To solve the key technical problems of the laborious process and difficulty in achieving rapid structural optimization design using the traditional computational fluid dynamics (CFD) method to obtain the wind coefficient of the main beam section of port crane

a rapid prediction model for wind coefficient of crane main beam section based on convolutional neural network is proposed. The wind coefficient rapid prediction model proposed in this paper uses the free geometric deformation method to process the basic section shape to obtain the cross-section pattern set of crane main beam with rich geometric characteristics

CFD method is then used to calculate the wind coefficient corresponding to the cross-section pattern of each main girder to generate the dataset. On this basis

the prediction model is trained based on the dataset and its network structure is optimized

and the nonlinear mapping relationship between the main beam section and the wind coefficient is established. In addition

this paper further combines the prediction model with the genetic algorithm to establish an optimization design method for main beam cross-section. Taking the F11 section in the dataset as an example

the accuracy and efficiency of the optimization method are tested by using the windproof performance as the optimization objective. The test results show that the wind coefficient rapid prediction model proposed in this paper achieves the average relative error of 1.87% when predicting the wind coefficient of each main beam section. The prediction time is in the order of milliseconds

which is significantly faster compared to the traditional CFD method

showcasing a significant improvement in efficiency. The optimized F11 section of crane main beam section developed by applying the optimization design method developed in this paper is reduced by 15.89% compared with the wind coefficient before optimization. This significant reduction greatly improves the windproof performance of the main beam section

which proves the reliability of the optimization method proposed in this paper. It can be used as a new method for the optimization design and rapid selection of crane main beam section structure.