启发式算法优化BP神经网络的圆锥形中空夹层钢管混凝土构件轴压承载力研究

Compression bearing capacity of tapered concrete-filled double skin steel tubular members based on heuristic algorithm optimized BP neural network

摘要

为解决圆锥形中空夹层钢管混凝土（tapered concrete-filled double skin steel tubular，TCFDST）构件因截面变化导致轴压承载力计算困难的问题，将遗传算法（genetic algorithms，GA）、粒子群算法（particle swarm optimization，PSO）、模拟退火算法（simulated annealing，SA）、蚁群算法（ant colony optimization，ACO）等启发式算法应用于BP神经网络（back propagation neural network，BPNN），提出了全局和局部优化相结合的TCFDST轴压承载力预测模型。选取了共计1 362组试验和有限元模拟数据构成了TCFDST轴压数据库，采用10种构件尺寸和材料参数作为输入参数，轴心受压承载力为输出参数，分别建立了4种启发式算法优化的BPNN模型。通过对比4种经典机器学习算法、规范公式证明了启发式算法优化BP神经网络具有更高的预测精度，结合参数化分析得出了预测模型承载力与各输入参数的变化规律，验证了预测模型的泛用性。基于启发式算法优化的BPNN轴压承载力预测模型可为解决TCFDST结构承载力计算问题和推广应用提供重要参考。

Abstract

To address the computational challenges posed by the cross-sectional variations in tapered concrete-filled double skin steel tubular (TCFDST)，this paper employs four heuristic algorithms-genetic algorithms (GA)，particle swarm optimization (PSO)，simulated annealing (SA)，and ant colony optimization (ACO) to enhance a backpropagation neural network (BPNN). This paper proposes a predictive model that integrates global and local optimization strategies. A database comprising 1 362 experimental and simulation results was established，with dimensional and material parameters as inputs and axial compressive capacity as the output. Four BPNN models optimized using heuristic algorithms were developed and validated against other machine learning algorithms and current design standards；the results indicate superior predictive accuracy. Parametric analysis reveals consistent relationships between the predicted load-bearing capacity and the various input variables. The proposed method provides a reliable reference for addressing the challenges of load-bearing capacity calculations for TCFDST structures.

关键词

圆锥形中空夹层钢管混凝土 / 轴压承载力 / 启发式算法 / BP神经网络 / 机器学习

Key words

tapered concrete-filled double skin steel tubular column / compression bearing capacity / heuristic algorithm / back propagation neural network / machine learning

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基金资助

国家自然科学基金资助项目(51978570)

国网浙江省电力有限公司科技资助项目(5211JY220003)