RELIABILITY ANALYSIS OF CYLINDRICAL SHELL STRUCTURES BASED ON BPNN OPTIMIZED BY GENETIC ALGORITHM
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摘要:
针对水下爆炸载荷作用下的圆柱结构失效概率分析问题，提出基于遗传算法优化的BP神经网络和Monte Carlo相结合的方法建立代理模型，采用混合学习函数，寻找新训练点进行模型更新；通过数值算例验证算法的计算效率和精度，实现对水下爆炸载荷作用下圆柱壳结构的高效率高精度分析。结果表明：基于遗传算法优化的BP神经网络结合蒙特卡洛法可以明显提高计算效率，可应用于圆柱壳结构在水下爆炸载荷作用下的可靠性分析，研究结果对于结构的风险评估以及安全设计具有参考意义。
Abstract:Aiming at the failure probability analysis of cylindrical structures under underwater explosion load, a surrogate model was established by combining BPNN optimized by genetic algorithm and Monte Carlo Simulation, and hybrid learning function was adopted to find the new training point for model updating. The calculation efficiency and accuracy of the algorithm were verified by numerical examples, and the high-efficiency and high-precision analysis of cylindrical shell structures under underwater explosion load was realized. The results show that the BPNN optimized based on genetic algorithm combined with Monte Carlo Simulation method can significantly improve the computational efficiency, and can be applied to the reliability analysis of cylindrical shell structures under underwater explosion load. The research results have reference significance for the risk assessment and safety-based design of structures.
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图 17 基于GA-BP-MCS方法圆柱壳结构失效概率
Figure 17. Failure probability of cylindrical shell structure based on GA-BP-MCS method
表 1 不同算法计算结果对比
Table 1 Comparison of calculation results of different algorithms
半径 方法 调用次数/训练样本量 失效概率 c=3 MCS[24] 1.2×105 3.35×10−3 AK-MCS[24] 10+85(105) 3.52×10−3 Meta-IS-AK[24] 48+600(—) 3.54×10−3 GA-BP-MCS 50+101(50 000) 3.50×10−3 c=4 MCS[24] 4.60×106 8.68×10−5 AK-MCS[24] 10+106(106) 8.79×10−5 Meta-IS-AK[24] 64+600(—) 8.60×10−5 GA-BP-MCS 50+47(33 000) 8.30×10−5 注： 括号内数字表示样本池的样本数量；“—”表示无样本池。 
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