摘要
为了提高响应面法的计算效率和精度,对现有的全局响应面法进行了改进。采用径向基函数(RBF)神经网络,代替目前常用的BP网络,迭代过程中在超锥体的范围内构造响应面,逼近隐式的非线性功能函数。数值算例分析表明,与通常的响应面法相比,改进全局响应面法的迭代次数和有限元分析次数均大幅减少,节省机时,有利于提高计算精度。同时,以浅基础和挡土墙为工程背景进行可靠度分析,表明该方法能以较快的收敛速度和较少的有限元分析次数,获得较高精度的可靠度指标,适用于岩土工程的可靠度分析。
In order to enhance computational efficiency and precision of response surface methods, the global response surface method is improved. Using RBF neural network substitutes BP network as the approximation of implicit performance function, and the response surface is formed in the sphere of hyper-pyramid at iteration step. Compared with other response surface methods, numerical case studies show that the proposed method not only markedly reduces the times of iterations and finite element analysis to shorten the processing time, but also benefits to better accuracy. Moreover, the engineering examples of shallow foundation and retaining wall are analyzed on their structural reliability. The calculation results show that the proposed method may gain reliability index satisfyingly by quicker convergence rate and less machine time, and to be suitable for reliability analysis in geotechnical engineering.
出处
《岩土力学》
EI
CAS
CSCD
北大核心
2008年第2期370-374,共5页
Rock and Soil Mechanics
基金
国家自然科学基金资助项目(No.50378034)
高校博士点专项基金资助项目(No.20030532020)
关键词
可靠度分析
响应面
RBF神经网络
隐式功能函数
岩土工程
reliability analysis
response surface
RBF neural network
implicit performance function
geotechnical engineering
