross-section designs that adhere to the established constraints, achieving a 4.09-fold enhancement in design efficiency. It was observed that the two outlined objectives are predominantly affected by the height of the web. The distribution trend of the optimal solution value of the independent variable is almost the same under the condition of different values of the supports and hangers spacing. With the change of subjective weight, the cross section selection focuses on three optimal schemes. Therefore, the proposed method can be used as reference for similar steel design optimization.