Open AccessBook
Random vibration and statistical linearization
J.B. Roberts,Pol D. Spanos +1 more
Reads0
Chats0
TLDR
In this paper, a comprehensive account of statistical linearization with related techniques allowing the solution of a very wide variety of practical non-linear random vibration problems is given, and the principal value of these methods is that they are readily generalized to deal with complex mechanical and structural systems and complex types of excitation such as earthquakes.Abstract:
Interest in the study of random vibration problems using the concepts of stochastic process theory has grown rapidly due to the need to design structures and machinery which can operate reliably when subjected to random loads, for example winds and earthquakes. This is the first comprehensive account of statistical linearization - powerful and versatile methods with related techniques allowing the solution of a very wide variety of practical non-linear random vibration problems. The principal value of these methods is that unlike other analytical methods, they are readily generalized to deal with complex mechanical and structural systems and complex types of excitation such as earthquakes.read more
Citations
More filters
Journal ArticleDOI
Stochastic averaging of duhem hysteretic systems
TL;DR: In this article, the response of a Duhem hysteretic system to externally and/or parametrically non-white random excitations is investigated by using the stochastic averaging method.
Journal ArticleDOI
Experimental and numerical study of the effects of heave plate on the motion of a new deep draft multi-spar platform
TL;DR: In this paper, a deep draft multi-spar (DDMS) platform for four different configurations (with/without upper and lower heave plates) was used to study the influence of the heave response on the surge, heave and pitch motions.
DissertationDOI
Stochastic System Design and Applications to Stochastically Robust Structural Control
TL;DR: General stochastic system design problems are discussed and a novel algorithm, called Stochastic Subset Optimization (SSO), is developed for efficiently exploring the sensitivity of the objective function to the design variables and iteratively identifying a subset of the original design space that has high plausibility of containing the optimal design variables.
Journal ArticleDOI
Stochastic linearisation approach to performance analysis of feedback systems with asymmetric nonlinear actuators and sensors
TL;DR: This paper derives transcendental equations for the quasilinear gain and bias, provides necessary and sufficient conditions for existence of their solutions, and investigates the accuracy of these solutions as a tool for predicting the quality of reference tracking and disturbance rejection.
Journal ArticleDOI
Fatigue Life Prediction of Nonlinear Plates Under Random Excitations
TL;DR: In this paper, an efficient method for estimating the high cycle fatigue life of nonlinear structures under random excitations is presented, based on an application of the method of equivalent linearization for constructing the response of the stress of the structure in time domain.