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DYNAMIC RESPONSE VARIABILITY OF STRUCTURES WITH UNCERTAIN PROPERTIES
Author(s) -
KATAFYGIOTIS L. S.,
PAPADIMITRIOU C.
Publication year - 1996
Publication title -
earthquake engineering and structural dynamics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.218
H-Index - 127
eISSN - 1096-9845
pISSN - 0098-8847
DOI - 10.1002/(sici)1096-9845(199608)25:8<775::aid-eqe581>3.0.co;2-1
Subject(s) - probabilistic logic , superposition principle , mathematics , residual , series (stratigraphy) , modal , random variable , uncertainty quantification , perturbation (astronomy) , stochastic process , mathematical optimization , statistics , algorithm , mathematical analysis , paleontology , chemistry , physics , quantum mechanics , polymer chemistry , biology
A modal‐based analysis of the dynamic response variability of multiple degree‐of‐freedom linear structures with uncertain parameters subjected to either deterministic or stochastic excitations is considered. A probabilistic methodology is presented in which random variables with specified probability distributions are used to quantify the parameter uncertainties. The uncertainty in the response due to uncertainties in the structural modelling and loading is quantified by various probabilistic measures such as mean, variance and coefficient of excess. The computation of these probabilistic measures is addressed. A series expansion involving orthogonal polynomials in terms of the system parameters is first used to model the response variability of each contributing mode. Linear equations for the coefficients of each series expansion are derived using the weighted residual method. Mode superposition is then used to derive analytical expressions for the variability and statistics of the uncertain response in terms of the coefficients of the series expansions for all contributing modes. A primary–secondary system and a ten‐story building subjected to deterministic and stochastic loads are used to demonstrate the methodology, as well as evaluate its performance by comparing it to existing methods, including the computationally cost‐efficient perturbation method.