Support vector machine （SVM） was introduced to analyze the reliability of the implicit performance function, which is difficult to implement by the classical methods such as the first order reliability method （FORM...Support vector machine （SVM） was introduced to analyze the reliability of the implicit performance function, which is difficult to implement by the classical methods such as the first order reliability method （FORM） and the Monte Carlo simulation （MCS）. As a classification method where the underlying structural risk minimization inference rule is employed, SVM possesses excellent learning capacity with a small amount of information and good capability of generalization over the complete data. Hence, two approaches, i.e., SVM-based FORM and SVM-based MCS, were presented for the structural reliability analysis of the implicit limit state function. Compared to the conventional response surface method （RSM） and the artificial neural network （ANN）, which are widely used to replace the implicit state function for alleviating the computation cost, the more important advantages of SVM are that it can approximate the implicit function with higher precision and better generalization under the small amount of information and avoid the ＂curse of dimensionality＂. The SVM-based reliability approaches can approximate the actual performance function over the complete sampling data with the decreased number of the implicit performance function analysis （usually finite element analysis）, and the computational precision can satisfy the engineering requirement, which are demonstrated by illustrations.展开更多
An efficient computational method is suggested for the first-excursion reliability assessment of nonstationary process. In the proposed method, the nonlinear performance function is Linearized at the Hasofer-Lind poin...An efficient computational method is suggested for the first-excursion reliability assessment of nonstationary process. In the proposed method, the nonlinear performance function is Linearized at the Hasofer-Lind point obtained by an iterative algorithm. The problem of the nonstationary processes is solved by the discrete-time method, in which the precision can be controlled by choosing the steps of discretization. The derived formulae can be conveniently degraded to calculate both the first-excursion reliability with linear performance function of stationary processes and the time-independent reliability. The suggested method is useful for the analysis of components and systems with nonstationary responses in structural design where some uncertainties are represented by a vector of nonstationary processes. Examples are given to demonstrate the fast convergency and effectiveness of the presented method.展开更多
基金Project supported by the National Natural Science Foundation of China (No.10572117)the National Astronautics Science Foundation of China (Nos.N3CH0502 and N5CH0001)Program for New Century Excellent Talent of Ministry of Education of China (No.NCET-05-0868)
文摘Support vector machine （SVM） was introduced to analyze the reliability of the implicit performance function, which is difficult to implement by the classical methods such as the first order reliability method （FORM） and the Monte Carlo simulation （MCS）. As a classification method where the underlying structural risk minimization inference rule is employed, SVM possesses excellent learning capacity with a small amount of information and good capability of generalization over the complete data. Hence, two approaches, i.e., SVM-based FORM and SVM-based MCS, were presented for the structural reliability analysis of the implicit limit state function. Compared to the conventional response surface method （RSM） and the artificial neural network （ANN）, which are widely used to replace the implicit state function for alleviating the computation cost, the more important advantages of SVM are that it can approximate the implicit function with higher precision and better generalization under the small amount of information and avoid the ＂curse of dimensionality＂. The SVM-based reliability approaches can approximate the actual performance function over the complete sampling data with the decreased number of the implicit performance function analysis （usually finite element analysis）, and the computational precision can satisfy the engineering requirement, which are demonstrated by illustrations.
基金The project supported by the National Natural Science Foundation of China
文摘An efficient computational method is suggested for the first-excursion reliability assessment of nonstationary process. In the proposed method, the nonlinear performance function is Linearized at the Hasofer-Lind point obtained by an iterative algorithm. The problem of the nonstationary processes is solved by the discrete-time method, in which the precision can be controlled by choosing the steps of discretization. The derived formulae can be conveniently degraded to calculate both the first-excursion reliability with linear performance function of stationary processes and the time-independent reliability. The suggested method is useful for the analysis of components and systems with nonstationary responses in structural design where some uncertainties are represented by a vector of nonstationary processes. Examples are given to demonstrate the fast convergency and effectiveness of the presented method.
