Design parameter optimization method for a prestressed steel structure driven by multi-factor coupling

Abstract

To achieve efficient structural design, it is crucial to reduce the cost of materials while ensuring structural safety. This study proposes an optimization method for design parameters (DPs) in a prestressed steel structure driven by multi-factor coupling. To accomplish this, a numerical proxy model of prestressed steel structures is established with integration of DPs and mechanical parameters (MPs). A data association-parameter analysis-optimization selection system is established. A correlation between DPs and MPs is established using a back propagation (BP) neural network. This correlation provides samples for parameter analysis and optimization selection. MPs are used to characterize the safety of the structure. Based on the safety grade analysis, the key DPs that affect the mechanical properties of the structure are obtained. A mapping function is created to match the MPs and the key DPs. The optimal structural DPs are obtained by setting structural materials as the optimization objective and safety energy as the constraint condition. The theoretical model is applied to an 80-m-span gymnasium and verified with a scale test physical model. The MPs obtained using the proposed method are in good agreement with the experimental results. Compared with the traditional design method, the design cycle can be shortened by more than 90%. Driven by the optimal selection method, a saving of more than 20% can be achieved through reduction of structural material quantities. Moreover, the cross-sectional dimensions of radial cables have a substantial influence on vertical displacement. The initial tension and cross-sectional size of the upper radial cable exhibit the most pronounced impact on the stress distribution in that cable. The initial tension and cross-sectional size of the lower radial cable hold the greatest sway over the stress distribution in that cable.

Graphical abstract

Keywords

structure design / association relationship / performance analysis / optimum selection / experimental verification

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Acknowledgements

The authors gratefully acknowledge the financial support provided by the National Natural Science Foundation of China (Grant No. 5217082614).

Competing interests

The authors declare that they have no competing interests.

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