Abstract
Structural control and health monitoring scheme play key roles not only in enhancing the safety and reliability of infrastructure systems when they are subjected to natural disasters, such as earthquakes, high winds and sea waves, but it also optimally minimize the life cycle cost and maximize the whole performance through the full life cycle design. In this scheme, system identification is regarded as a major technique to identifiy system states and related parameter variables, thus preventing degradation of structural or mechanical systems when unexpected disturbances occur. In this paper, three different strategies are proposed to identify general hysteretic behavior of a typical shear structure subjected to external excitations. Different case studies are presented to analyze the dynamic responses of a time varying shear structural system with the early version of Bouc-Wen-Baber-Noori (BWBN) hysteresis model. By incorporating a “Grey Box” strategy utilizing an Intelligent Parameter Varying (IPV) and Artificial Neural Network (ANN) approach, a Genetic algorithm (GA) and a Transitional Markov Chain Monte Carlo (TMCMC) based Bayesian Updating framework system identification schemes are developed to identify the hysteretic behavior of the structural system. Hysteresis characteristics, computational accuracy and algorithm efficiency are further discussed by evaluating the system identification results. Results show that IPV performs superior computational efficiency and system identification accuracy over GA and TMCMC approaches.
Highlights
In recent years, an increasing attention is witnessed to face the challenging issues of safety, serviceability, reliability, risk and life-cycle management, and performance improvement of structures and infrastructure due to changing and more frequently occurring natural and man-made hazards, infrastructure crisis, and sustainability issues
This BWBN model will be capable of producing all significant and prominent features of structural strength and stiffness degradations as well as slip lock behavior, and conduct a comparative study using three system identification approaches including an Intelligent Parameter Varying Artificial Neural Network developed in an earlier research work by a group that involved one of the authors (a “gray box” model that considers linear as well as non-linear parts of the dynamic system), genetic algorithm optimization method, and a novel Transitional Markov Chain Monte Carlo (TMCMC) statistical approach
It is a totally different strategy for TMCMC to acquire fj+1 (θ ) samples based on fj (θ ) samples, kernel density function (KDF) method is replaced by a resampling algorithm
Summary
An increasing attention is witnessed to face the challenging issues of safety, serviceability, reliability, risk and life-cycle management, and performance improvement of structures and infrastructure due to changing and more frequently occurring natural and man-made hazards, infrastructure crisis, and sustainability issues. Based on what was discussed in the introduction above, the main contributions of the research are to present a three story hysteretically degrading shear structure by incorporating BWBN slip lock hysteresis to represent the hysteretic restoring forces in this system This BWBN model will be capable of producing all significant and prominent features of structural strength and stiffness degradations as well as slip lock behavior, and conduct a comparative study using three system identification approaches including an Intelligent Parameter Varying Artificial Neural Network developed in an earlier research work by a group that involved one of the authors (a “gray box” model that considers linear as well as non-linear parts of the dynamic system), genetic algorithm optimization method, and a novel TMCMC statistical approach. To the best of the authors’ knowledge such comparative study has not been reported in the literature
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