PDEM-BASED MODELING OF RANDOM VARIABLE AND STOCHASTIC PROCESS
To reasonably deal with the uncertainty involved in the measured data for structural identification and health evaluation is of paramount importance. One of the most important issues is how to obtain the probability density of a basic random variable or stochastic process based on the measured data. Being a fundamental issue in stochastic modelling, it is still not well resolved in spite of many contributions. In the present paper, the probability density evolution method, which is originally developed in stochastic response analysis of nonlinear systems, is extended in probability density estimation of random variable and stochastic process based on measured data. Two examples, one for probability density estimation of the basic wind speed and the other for time-variant probability density estimation of the wind speed time history, are illustrated.
Jie Li Jian-Bing Chen Lin-Lin Zhang
School of Civil Engineering, Tongji University, Shanghai 200092, P.R. China Shanghai Municipal Engineering Design Institute, Shanghai 200092, P.R. China
国际会议
长沙
英文
1463-1468
2007-11-19（万方平台首次上网日期，不代表论文的发表时间）