大型结构系统可靠性分析方法研究(英文)

System Reliability Analysis of Large-Scale Offshore Platform Based on Neural Network Response Surface and Ductile Limit Theory

结构系统的可靠性评估是结构设计的一个重要研究内容,而极限状态函数的建立是进行可靠性评估的基础。但是,大型结构系统的极限状态函数极为复杂,响应面法用简单的多项式进行模拟的精度较低,导致误差较大。文章提出用神经网络替代多项式来拟合复杂的极限状态函数,形成所谓的神经网络响应面。然后,基于塑性极限理论,文中提出了不依赖于失效模式的极限状态函数表达形式及采用ICP对该极限状态函数进行计算的方法。最后,依照拟合得到的神经网络响应面,给出了大型结构系统失效概率的方法。通过两个算例计算并和其它方法进行比较,表明该方法的计算精度较高,而计算时间大大降低。

The evaluation of the failure probability of structural system is of extreme importance in structural design and the limit state function （LSF） is the key to do it. As the basis function of Response Surface （RS）, the quadratic, polynomial is too weak to approximate the real LSF for largescale structure, which is very complex. In this paper by substituting the Artificial Neural Network （ANN） for the quadratic polynomial, a Neural Network Response Surface （NNRS） was built to approximate the very complex LSF of large-scale structure accurately.Based on ductile limit analysis theory,a system LSF independent of structural system failure modes and a method to compute it with Improved Compact Procedure （ICP）were presented.By approximating this system LSF with NNRS.Finally,an evaluation method of system failure probability of large-scale structural was presented. Compared with FORM,the accuracy and efficiency of this method was demonstrated using two numerical examples.