Localized and decentralized identification for large-scale structures

Abstract

Mathematical-model-based structural identification algorithms for the damage detection and performance evaluation of civil engineering structures have been widely proposed and their performance for small and simple structural models has been studied in the past two decades. Actual civil engineering structures, however, usually have a great number of degrees of freedom (DOFs) . It is unpractical to directly apply these conventional methods for the identification of large-scale structures, because excessive computation time and computer memory are necessary for the search of optimal solutions in inverse analysis, which is often computationally inefficient and even numerically unstable. Moreover, for the identification of largescale structures, it is difficult to obtain unique estimates of all structural parameters by the optimization search processes involved in the conventional identification algorithms requiring the use of secant, tangent, or higher-order derivatives of the objective function. The ability of artificial neural networks to approximate arbitrary continuous function provides an efficient soft computing strategy for structural parametric identification. Based on the concept of localized and decentralized information architecture, novel decentralized and localized identification strategies for large-scale structure system by the direct use of structural vibration response measurements with neural networks are proposed in this paper. These methodologies does not require the extraction of structural frequencies and mode shapes from the measurements and have the potential of being a practical tool for on-line near-real time and damage detection and performance evaluation of large-scale engineering structures.