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<p>Based on TRIZ theory, a multi-level optimization design method for the beam structures of a plug-in full-carrying bus frame was proposed. In the first level of optimization, the material of the four surrounding frames was replaced from steel to aluminum, and the relative sensitivity analysis (RSA) and CRITIC weighted technique for order preference by similarity to ideal solution (TOPSIS) decision-making method was employed to optimize the thickness and shape of the cross-sections with the goal of improving performance. In the second level of optimization, the progressive relative sensitivity analysis (PRSA), optimal Latin hypercube sampling (OLHS), Gaussian process regression (GPR) model, multi-objective particle swarm (MPSO) algorithm, and adaptive point-adding (APA) strategy were used to perform multi-objective optimization design on the beam structures of the underbody frame. After two levels of optimization, the weight of the bus frame was reduced by 14.8%, and all performance indicators met the design requirements, verifying the effectiveness of the proposed method. The method proposed in this study is applicable to multi-objective optimization problems with complex structural systems and expensive computational cost.</p> Graphical Abstract <p></p>

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Multi-level structural optimization design of beam structures in bus frame

  • 期刊论文

  • 发表日期：

    2026年7月28日

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  • Wenchao Xu,
  • Jing Chen,
  • Aotian Tang,
  • Dengfeng Wang

摘要

Based on TRIZ theory, a multi-level optimization design method for the beam structures of a plug-in full-carrying bus frame was proposed. In the first level of optimization, the material of the four surrounding frames was replaced from steel to aluminum, and the relative sensitivity analysis (RSA) and CRITIC weighted technique for order preference by similarity to ideal solution (TOPSIS) decision-making method was employed to optimize the thickness and shape of the cross-sections with the goal of improving performance. In the second level of optimization, the progressive relative sensitivity analysis (PRSA), optimal Latin hypercube sampling (OLHS), Gaussian process regression (GPR) model, multi-objective particle swarm (MPSO) algorithm, and adaptive point-adding (APA) strategy were used to perform multi-objective optimization design on the beam structures of the underbody frame. After two levels of optimization, the weight of the bus frame was reduced by 14.8%, and all performance indicators met the design requirements, verifying the effectiveness of the proposed method. The method proposed in this study is applicable to multi-objective optimization problems with complex structural systems and expensive computational cost.

Graphical Abstract