A Principal Hankel Component Algorithm with Data Correlations (PHCA/DC) for the state-space model identification and the experimental modal analysis of structural systems

Abstract

This paper develops a new computational procedure for the time-domain state-space first-order model identification of dynamical systems and demonstrates its superior capabilities for the experimental modal analysis of structural systems. The applied system identification method devised in this work is referred to as the Principal Hankel Component Algorithm with Data Correlations (PHCA/DC). This study extensively evaluates the performance of the proposed computational method across various scenarios of interest in mechanical engineering. Firstly, the identification method analyzed in the paper is applied to a benchmark system comprising a two-degree-of-freedom mass–spring–damper mechanical system. Subsequently, a demonstrative example involving a finite element model of a truss system is used to demonstrate the effectiveness and applicability of the proposed method in more complex structural configurations. Finally, the methodology considered in this work is tested in a case study involving the experimental modal analysis of a three-story shear building system, providing insights into its applicability and performance in realistic scenarios. The numerical and experimental results found in this investigation corroborate the effectiveness and reliability of the proposed time-domain system identification methodology, thereby highlighting its potential for practical applications in structural dynamic analysis and modal parameters identification of mechanical engineering systems.