Optimization of Aluminum Alloy Formwork Geometry Parameters Based on a PSO-BP Neural Network

Abstract

To assist in addressing the problem where an aluminum alloy formwork (AAF) deforms more greatly under the action of lateral pressure and therefore does not meet the requirements of plaster-free engineering, we propose a method for determining the geometric parameters of this formwork based on a PSO algorithm and BP neural network with ABAQUS as the platform. The influence of six geometric parameters of the formwork on the maximum deflection value of the panel under the action of lateral pressure is studied using finite element analysis. The maximum deflection value of the panel is used as the index, and the influence of each factor is analyzed with an orthogonal test, and a set of optimal geometric parameters is obtained via extreme difference analysis and analysis of variance. The sample data are obtained via finite element simulation, and the PSO-BP neural network model is established using the six factors of the orthogonal test as input values and the maximum deflection of the panel as the output value, and the optimal geometric parameters are optimized using the PSO algorithm. The results indicate that the maximum deflection for the panel in the orthogonal scheme is 1.446 mm. The PSO-BP neural network prediction model demonstrates greater accuracy and a 31.74% reduction in running time compared to the BP neural network prediction model. The optimized PSO-BP neural network prediction model scheme reveals a maximum panel deflection of 1.296 mm, a 10.37% decrease compared to the orthogonal solution. These findings offer technical guidance and a foundation for optimizing AAF designs, presenting practical applications.

Intelligent Building Construction Cost Prediction Based on BIM and Elman Neural Network

“…The research algorithm is compared with the ENN, BP neural network [29], LSTM algorithm [30], and the model algorithm proposed by Li et al (2023) in terms of tting effect, root mean squared error (RMSE), and determination coe cient (R 2 ) in order to assess the performance of the model built in this paper.…”