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Methodology for estimating probability of dynamical system's failure for concrete gravity dam

Journal of Central South University 2014年第2期21卷 775-789页

作者：王超张社荣孙博王高辉 State Key Laboratory of Hydraulic Engineering Simulation and Safety (Tianjin University)

Methodology for the reliability analysis of hydraulic gravity dam is the key technology in current hydropower *** analysis for the dynamical dam safety should be divided into two phases:failure mode identification and the calculation of the failure *** of them are studied based on the mathematical statistics and structure reliability theory considering two kinds of uncertainty characters(earthquake variability and material randomness).Firstly,failure mode identification method is established based on the dynamical limit state system and verified through example of Koyna Dam so that the statistical law of progressive failure process in dam body are revealed; Secondly,for the calculation of the failure probability,mathematical model and formula are established according to the characteristics of gravity dam,which include three levels,that is element failure,path failure and system failure.A case study is presented to show the practical application of theoretical method and results of these methods.

关键词： concrete gravity dam dynamical system failure mode identification calculation of system failure probability stochastic model

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Research of Genetic Training Algorithm for Identifying Mechanical failure modes within the Framework of Case-Based Reasoning

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Chinese Journal of Aeronautics 2005年第2期18卷 122-129页

作者：徐元铭张洋陈丽娜 School of Aeronautical Science and Engineering Technology Beijing University of Aeronautics and Astronautics Beijing 100083 China

The combination of case-based reasoning （CBR） and genetic algorithm （GA） is considered in the problem of failure mode identification in aeronautical component failure analysis. Several imple- mentation issues such as matching attributes selection, similarity measure calculation, weights learning and training evaluation policies are carefully studied. The testing applications illustrate that an accuracy of 74.67 % can be achieved with 75 balanced-distributed failure cases covering 3 failure modes, and that the resulting learning weight vector can be well applied to the other 2 failure modes, achieving 73.3 % of recognition accuracy. It is also proved that its popularizing capability is good to the recognition of even more mixed failure modes.

关键词： failure mode identification case-based reasoning genetic algorithm learning train

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