Optimization of Large-Span Spatial Building Structures Based on Kalman Filter and Improved Genetic Algorithm

Xiangfeng Chen; Jianjun  Xu

doi:10.56748/ejse.24805

Authors

Xiangfeng Chen Longdong University https://orcid.org/0009-0009-1124-0185
Jianjun Xu Engineering Project Department of the Second Oil Production Plant of Changqing Oilfield

DOI:

https://doi.org/10.56748/ejse.24805

Keywords:

Genetic algorithm, Kalman filter, Artificial neural network, Influence matrix, Particle swarm optimization, Large-span spatial

Abstract

Large-span spatial building structures are complex and face challenging environments after construction. Under external impacts, vibrations, and wind and snow loads, certain structural components may undergo deformation. Therefore, optimizing structural engineering during construction is essential. This study proposes an optimization model for large-span spatial building structures by integrating Genetic Algorithm, Kalman Filter, Influence Matrix, and Particle Swarm Optimization. Experimental results show that the proposed algorithm achieves the lowest tracking frequency and phase mean square error, with a loop convergence time of only 0.3s and a frequency tracking error of 15Hz. In practical applications, the optimized cable force values are reduced by an average of 61kN compared to the original values, and the average bending stress decreases by 5.9MPa. The mean error of model-reconstructed displacement is 3.3% and 3.8%, achieving the highest reconstruction accuracy. The experimental data demonstrate that the proposed model exhibits superior performance in real-world optimization, contributing to large-span spatial building structures by ensuring safety and improving construction efficiency.

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